JP6945176B2 - Walking support robot and walking support method - Google Patents

Description

The present disclosure relates to a walking support robot and a walking support method that support the walking of a user.

In recent years, a guiding mobile robot that calculates a moving speed based on a user's input and guides the user to a destination by pulling the user's hand has been developed (see, for example, Patent Document 1).

Patent Document 1 discloses a guiding robot that calculates a target speed of a substrate according to an input to a gripping portion by a guided person and moves the substrate at the calculated target speed.

Japanese Unexamined Patent Publication No. 2010-271911

The robot of Patent Document 1 still has room for improvement from the viewpoint of providing more comfortable walking support for the user.

The present disclosure solves the above-mentioned problems, and provides a walking support robot and a walking support method capable of providing walking support for a more comfortable user.

The walking support robot according to one aspect of the present disclosure is
A walking support robot that supports the user's walking
With the main body
A handle portion provided on the main body portion and which can be grasped by the user, and a handle portion.
A detection unit that detects the load applied to the handle unit and
A moving device having a rotating body and controlling the rotation of the rotating body according to a load detected by the detection unit to move the walking support robot.
A load tendency data generation unit that generates load tendency data indicating the tendency of the load applied to the handle portion based on the past load data applied to the handle portion acquired during the movement of the walking support robot.
With
The moving device has an actuator that controls the amount of rotation of the rotating body based on the load detected by the detection unit and the load tendency data generated by the load tendency data generation unit.

The walking support method according to one aspect of the present disclosure is
It is a walking support method that supports the user's walking using a walking support robot.
A step of detecting the load applied to the handle portion of the walking support robot by the detection unit,
A step of generating load tendency data indicating the tendency of the load applied to the handle portion based on the past load data applied to the handle portion acquired while the walking support robot is moving.
A step of controlling the amount of rotation of a rotating body provided in the moving device of the walking support robot based on the load detected by the detection unit and the load tendency data.
including.

As described above, according to the walking support robot and the walking support method of the present disclosure, it is possible to provide more comfortable walking support for the user.

FIG. 1 is an external view of the walking support robot according to the first embodiment of the present disclosure. FIG. 2 is a diagram showing a state in which a user is walking while receiving walking support by the walking support robot according to the first embodiment of the present disclosure. FIG. 3 is a diagram showing a detection direction of the handle load detected by the detection unit according to the first embodiment of the present disclosure. FIG. 4 is a control block diagram showing a main control configuration in the walking support robot according to the first embodiment of the present disclosure. FIG. 5 is a control block diagram showing a walking support control configuration of the walking support robot according to the first embodiment of the present disclosure. FIG. 6 is a diagram showing a load trend map according to the first embodiment of the present disclosure. FIG. 7 is an exemplary flowchart of the load tendency data generation process of the walking support robot according to the first embodiment of the present disclosure. FIG. 8 is an example of input waveform information of the handle load. FIG. 9A is a diagram showing an example of waveform information of load data in the Fz direction during a straight-ahead operation of the user. FIG. 9B is a diagram showing frequency components of the load data in the Fz direction shown in FIG. 9A. FIG. 10A is a diagram showing an example of waveform information of load data in the My direction during a straight-ahead operation of the user. FIG. 10B is a diagram showing frequency components of the load data in the My direction shown in FIG. 10A. FIG. 11A is a diagram showing an example of waveform information of load data in the Fz direction when the user turns to the right. FIG. 11B is a diagram showing frequency components of load data in the Fz direction shown in FIG. 11A. FIG. 12 is an exemplary flowchart of the movement intention estimation process of the walking support robot according to the first embodiment of the present disclosure. FIG. 13A is a diagram showing an example of waveform information of load data in the Fz direction during straight-ahead operation of the user. FIG. 13B is a diagram showing waveform information obtained by filtering fluctuation frequency components from waveform information of load data in the Fz direction shown in FIG. 13A. FIG. 14A is a diagram showing an example of waveform information of load data in the My direction during straight-ahead operation of the user. FIG. 14B is a diagram showing waveform information obtained by filtering fluctuation frequency components from waveform information of load data in the My direction shown in FIG. 14A. FIG. 15 is an exemplary flowchart of the driving force calculation process of the walking support robot according to the first embodiment of the present disclosure. FIG. 16 is a diagram showing a load trend map according to the second embodiment of the present disclosure. FIG. 17 is an exemplary flowchart of the load tendency data generation process of the walking support robot according to the second embodiment of the present disclosure. FIG. 18 is an exemplary flowchart of the movement intention estimation process of the walking support robot according to the second embodiment of the present disclosure. FIG. 19A is a diagram showing an example of waveform information of the current load data in the Mz direction during the straight-ahead operation of the user. FIG. 19B is a diagram showing an average load value of past load data in the Mz direction. FIG. 19C is a diagram showing an example of waveform information of the corrected load data in the second embodiment of the present disclosure. FIG. 20A is a diagram showing an example of waveform information of past load data in the Mz direction during straight-ahead operation of the user. FIG. 20B is a diagram showing an average load value of past load data in the Mz direction shown in FIG. 20A. FIG. 21A is a diagram showing an example of waveform information of the current load data in the Mz direction during the straight-ahead operation of the user. FIG. 21B is a diagram showing an average load value of the current load data in the Mz direction shown in FIG. 21A. FIG. 22 is a diagram showing an example of waveform information of the corrected load data in the second embodiment of the present disclosure. FIG. 23 is a control block diagram showing a main control configuration in the walking support robot according to the third embodiment of the present disclosure. FIG. 24 is a control block diagram showing a walking support control configuration of the walking support robot according to the third embodiment of the present disclosure. FIG. 25 is an exemplary flowchart of the body information estimation process of the walking support robot according to the third embodiment of the present disclosure. FIG. 26A is an example of physical information stored in the physical information database of the walking support robot according to the third embodiment of the present disclosure. FIG. 26B is another example of the physical information stored in the physical information database of the walking support robot according to the third embodiment of the present disclosure. FIG. 27 is an exemplary flowchart of the user movement intention estimation process of the walking support robot according to the third embodiment of the present disclosure. FIG. 28 is another control block diagram showing a walking support control configuration of the walking support robot according to the third embodiment of the present disclosure.

(Background to this disclosure)
In recent years, with the declining birthrate and aging population in developed countries, the need for watching over the elderly and supporting their livelihoods is increasing. In particular, elderly people tend to have difficulty in maintaining the QOL (Quality of Life) of their home life due to the deterioration of their physical abilities with aging. In order to prevent sarcopenia in the elderly and maintain physical fitness, it is important to maintain muscle mass by continuing exercise above a certain level. However, it becomes difficult to go out due to a decrease in physical ability, and in the case of elderly people who tend to stay in the house, it is difficult to maintain a constant amount of exercise, and a vicious cycle of further attenuation of muscle mass occurs. In recent years, against such a background, various devices that support the walking of the user have been proposed.

As described in the background art, Patent Document 1 discloses a guiding mobile robot that moves at a moving speed according to a user's input. However, in the guide mobile robot of Patent Document 1, when a user having low physical ability such as an elderly person uses the robot, it is difficult to provide walking support for a comfortable user.

For example, an elderly person with low physical strength may lean forward with respect to a robot. In this case, in the robot of Patent Document 1, although the walking speed of the elderly person is slow, the input value in the traveling direction is large, so that the moving speed in the traveling direction is large. As a result, the elderly cannot keep up with the movement of the mobile robot. In addition, elderly people may have their body's center of gravity unstable and sway from side to side even though they are moving straight. In this case, the robot of Patent Document 1 may detect the input in the left-right direction and move in the left-right direction. As described above, in the robot of Patent Document 1, in the case of a user having low physical ability, the robot may move in a direction contrary to the user's movement intention even though the user wants to perform a straight-ahead motion. .. Therefore, in the robot of Patent Document 1, the user needs to walk while finely adjusting the traveling direction, and it is difficult to provide comfortable walking support for the user.

The present inventors can obtain the intention of the user's walking motion by accumulating information on the moving motion of the walking support robot (for example, moving direction, moving speed, etc.) even if the user is swaying from side to side. I found that I could grasp it. Therefore, the present inventors have reached the following inventions because the robot is moved according to the user's movement intention based on the accumulated information on the movement motion even when the user is swaying from side to side and walking. rice field.

The walking support robot according to one aspect of the present disclosure is
A walking support robot that supports the user's walking
With the main body
A handle portion provided on the main body portion and which can be grasped by the user, and a handle portion.
A detection unit that detects the load applied to the handle unit and
A moving device having a rotating body and controlling the rotation of the rotating body according to a load detected by the detection unit to move the walking support robot.
A load tendency data generation unit that generates load tendency data indicating the tendency of the load applied to the handle portion based on the past load data applied to the handle portion acquired during the movement of the walking support robot.
With
The moving device has an actuator that controls the amount of rotation of the rotating body based on the load detected by the detection unit and the load tendency data generated by the load tendency data generation unit.

With such a configuration, walking support can be provided according to the physical ability of the user, so that it is possible to provide more comfortable walking support for the user.

The moving device further has a load correction unit that corrects the value of the load detected by the detection unit based on the load tendency data.
The actuator may control the amount of rotation of the rotating body based on the value of the load corrected by the load compensating unit.

With such a configuration, the load value can be corrected based on the load tendency data, so that it is possible to provide more comfortable walking support for the user.

The load tendency data generation unit generates load tendency data for each movement motion of the walking support robot, and generates load tendency data.
The load correction unit may correct the value of the load based on the load tendency data corresponding to the movement operation of the walking support robot when the load is detected.

With such a configuration, the load tendency data can be generated for each movement operation, so that the load tendency of the user can be grasped more accurately. As a result, it is possible to provide more comfortable walking support for the user.

When the load tendency data corresponding to the movement motion of the walking support robot becomes equal to or higher than a predetermined threshold value, the load correction unit may correct the load value based on the load tendency data.

With such a configuration, when the load tendency data corresponding to the movement motion of the walking support robot exceeds a predetermined threshold value, the load value can be corrected, so that more comfortable walking support for the user can be performed. Can be done.

The load tendency data is a fluctuation frequency calculated from the past load data.
The load correction unit may correct the value of the load by filtering the frequency component of the fluctuation from the load detected by the detection unit.

With such a configuration, by using the fluctuation frequency as the load tendency data, it is possible to acquire the load tendency data of the user in a wide range from the fluctuation with small unevenness to the fluctuation with large unevenness and correct the load value. Therefore, it is possible to provide more comfortable walking support for the user.

The load tendency data is an average load value calculated from the past load data.
The load correction unit may correct the value of the load based on the average load value.

With such a configuration, by using the average load value as the load tendency data, it is possible to acquire the load constantly applied for each user as the load tendency data and correct the load value, so that the user can feel more comfortable. Can provide walking support.

The load correction unit may correct the value of the load by subtracting the average load value from the load detected by the detection unit.

With such a configuration, by subtracting the average load value from the load detected by the detection unit, the load constantly applied to each user can be reduced, so that more comfortable walking support for the user can be performed. ..

In the walking support robot, further
It is provided with a physical information estimation unit that estimates the physical information of the user.
The load correction unit may correct the value of the load based on the physical information estimated by the physical information estimation unit.

With such a configuration, the strength of the correction can be adjusted based on the physical information, so that walking support can be performed according to the physical ability of the user.

In the walking support robot, further
A user notification unit that notifies the user of the physical information may be provided.

With such a configuration, the user himself / herself can grasp daily physical information, which leads to motivation to maintain and improve physical ability or to arouse attention during walking.

In the walking support robot, further
A user movement intention estimation unit that estimates the movement intention of the user based on the corrected load value is provided.
The user notification unit may notify the user of the user's intention to move.

With such a configuration, the user can grasp the control state of the walking support robot.

A storage unit for storing a control table showing the correspondence between the load applied to the handle unit and the rotation amount of the rotating body is provided.
The actuator drives and controls the rotating body with a rotation amount corresponding to the load detected by the detection unit by the control table stored in the storage unit.
The control table may be updated by modifying the value of the load based on the load tendency data.

With such a configuration, the correspondence between the load applied to the handle portion and the amount of rotation of the rotating body can be easily specified by the control table, so that walking support according to the physical ability of the user can be performed more easily. can.

The detection unit detects a plurality of axial loads applied to the handle unit, and receives the load.
The moving device may control the rotation of the rotating body to switch the moving operation of the walking support robot according to the magnitude of the load applied to each of the plurality of axial directions.

With such a configuration, it is possible to more accurately detect the user's movement intention by detecting the load applied in a plurality of axial directions. Therefore, the movement operation of the walking support robot can be switched according to the movement intention of the user.

The moving motion may include a straight-ahead motion, a backward motion, and a turning motion of the walking support robot.

With such a configuration, the movement operation of the walking support robot can be switched according to the movement intention of the user, so that more comfortable walking support of the user can be performed.

The actuator may change the turning radius in the turning operation based on the load tendency data.

With such a configuration, since the walking support robot performs a turning motion according to the physical ability of the user, it is possible to provide more comfortable walking support for the user.

The walking support method according to one aspect of the present disclosure is
It is a walking support method that supports the user's walking using a walking support robot.
A step of detecting the load applied to the handle portion of the walking support robot by the detection unit,
A step of generating load tendency data indicating the tendency of the load applied to the handle portion based on the past load data applied to the handle portion acquired while the walking support robot is moving.
A step of controlling the amount of rotation of a rotating body provided in the moving device of the walking support robot based on the load detected by the detection unit and the load tendency data.
including.

With such a configuration, walking support can be provided according to the physical ability of the user, so that it is possible to provide more comfortable walking support for the user.

In the walking support method, further
Including a step of correcting the value of the load detected by the detection unit based on the load tendency data.
The step of controlling the rotation amount of the rotating body may control the rotation amount of the rotating body based on the corrected load value.

With such a configuration, the load value can be corrected based on the load tendency data, so that it is possible to provide more comfortable walking support for the user.

In the walking support method, further
Including the step of estimating the physical information of the user.
The step of correcting the load value may correct the load value based on the physical information.

With such a configuration, the strength of the correction can be adjusted based on the physical information, so that walking support can be performed according to the physical ability of the user.

In the walking support method, further
It may include a step of notifying the user of the physical information.

With such a configuration, the user himself / herself can grasp daily physical information, which leads to motivation to maintain and improve physical ability or to arouse attention during walking.

In the walking support method, further
Including the step of estimating the user's movement intention based on the corrected load value.
The notification step may notify the user of the user's intention to move.

With such a configuration, the user can grasp the state of walking support.

Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. Further, in each figure, each element is exaggerated for easy explanation.

(Embodiment 1)
[overall structure]
FIG. 1 shows an external view of the walking support robot 1 (hereinafter, referred to as “robot 1”) according to the first embodiment. FIG. 2 shows a state in which a user is walking with walking support by the robot 1.

As shown in FIGS. 1 and 2, the robot 1 moves the main body portion 11, the handle portion 12 that can be gripped by the user, the detection unit 13 that detects the handle load applied to the handle portion 12, and the main body portion 11. A moving device 14 and a load tendency data generation unit 15 are provided.

The handle portion 12 is provided on the upper portion of the main body portion 11 and is provided in a shape and a height position that can be easily grasped by both hands of a walking user.

The detection unit 13 detects the load (handle load) applied by the user to the handle unit 12 by the user gripping the handle unit 12. Specifically, when the user grips the handle portion 12 and walks, the user applies a handle load to the handle portion 12. The detection unit 13 detects the direction and magnitude of the handle load applied by the user to the handle unit 12.

FIG. 3 shows the detection direction of the handle load detected by the detection unit 13. As shown in FIG. 3, the detection unit 13 is a six-axis force sensor capable of detecting a force applied in three axial directions orthogonal to each other and a moment around the three axes. The three axes orthogonal to each other are the x-axis extending in the left-right direction of the robot 1, the y-axis extending in the front-rear direction of the robot 1, and the z-axis extending in the height direction of the robot 1. The force applied in the triaxial direction is a force Fx applied in the x-axis direction, a force Fy applied in the y-axis direction, and a force Fz applied in the z-axis direction. In the first embodiment, the force applied to the right side of Fx is defined as Fx ^+, and the force applied to the left direction is defined as Fx ⁻ . Of the Fy, the force applied in the forward direction is defined as Fy ^+, and the force applied in the backward direction is defined as Fy ⁻ . Of the Fz directions, the force applied vertically downward with respect ^{to the walking surface is defined as Fz +,} and the force applied vertically upward with respect to the walking surface is defined as Fz ⁻ . The three-axis axial moments are the x-axis axial moment Mx, the y-axis axial moment My, and the z-axis axial moment Mz.

The moving device 14 moves the main body 11 based on the magnitude and direction of the handle load (force and moment) detected by the detecting unit 13. In the first embodiment, the mobile device 14 controls as follows. In addition, in this specification, Fx, Fy, Fz, Mx, My, Mz may be referred to as a load.

<Forward movement>
When the detection unit 13 detects a Fy ⁺ force, the moving device 14 moves the main body unit 11 in the forward direction. That is, when ^{the force of Fy +} is detected by the detection unit 13, the robot 1 moves forward. ^{When the force of Fy +} detected by the detection unit 13 increases while the robot 1 is moving forward, the moving device 14 increases the speed of movement of the robot 1 in the forward direction. ^{On the other hand, if the force of Fy +} detected by the detection unit 13 becomes small while the robot 1 is moving forward, the moving device 14 reduces the moving speed of the robot 1 in the forward direction.

<Reverse movement>
^{When the force of Fy −} is detected by the detection unit 13, the moving device 14 moves the main body portion 11 in the rear direction. That is, when ^{the force of Fy −} is detected by the detection unit 13, the robot 1 performs a backward operation. ^{When the force of Fy −} detected by the detection unit 13 increases while the robot 1 is performing the backward movement, the moving device 14 increases the speed of movement of the robot 1 in the backward direction. ^{On the other hand, if the force of Fy −} detected by the detection unit 13 becomes small while the robot 1 is performing the backward operation, the moving device 14 reduces the moving speed of the robot 1 in the backward direction.

<Right turn operation>
When the detection unit 13 detects the force ^{of Fy + and the moment of Mz +} , the moving device 14 swivels the main body 11 to the right. That is, when the detection unit 13 detects the force ^{of Fy + and the moment of Mz +} , the robot 1 makes a right turn operation. ^{If the moment of Mz +} detected by the detection unit 13 increases while the robot 1 is performing the right turn operation, the turning radius of the robot 1 decreases. ^{Further, if the force of Fy +} detected by the detection unit 13 increases while the robot 1 is performing the right turning operation, the turning speed increases.

<Left turn operation>
When the detection unit 13 detects the Fy ⁺ force and the Mz ⁻ moment, the moving device 14 swivels the main body 11 to the left. That is, when the detection unit 13 detects the force ^{of Fy + and the moment of Mz −} , the robot 1 makes a left turn operation. ^{If the moment of Mz −} detected by the detection unit 13 increases while the robot 1 is performing the left turn operation, the turning radius of the robot 1 decreases. ^{Further, if the force of Fy +} detected by the detection unit 13 increases while the robot 1 is performing the left turning operation, the turning speed increases.

The control of the mobile device 14 is not limited to the above-mentioned example. The moving device 14 may control the forward movement and the backward movement of the robot 1 based on, for example, the forces of Fy and Fz. Further, the moving device 14 may control the turning motion of the robot 1 based on, for example, a moment of Mx or My.

In the first embodiment, an example in which the detection unit 13 is a six-axis force sensor has been described, but the present invention is not limited to this. The detection unit 13 may use, for example, a triaxial sensor, a strain sensor, or the like.

The moving device 14 includes wheels 16 which are rotating bodies provided below the main body 11, and drive units 17 which drive and control the wheels 16.

The wheels 16 support the main body 11 in a self-supporting state and are rotationally driven by the drive unit 17, so that the main body 11 is maintained in a self-supporting posture, for example, in the direction of the arrow shown in FIG. Move (forward or backward). In the first embodiment, the case where the moving device 14 is provided with a moving mechanism using two wheels 16 is taken as an example, but there is a case where a rotating body (running belt, roller, etc.) other than the wheels is used. You may.

The drive unit 17 includes a load correction unit 18, a user movement intention estimation unit 19, a driving force calculation unit 20, an actuator control unit 21, and an actuator 22.

The load correction unit 18 corrects the handle load detected by the detection unit 13 based on the load tendency of the user. Specifically, the load correction unit 18 corrects the value of the handle load detected by the detection unit 13 based on the load tendency data generated by the load tendency data generation unit 15. In the first embodiment, the fluctuation frequency is calculated from the past handle load data during walking of the user, and the fluctuation frequency is filtered from the handle load detected by the detection unit 13 to correct the handle load. Further, the load correction unit 18 may correct the value of the handle load based on the place of use of the robot 1, the time of use, the physical condition of the user, and the like.

The user movement intention estimation unit 19 estimates the user's movement intention based on the handle load corrected by the load correction unit 18 (hereinafter, referred to as “corrected handle load”). The user's movement intention includes the movement direction and the movement speed. In the first embodiment, the user movement intention estimation unit 19 estimates the user's movement intention from the value of the correction handle load in each movement direction. ^{For example, when the Fy +} force detected by the detection unit 13 is a value equal to or more than a predetermined first threshold value and the My ⁺ force is a value less than a predetermined second threshold value, the user movement intention estimation unit 19 may perform the user movement intention estimation unit 19. It may be presumed that the user's intention to move is a straight-ahead operation. Further, the user movement intention estimation unit 19 may estimate the movement speed based on the value of the correction handle load in the Fz direction. ^{On the other hand, when the force of Fy +} detected by the detection unit 13 is a value equal to or higher than a predetermined third threshold value and ^{the force of My +} is a value equal to or higher than a predetermined second threshold value, the user movement intention estimation unit 19 determines. It may be presumed that the user's intention to move is a right turn operation. Further, the user movement intention estimation unit 19 may estimate the turning speed based on the value of the correction handle load in the Fz direction, and may estimate the turning radius based on the value of the correction handle load in the My direction.

The driving force calculation unit 20 calculates the driving force based on the information of the handle load corrected by the load correction unit 18. Specifically, the driving force calculation unit 20 calculates the driving force based on the user's movement intention estimated from the information of the correction handle load, that is, the user's movement direction and movement speed. For example, when the user's intention to move is forward movement or backward movement, the driving force is calculated so that the rotation amounts of the two wheels 16 are equal. When the user intends to move to the right, the driving force is calculated so that the rotation amount of the right wheel 16 of the two wheels 16 is larger than the rotation amount of the left wheel 16. In addition, the magnitude of the driving force is calculated according to the moving speed of the user.

The actuator control unit 21 performs drive control of the actuator 22 based on the drive force information calculated by the drive force calculation unit 20. Further, the actuator control unit 21 can acquire information on the rotation amount of the wheel 16 from the actuator 22, and can transmit the information on the rotation amount of the wheel 16 to the driving force calculation unit 20 and the user load tendency extraction unit 23.

The actuator 22 is, for example, a motor that rotationally drives the wheels 16. The actuator 22 is connected to the wheel 16 via a gear mechanism, a pulley mechanism, or the like. The actuator 22 is driven to rotate the wheels 16 by being driven and controlled by the actuator control unit 21.

The load tendency data generation unit 15 generates load tendency data of the user based on the information of the handle load detected in the past. The load tendency data is data showing the tendency of the handle load of the user in a predetermined operation. The predetermined operation means, for example, a straight-ahead operation, a backward operation, a turning operation, and the like. For example, a user with a bent waist leans against the robot 1 when grasping the handle portion 12, so that the handle load in the vertical downward direction with respect to the walking surface of the road on which the robot 1 moves, that is, Fz ⁺ The force tends to increase. Further, when the user who walks while swinging left and right tends to increase the handle load in the left-right direction, that is, the moment of My, when the handle portion 12 is gripped, the handle load in the left-right direction tends to be large even though the user is performing the forward movement. In this way, the load tendency data generation unit 15 generates the load tendency of the user for each predetermined operation from the past load data.

[Control configuration of walking support robot]
In the walking support robot 1 having such a configuration, a control configuration for supporting the walking of the user will be described. FIG. 4 is a control block diagram showing a main control configuration in the robot 1. Further, the control block diagram of FIG. 4 also shows the relationship between each control configuration and the information to be handled.

As shown in FIG. 4, the detection unit 13 detects the handle load applied to the handle unit 12. The information on the handle load detected by the detection unit 13 is transmitted to the load correction unit 18. The load correction unit 18 corrects the value of the handle load detected by the detection unit 13 based on the load tendency data generated by the load tendency data generation unit 15. Information on the corrected handle load (corrected handle load) is transmitted to the user movement intention estimation unit 19. The user movement intention estimation unit 19 estimates the user's movement intention (movement direction and movement speed) based on the information of the correction handle load. The estimated user's movement intention information is transmitted to the driving force calculation unit 20. The driving force calculation unit 20 calculates the driving force based on the estimated information of the user's movement intention. The calculated driving force information is transmitted to the actuator control unit 21. The actuator control unit 21 performs drive control of the actuator 22 based on the drive force information calculated by the drive force calculation unit 20. The actuator 22 is driven and controlled by the actuator control unit 21 to rotationally drive the wheels 16 and move the main body 11.

Further, as shown in FIG. 4, the information on the handle load detected by the detection unit 13 is also transmitted to the load tendency data generation unit 15. The handle load information detected by the detection unit 13 is also used to generate and update the load tendency data.

Detailed control of the walking support of the robot 1 will be described with reference to FIG. FIG. 5 is a control block diagram showing a detailed control configuration of walking support of the robot 1.

As shown in FIG. 5, the load tendency data generation unit 15 includes a user load tendency extraction unit 23 that extracts the user's load tendency corresponding to the user's movement direction, and a load tendency map 24 that stores the user's load tendency data. , Equipped with.

The user load tendency extraction unit 23 extracts the load tendency of the user corresponding to the movement direction of the user. Specifically, the user load tendency extraction unit 23 extracts the user's load tendency data corresponding to the user's movement direction from the load tendency map 24. For example, when the user is performing a straight-ahead operation (forward forward), the user load tendency extraction unit 23 extracts the load tendency of the user corresponding to the straight-ahead operation from the load tendency map 24. The user load tendency extraction unit 23 transmits the load tendency data extracted from the load tendency map 24 to the load correction unit 18.

Further, the user load tendency extraction unit 23 generates user load tendency data based on the handle load information detected by the detection unit 13 and the wheel 16 rotation amount information acquired by the actuator control unit 21. .. The generated load tendency data is transmitted to the load tendency map 24. As a result, the load tendency data of the load tendency map 24 is updated.

The load tendency map 24 is a database that stores the load tendency data of the user in each movement direction of the user. The load tendency map 24 stores the load tendency data of the user for each movement direction. FIG. 6 shows a load trend map 24. As shown in FIG. 6, in the first embodiment, the load tendency map 24 stores the fluctuation frequency in the moving direction during walking and the fluctuation frequency in the bias direction of the center of gravity during walking as the load tendency data of the user. .. Further, the load tendency map 24 may store the fluctuation frequency data calculated in the past.

Although not shown in FIG. 6, the load tendency map 24 may store data such as the usage location, usage time, and physical condition of the user of the robot 1. These data may be used when the load correction unit 18 corrects the handle load.

[Generation of load tendency data]
The generation of load tendency data will be described with reference to FIG. 7. FIG. 7 shows an exemplary flowchart of the load tendency data generation process.

As shown in FIG. 7, in step ST1, it is determined whether or not the handle load is detected by the detection unit 13. In step ST1, it is determined whether or not the user is holding the handle portion 12. When the detection unit 13 detects the handle load, the process proceeds to step ST2. If the detection unit 13 does not detect the handle load, step ST1 is repeated.

In step ST2, the user load tendency extraction unit 23 estimates the user's moving direction based on the information on the amount of rotation of the wheels 16. Specifically, when a change in the handle load is detected in step ST1, the actuator control unit 21 acquires information on the amount of rotation of the wheels 16. The rotation amount information acquired by the actuator control unit 21 is transmitted to the user load tendency extraction unit 23. The user load tendency extraction unit 23 estimates the user's moving direction based on the information on the amount of rotation of the wheel 16, that is, the rotation direction and the number of rotations of the wheel. In the first embodiment, the user load tendency extraction unit 23 estimates the moving direction of the user based on the amount of rotation of the two wheels 16 arranged on the left and right. For example, the user load tendency extraction unit 23 may presume that the user is turning to the left when the amount of rotation of the right wheel 16 is larger than the amount of rotation of the left wheel 16. Further, the user load tendency extraction unit 23 may presume that the robot 1 is performing a straight-ahead operation when the left and right wheels 16 have the same rotation speed and are rotating in the forward direction.

In step ST3, the user load tendency extraction unit 23 acquires the waveform information of the handle load in the estimated movement direction of the user. The waveform information of the handle load in the movement direction of the user is not particularly limited, but for example, when the movement direction of the user is the Fy ⁺ direction, the waveform information of the handle load in the Fz direction or the waveform information of the moment in the My direction may be used. There may be.

In step ST4, the user load tendency extraction unit 23 adds up the acquired waveform information of the handle load and the waveform information of the past handle load. For example, past waveform information is stored in the load trend map 24. The user load tendency extraction unit 23 reads the past waveform information from the load tendency map 24, and adds the acquired current waveform information to the past waveform information. FIG. 8 shows an example of the input waveform information of the handle load. As shown in FIG. 8, the waveform information of the handle load detected so far is stored in the load tendency map 24.

In step ST5, the user load tendency extraction unit 23 calculates the fluctuation frequency based on the total waveform information. Specifically, the user load tendency extraction unit 23 calculates the fluctuation frequency by performing frequency analysis of the handle load in the estimated movement direction of the user.

As an example, the calculation of the fluctuation frequency during straight-ahead operation of a user with low physical ability will be described. FIG. 9A shows an example of waveform information of load data in the Fz direction when the user goes straight. FIG. 9B shows the frequency components of the load data in the Fz direction shown in FIG. 9A. FIG. 10A shows an example of waveform information of load data in the My direction when the user goes straight. FIG. 10B shows the frequency component of the load data in the My direction shown in FIG. 10A. Although FIG. 9A shows the waveform of the load data for three steps, the frequency analysis is actually performed for the waveform of the load data for more than ten steps.

Since a user with low physical ability walks while swinging from side to side, the handle load is not stable even if the user is moving straight at a constant speed. Therefore, as shown in FIG. 9A, the waveform information of the load data in the height direction of the robot 1, that is, the Fz direction fluctuates. The fluctuation means a component in which the waveform information fluctuates and is not stable, and specifically, means a fluctuation from the average value of the load data.

In this case, although the user intends to go straight in the forward direction, the robot 1 moves in the left-right direction, so that the user walks while finely adjusting the traveling direction to the left and right. In the first embodiment, the user load tendency extraction unit 23 presumes that the user walks while swaying from side to side, and uses the load fluctuation component as the load tendency data in order to correct the handle load. Hereinafter, an example of processing by the user load tendency extraction unit 23 will be described.

The user load tendency extraction unit 23 performs frequency analysis on the waveform information of the load data in the Fz direction shown in FIG. 9A, and calculates the frequency component of the load data as shown in FIG. 9B. As a result, the user load tendency extraction unit 23 can specify that there is a fluctuation frequency of 2 Hz in the Fz direction as shown in FIG. 9B when the user goes straight.

Further, as shown in FIG. 10A, the waveform information of the load data in the My direction of the user with low physical ability also fluctuates. The user load tendency extraction unit 23 performs frequency analysis on the load data in the My direction shown in FIG. 10A, and calculates the frequency component of the load data as shown in FIG. 10B. As a result, the user load tendency extraction unit 23 can specify that there is a fluctuation frequency of 2 Hz in the My direction as shown in FIG. 10B when the user goes straight.

As another example, the calculation of the fluctuation frequency when the user with low physical ability turns to the right will be described. FIG. 11A shows an example of waveform information of load data in the Fz direction when the user turns to the right. FIG. 11B shows the frequency components of the load data in the Fz direction shown in FIG. 11A.

As shown in FIG. 11A, even when a user with low physical ability is turning to the right, the waveform information of the load data in the Fz direction fluctuates. The user load tendency extraction unit 23 performs frequency analysis on the load data in the Fz direction shown in FIG. 11A, and calculates the frequency component of the load data as shown in FIG. 11B. The user load tendency extraction unit 23 can specify that there is a fluctuation frequency of 6 Hz in the Fz direction as shown in FIG. 11B when the user turns to the right.

As described above, in step ST5, the user load tendency extraction unit 23 calculates the fluctuation frequency from the waveform information of the total handle load in the estimated movement direction of the user.

Returning to FIG. 7, in step ST6, the user load tendency extraction unit 23 sets the fluctuation frequency calculated in step ST5 as the load tendency data. Specifically, the user load tendency extraction unit 23 updates the load tendency data of the user of the load tendency map 24 to the fluctuation frequency calculated in step ST5.

As described above, in the first embodiment, by performing the processes of steps ST1 to ST6, the fluctuation frequency of the handle load of the user can be calculated, and the fluctuation frequency can be used as the load tendency data. Further, in the first embodiment, the load tendency data can be generated for each movement operation.

In steps ST4 and ST5, an example of setting the fluctuation frequency calculated based on the waveform information obtained by adding the waveform information of the acquired handle load and the waveform information of the past handle load as the load tendency data has been described. , Not limited to this. After adding the fluctuation frequency calculated based on the acquired handle load waveform information to the fluctuation frequency calculated based on the past waveform information, the average calculation is performed, and the average value of the calculated fluctuation frequency is the load tendency. It may be used as data. In addition, the median or mode of the fluctuation frequency is calculated based on the fluctuation frequency calculated from the acquired waveform information of the handle load and the past fluctuation frequency, and the median or mode of the fluctuation frequency is set as the load tendency. It may be used as data. Further, the average value, the median value, and the mode value of the fluctuation frequency may be combined and used as the load tendency data. Alternatively, the most recently calculated fluctuation frequency may be used as the load tendency data. The above-mentioned load tendency data may be used properly according to the situation and the purpose. For example, for a user who has little data on the past fluctuation frequency stored in the load tendency map 24, the most recently calculated fluctuation frequency may be used as the load tendency data. On the contrary, for a user who has a large amount of past fluctuation frequency data stored in the load tendency map 24, the average value, the median value, or the mode value of the fluctuation frequency may be used as the load tendency data.

[Estimation of user's movement intention]
The estimation of the user's movement intention will be described with reference to FIG. FIG. 12 shows an exemplary flowchart of the user's movement intention estimation process.

As shown in FIG. 12, in step ST11, the load correction unit 18 acquires information on the handle load detected by the detection unit 13.

In step ST12, the user load tendency extraction unit 23 acquires the load tendency data from the load tendency map 24. Specifically, the user load tendency extraction unit 23 acquires the fluctuation frequency that is the target of the user's current movement direction from the load tendency map 24. The user load tendency extraction unit 23 transmits the fluctuation frequency information to the load correction unit 18 as load tendency data. The current moving direction of the user can be estimated by acquiring information on the amount of rotation of the wheels 16 from the actuator control unit 21.

In step ST13, the load correction unit 18 filters the fluctuation frequency component acquired in step ST12 from the handle load acquired in step ST11. As a result, the load correction unit 18 corrects the value of the handle load detected by the detection unit 13. The information on the correction handle load obtained by the load correction unit 18 is transmitted to the user movement intention estimation unit 19.

Further, the load correction unit 18 may correct the value of the handle load based on the place of use of the robot 1, the time of use, and the physical condition of the user. In this case, the user load tendency extraction unit 23 extracts data on the usage location, usage time, and user's physical condition of the robot 1 from the load tendency map 24, and transmits these data to the load correction unit 18. For example, the load compensating unit 18 makes the handle load when the robot 1 is used in the living room or when the user is in poor physical condition smaller than the handle load when the robot 1 is used in the corridor or when the user is in good physical condition. The handle load may be corrected so as to be.

In step ST14, the user movement intention estimation unit 19 estimates the user's movement intention based on the correction handle load acquired in step ST13. Specifically, the user movement intention estimation unit 19 estimates the movement direction and movement speed of the user based on the magnitude of the force in the Fx, Fy, Fz, Mx, My, and Mz directions of the correction handle load.

As described above, in the first embodiment, by performing the processes of steps ST11 to ST14, the fluctuation frequency component is filtered from the waveform information of the handle load of the user, and the user moves based on the obtained correction handle load information. I'm estimating the intention.

In the first embodiment, as filtering, correction may be performed so as to cut off all the frequency components corresponding to the fluctuation portion so as to remove all the fluctuation components, or the load data during walking may be corrected for the fluctuation components. Corrections may be made to reduce the proportion.

Further, the load correction unit 18 does not correct only the load tendency data of the user, but compares the load tendency data of the user with the average load tendency data of a plurality of users and corrects so as to reduce the difference portion. The ratio of may be changed. As a method of calculating the average of a plurality of users, it may be created for each group classified by a combination of age, gender, place, walking ability (walking speed, walking rate, stride length, standing posture, left-right shaking), etc. ..

As an example, the process of estimating the movement intention of a user with low physical ability will be described. FIG. 13A shows an example of waveform information of load data in the Fz direction when the user goes straight. FIG. 13B shows waveform information obtained by filtering the fluctuation frequency component from the waveform information of the load data in the Fz direction shown in FIG. 13A. FIG. 14A shows an example of waveform information of load data in the My direction when the user goes straight. FIG. 14B shows waveform information obtained by filtering the fluctuation frequency component from the waveform information of the load data in the My direction shown in FIG. 14A. The waveform information shown in FIGS. 13A and 14A is the waveform information of the handle load acquired in step ST11. The waveform information shown in FIGS. 13B and 14B is the waveform information of the correction handle load obtained by filtering the fluctuation frequency component in step ST13.

As shown in FIG. 13A, since walking is not stable for a user with low physical ability, the waveform information of the load data in the Fz direction during straight-ahead operation fluctuates. That is, the value of the handle load in the Fz direction detected by the detection unit 13 fluctuates during the straight-ahead operation of the user. The load correction unit 18 filters the fluctuation frequency component from the waveform information of the handle load in the Fz direction acquired by the detection unit 13. As a result, as shown in FIG. 13B, it is possible to cut the fluctuation of the waveform information of the handle load in the Fz direction during the straight-ahead operation. As a result, the user movement intention estimation unit 19 can easily estimate that the user's movement intention is a straight-ahead operation based on the corrected handle load.

Further, as shown in FIG. 14A, the waveform information of the load data in the My direction during the straight-ahead operation of a user having low physical ability also fluctuates. That is, the value of the handle load in the My direction detected by the detection unit 13 fluctuates during the straight-ahead operation of the user. The load correction unit 18 filters the fluctuation frequency component from the waveform information of the handle load in the My direction acquired by the detection unit 13. As a result, as shown in FIG. 14B, it is possible to cut the fluctuation of the waveform information of the handle load in the My direction during the straight-ahead operation. As a result, the user movement intention estimation unit 19 can easily estimate that the user's movement intention is a straight-ahead operation based on the corrected handle load.

Further, the user movement intention estimation unit 19 may estimate the turning radius at the time of turning. For example, for a user with weak legs, the robot 1 may turn gently by making the turning radius larger than usual. On the contrary, for a user with strong legs, the turning radius may be made smaller than usual to make a sharp turn. The turning radius is estimated from, for example, the value of the correction handle load.

Further, the user movement intention estimation unit 19 may acquire information on the rotation amount of the wheel 16 from the actuator control unit 21 and estimate the movement intention of the user based on the rotation amount information and the correction handle load information. good.

[Calculation of driving force]
The calculation of the driving force will be described with reference to FIG. FIG. 15 shows an exemplary flowchart of the driving force calculation process.

As shown in FIG. 15, in step ST21, the driving force calculation unit 20 acquires the information of the user's movement intention from the user movement intention estimation unit 19.

In step ST22, the driving force calculation unit 20 acquires information on the amount of rotation of the wheels 16 from the actuator control unit 21.

In step ST23, the driving force calculation unit 20 calculates the driving force based on the user's movement intention acquired in step ST21 and the information on the amount of rotation of the wheels 16. Specifically, the driving force calculation unit 20 is the difference between the current moving direction and moving speed calculated from the information on the amount of rotation of the wheel 16 and the moving direction and moving speed estimated from the information on the user's movement intention. The amount of rotation of the wheel 16 is calculated based on the above.

As an example, when the robot 1 is moving in the forward direction at a moving speed of 71 cm / s, ^{the driving force calculation unit 20 when the user increases the force of Fy +} to accelerate the moving speed to 77 cm / s. The operation of is explained. The driving force calculation unit 20 acquires information indicating that the amount of rotation of both the left and right wheels 16 is 2000 rpm in a state of moving at a speed of 71 cm / s in the forward direction. Next, the driving force calculation unit 20 calculates that the rotation amount of both the left and right wheels 16 needs to be 2500 rpm in order to make the moving speed of the robot 1 77 cm / s. The driving force calculation unit 20 calculates the driving force so as to increase the amount of rotation of the left and right wheels 16 by 500 rpm.

In the first embodiment, an example in which the driving force calculation unit 20 calculates the driving force based on the information of the user's movement intention and the information of the rotation amount of the wheel 16 acquired from the actuator control unit 21 has been described. However, it is not limited to this. For example, the driving force calculation unit 20 may calculate the driving force only from the information of the user's movement intention. That is, step ST22 may not be included in the driving force calculation process.

Further, the driving force calculation unit 20 may calculate the driving force based on the control table showing the correspondence relationship between the handle load and the rotation amount of the wheel 16. Specifically, the driving force calculation unit 20 may include a storage unit that stores a control table showing a correspondence relationship between the handle load and the rotation amount of the wheel 16. The driving force calculation unit 20 may calculate the rotation amount of the wheel 16 corresponding to the value of the handle load detected by the detection unit 13 by using the control table stored in the storage unit. Further, the control table may be updated by modifying the value of the handle load in the control table based on the load tendency data extracted from the user load tendency extraction unit 23.

[effect]
According to the walking support robot 1 according to the first embodiment, the following effects can be obtained.

According to the walking support robot 1 according to the first embodiment, the value of the handle load can be corrected based on the load tendency data of the user. With such a configuration, the robot 1 can correct the value of the handle load according to the tendency of the user.

For example, for a user who tends to swing left and right and walk, the value of the handle load is corrected by cutting the frequency of fluctuation caused by the left and right swing from the handle load. In this way, since the value of the handle load can be corrected according to the physical ability of the user, the moving direction and the moving speed of the robot 1 can be set for each user having a different physical ability. As a result, the robot 1 can be moved according to the physical ability of the user, so that the walking support of the user can be more comfortable.

In the first embodiment, the fluctuation frequency of the handle load is used as the load tendency data. By using the fluctuation frequency, the robot 1 can acquire the load tendency data of the user in a wide range from the fluctuation with small unevenness appearing in the waveform information of the handle load to the fluctuation with large unevenness, and can correct the handle load. As a result, the robot 1 can perform walking support according to the physical ability of the user.

In the first embodiment, the load tendency data generation unit 15, the load correction unit 18, the user movement intention estimation unit 19, the driving force calculation unit 20, and the actuator control unit 21 have, for example, a program for functioning these elements. A stored memory (not shown) and a processing circuit (not shown) corresponding to a processor such as a CPU (Central Processing Unit) may be provided, and the processor may function as these elements by executing a program. Alternatively, the load tendency data generation unit 15, the load correction unit 18, the user movement intention estimation unit 19, the driving force calculation unit 20, and the actuator control unit 21 may be configured by using an integrated circuit that functions these elements. ..

Although the movements of the walking support robot 1 have been mainly described in the first embodiment, these movements can also be executed as a walking support method.

In the first embodiment, an example of setting the fluctuation frequency in the moving direction and the moment direction has been described, but the present invention is not limited to this. For example, a common fluctuation frequency may be set in all directions. As a result, the handle load can be easily corrected.

In the first embodiment, an example of controlling the forward movement, the backward movement, the right turning movement, the left turning movement, and the like of the robot 1 by setting the rotation amounts of the two wheels 16 has been described, but the present invention is limited to this. Not done. For example, the rotation amount of the wheels 16 may be controlled by a brake mechanism or the like to control the operation of the robot 1.

In the first embodiment, when the load tendency data corresponding to the moving motion of the robot 1 exceeds a predetermined threshold value, the load correction unit 18 is the value of the handle load detected by the detection unit 13 based on the load tendency data. May be corrected (filtered). For example, in the straight movement of the robot 1 ( ^{forward movement in the Fy +} direction), when the load tendency data (fluctuation frequency) in the Mz direction becomes equal to or higher than the threshold of 0 Hz, the load data corresponding to the straight movement of the robot 1 is loaded. It may be corrected based on the data. With such a configuration, it is possible to filter fluctuation frequencies in the Mz direction that are unnecessary in the straight-ahead operation of the robot 1. The predetermined threshold value may be changed according to the physical ability of the user and the like. For example, the predetermined threshold value may be changed to 1 Hz based on the information that the fluctuation frequency occurs at 1 Hz in a healthy person. Further, the load tendency data corresponding to the moving motion of the robot 1 may be the load tendency data in the same direction as the moving direction of the robot 1, or the load tendency data in a direction different from the moving direction of the robot 1. May be good. For example, when the load tendency data of another user is set as a predetermined threshold value, the load tendency data of the user and the load tendency data of the other user are displayed in the same movement direction as the movement direction corresponding to the movement operation of the robot 1. You may compare.

(Embodiment 2)
The walking support robot according to the second embodiment of the present disclosure will be described. In the second embodiment, the points different from the first embodiment will be mainly described. In the second embodiment, the same or equivalent configurations as those in the first embodiment will be described with the same reference numerals. Further, in the second embodiment, the description overlapping with the first embodiment is omitted.

The second embodiment is different from the first embodiment in that the average load value is used as the load tendency data. The walking support robot according to the second embodiment has the same components as the walking support robot 1 according to the first embodiment, and is indicated by reference numeral "51" in FIGS. 1, 2, and 4. Is done.

FIG. 16 shows a load trend map 24 according to the second embodiment. As shown in FIG. 16, the load tendency map 24 stores the average load value in the movement direction during walking and the average load value in the bias direction of the center of gravity during walking as load tendency data for each movement direction of the user. ing.

[Generation of load tendency data]
The generation of load tendency data will be described with reference to FIG. FIG. 17 shows an exemplary flowchart of the load tendency data generation process of the walking support robot 51 (hereinafter, referred to as “robot 51”).

As shown in FIG. 17, in step ST31, it is determined whether or not the handle load is detected by the detection unit 13. In step ST31, it is determined whether or not the user is holding the handle portion 12. When the detection unit 13 detects the handle load, the process proceeds to step ST32. If the detection unit 13 does not detect the handle load, step ST31 is repeated.

In step ST32, the user load tendency extraction unit 23 estimates the current moving direction of the user based on the information on the amount of rotation of the wheels 16. Specifically, when the handle load is detected in step ST31, the actuator control unit 21 acquires information on the amount of rotation of the wheels 16. The rotation amount information acquired by the actuator control unit 21 is transmitted to the user load tendency extraction unit 23. For example, the user load tendency extraction unit 23 estimates the moving direction of the user based on the amount of rotation of the two wheels 16 arranged on the left and right.

In step ST33, the user load tendency extraction unit 23 adds the handle load detected in step ST31 to the past load data in the estimated movement direction of the user. Specifically, the user load tendency extraction unit 23 reads out the past load data stored in the load tendency map 24, and adds the handle load detected in step ST31 to the read past load data. The past load data means all the load data detected so far.

In step ST34, the user load tendency extraction unit 23 calculates the average load value in the moving direction and the average load value in the bias direction when the user is walking.

In step ST35, the user load tendency extraction unit 23 sets the calculated average load value in the moving direction and the average load value in the bias direction during walking as load tendency data. Specifically, the user load tendency extraction unit 23 transmits the calculated average load value information to the load tendency map 24, and the average load value in the moving direction of the user of the load tendency map 24 during walking and the average in the bias direction. Update with the load value.

[Estimation of user's movement intention]
The estimation of the user's movement intention will be described with reference to FIG. FIG. 18 shows an exemplary flowchart of the user's movement intention estimation process.

As shown in FIG. 18, in step ST41, the load correction unit 18 acquires information on the current handle load detected by the detection unit 13.

In step ST42, the user load tendency extraction unit 23 reads out the load tendency data of the user. Specifically, the user load tendency extraction unit 23 reads the past average load value from the load tendency map 24 and transmits the past average load value to the load correction unit 18.

In step ST43, the load correction unit 18 subtracts the past average load value from the current load data. As a result, the load correction unit 18 corrects the value of the handle load.

In step ST44, the user movement intention estimation unit 19 estimates the user's movement intention based on the corrected handle load information.

As an example, the correction of the handle load in the second embodiment will be described. Here, the correction of the handle load for a user walking with the center of gravity deviated to the right will be described.

[Correction of handle load using average load value]
FIG. 19A shows an example of waveform information of the current load data in the Mz direction during the straight-ahead operation of the user. As shown in FIG. 19A, since the center of gravity of the user is biased to the right, the detection unit 13 detects the load (moment) in the Mz direction even during the straight-ahead operation.

FIG. 19B shows the average load value of the past load data in the Mz direction. The user load tendency extraction unit 23 calculates the average load value of the past load data as shown in FIG. 19B by performing the average calculation on the waveform information of the past load data. In the case of FIG. 19B, the past average load value is 1.0 Nm in the Mz direction. In the second embodiment, the average load value shown in FIG. 19B is used as the load tendency data.

Next, the load correction unit 18 corrects the current load data based on the load tendency data. Specifically, the load correction unit 18 subtracts the past average load value of 1.0 Nm shown in FIG. 19B from the waveform information of the current load data shown in FIG. 19A in the Mz direction. FIG. 19C shows the waveform information of the current load data in the Mz direction corrected using the load tendency data. As shown in FIG. 19C, by subtracting the past average load value from the current load data, the load applied in the Mz direction is reduced as a whole. As a result, the load correction unit 18 can correct the steady rightward bias of the load.

The user movement intention estimation unit 19 estimates the user's movement intention based on the corrected current handle load information. As a result, the robot 51 can accurately determine the user's movement intention and move, so that the user does not need to finely adjust the traveling direction of the robot 51.

The example of the above correction has been described as an example of a user walking with the center of gravity biased to the right, but the present invention is not limited to this. For example, a user with a bent waist may have a downward load bias. In this case, the handle load value may be corrected by using the average load value in the Fz direction.

Further, when the forward movement of the robot 51 is performed based on the handle load values in the Fy direction and the Fz direction, the average load values in the Fy direction and the Fz direction may be used as the load tendency data. That is, the handle load value may be corrected by using the average load values in the Fy direction and the Fz direction during the forward movement of the robot 51. Further, when the turning operation of the robot 51 is performed based on the load (moment) in the Mz direction, the average load value in the Mz direction may be used as the load tendency data. That is, the handle load value may be corrected by using the average load value in the Mz direction during the turning operation of the robot 51. Further, the average load value in all directions of Fx, Fy, Fz, Mx, My, and Mz may be calculated, and the handle load value may be corrected by using the average load value in all directions. By correcting the handle load using the average load values in the plurality of directions in this way, the load tendency of the user can be grasped more accurately, so that the operation of the robot 51 more suitable for the physical ability of the user can be performed. It will be possible. In the correction of the handle load, at least one average load value in the Fx, Fy, Fz, Mx, My, and Mz directions is calculated according to the movement control of the robot 51, and the handle is used using the calculated average load value. The load may be corrected.

[effect]
According to the walking support robot 51 according to the second embodiment, the following effects can be obtained.

According to the walking support robot 51 according to the second embodiment, the average load value of the handle load is used as the load tendency data of the user. With such a configuration, it is possible to acquire the load constantly applied to each user as load tendency data and correct the load value, so that it is possible to perform walking support more suitable for the physical ability of the user. .. Further, by using the average load value of the handle load as the load tendency data of the user, it is possible to reduce the error of extracting the load tendency of the user.

In the second embodiment, an example in which all the handle loads detected in the past are used as the past load data when calculating the load tendency data has been described, but the present invention is not limited to this. The past load data used when calculating the load tendency data may be, for example, load data within a predetermined period. For example, the past load data used when calculating the load tendency data may be the past load data detected within a predetermined period (for example, one year). In this way, using only relatively new load data makes it easier to extract the user's current load tendency.

In the second embodiment, the load tendency map 24 may store the load tendency data during stable walking. The user load tendency extraction unit 23 may acquire the load tendency data from the load tendency map 24 and transmit the load tendency data during stable walking to the load correction unit 18. The load correction unit 18 may compare the load tendency data during stable walking with the load data of the current user, and correct the value of the steering wheel load when both data are different. For example, in the past load tendency, when the load in the Fz direction during stable walking of the user's straight-ahead motion is 10 N, when the user walks in a forward leaning posture and the handle load in the Fz direction becomes 20 N, the load correction unit 18 May correct the handle load in the Fz direction to the value of the handle load in the Fz direction during stable walking. That is, the load correction unit 18 may correct the load of 20N in the Fz direction to 1/2.

In the second embodiment, the load correction unit 18 has described an example of correcting the current load data by subtracting the past average load value from the current load data, but the present invention is not limited to this. For example, the load correction unit 18 may consider another parameter so that the handle load can be corrected according to the place of use of the robot 51, the usage time, the physical condition of the user, and the like.

Further, when the robot 1 performs movement control by the total value of the Fz value and the Fy value, the handle load may be corrected by changing the ratio when the Fz value and the Fy value are totaled. For example, the control at Fz: Fy = 8: 2 may be changed to Fz: Fy = 6: 4. Also, instead of making corrections only with the user's load tendency data, even if the user's load tendency data is compared with the average load tendency data of multiple users and the correction ratio is changed so as to reduce the difference portion. good. As a method of calculating the average of a plurality of users, it may be created for each group classified by a combination of age, gender, place, walking ability (walking speed, walking rate, stride length, standing posture, left-right shaking), etc. ..

Further, the load correction unit 18 may correct the handle load by multiplying the current load data by a correction coefficient calculated from the past load tendency data. Hereinafter, an example of correction of the handle load using the correction coefficient will be described.

[Correction of handle load using correction coefficient]
FIG. 20A shows an example of waveform information of past load data in the Mz direction when the user goes straight. FIG. 20B shows the average load value of the past load data in the Mz direction shown in FIG. 20A. The user load tendency extraction unit 23 performs an average calculation on the waveform information of the past load data shown in FIG. 20A. As a result, the average load value of the past load data as shown in FIG. 20B is calculated as the load tendency data. In the case of FIG. 20B, the past average load value is −1.0 Nm in the Mz direction.

Next, the average load value is calculated from the current load data. FIG. 21A shows an example of waveform information of the current load data in the Mz direction during the straight-ahead operation of the user. FIG. 21B shows the average load value of the current load data in the Mz direction shown in FIG. 21A.

The load correction unit 18 performs an average calculation on the waveform information of the current load data shown in FIG. 21A. As a result, the average load value of the current load data as shown in FIG. 21B is calculated. In the case of FIG. 21B, the current average load value is −2.0 Nm in the Mz direction.

The load correction unit 18 calculates the correction coefficient by dividing the past average load value by the current average load value. In this case, the correction coefficient is (-1.0 Nm / -2.0 Nm) = 0.5. The load correction unit 18 corrects the handle load by multiplying the correction coefficient by the waveform information of the current load data. That is, the value of the handle load in the Mz direction detected by the detection unit 13 is corrected by multiplying the waveform information of the current load data shown in FIG. 21A by the correction coefficient 0.5.

FIG. 22 shows an example of waveform information of the corrected load data. As shown in FIG. 22, the handle load detected by the detection unit 13 (see the waveform information in FIG. 21A) is corrected by multiplying the correction coefficient. In this way, the load correction unit 18 may correct the current handle load by multiplying the current load data by a correction coefficient calculated based on the past load tendency data.

(Embodiment 3)
The walking support robot according to the third embodiment of the present disclosure will be described. In the third embodiment, the points different from the first and second embodiments will be mainly described. In the third embodiment, the same or equivalent configurations as those in the first and second embodiments will be described with the same reference numerals. Further, in the third embodiment, the description overlapping with the first and second embodiments is omitted.

The third embodiment is different from the first and second embodiments in that the load is corrected based on the physical information.

FIG. 23 is a control block diagram showing a main control configuration of the walking support robot 61 (hereinafter, referred to as “robot 61”) according to the third embodiment. Further, the control block diagram of FIG. 23 also shows the relationship between each control configuration and the information to be handled. FIG. 24 is a control block diagram showing a detailed control configuration of walking support of the robot 61.

As shown in FIGS. 23 and 24, the robot 61 of the third embodiment is different from the robots 1 and 51 of the first and second embodiments in that it includes a physical information estimation unit 25 and a physical information database 26. In the third embodiment, the physical information database 26 is not an essential configuration.

The physical information estimation unit 25 estimates the physical information of the user. In the present specification, the physical information is physical information related to walking, and includes, for example, walking speed, walking rate, body inclination, body shaking, stride length, and muscle strength. The physical information is not limited to these. For example, the physical information may include an average load in the moving direction, an average load in the biased direction of the center of gravity, a fluctuation frequency in the moving direction, a fluctuation frequency in the left-right direction, and the like.

The body information estimation unit 25 has, for example, information on the handle load detected by the detection unit 13, information on the amount of rotation of the rotating body 16 acquired by the actuator control unit 21, and drive calculated by the driving force calculation unit 20. Estimate physical information based on force information.

The physical information database 26 stores physical information for each user. The physical information database 26 stores and updates the physical information estimated by the physical information estimation unit 25 for each user.

[Estimation of physical information]
The estimation of physical information will be described with reference to FIG. FIG. 25 shows an exemplary flowchart of the physical information estimation process of the walking support robot 61 according to the third embodiment.

As shown in FIG. 25, in step ST51, the detection unit 13 detects a change in the handle load. When the detection unit 13 detects a change in the handle load, the process proceeds to step ST52. If the detection unit 13 does not detect a change in the handle load, step ST51 is repeated.

In step ST52, the physical information estimation unit 25 estimates the user's moving direction based on the information on the amount of rotation of the wheels 16. Specifically, when a change in the handle load is detected in step ST51, the actuator control unit 21 acquires information on the amount of rotation of the wheels 16. The rotation amount information acquired by the actuator control unit 21 is transmitted to the physical information estimation unit 25. The physical information estimation unit 25 estimates the user's movement direction based on the information on the amount of rotation of the wheel 16, that is, the rotation direction and rotation speed of the wheel. In the third embodiment, the physical information estimation unit 25 estimates the moving direction of the user based on the amount of rotation of the two wheels 16 arranged on the left and right. For example, the physical information estimation unit 25 may estimate that the user is turning to the left when the amount of rotation of the right wheel 16 is larger than the amount of rotation of the left wheel 16. Further, the body information estimation unit 25 may estimate that the robot 61 is performing a straight-ahead operation when the left and right wheels 16 have the same rotation speed and are rotating in the forward direction.

In step ST53, the physical information estimation unit 25 acquires the waveform information of the handle load in the estimated movement direction of the user. The waveform information of the handle load in the movement direction of the user is not particularly limited, but for example, when the movement direction of the user is the Fy ⁺ direction, the waveform information of the handle load in the Fz direction or the waveform information of the moment in the My direction is used. You may.

In step ST54, the physical information estimation unit 25 adds up the acquired waveform information of the handle load and the waveform information of the past handle load. For example, past waveform information is stored in the physical information database 26. The physical information estimation unit 25 reads the past waveform information from the physical information database 26, and adds the acquired current waveform information to the past waveform information. As the input waveform information of the handle load, for example, the waveform information of the handle load shown in FIG. 8 can be mentioned.

In step ST55, the physical information estimation unit 25 acquires the driving force information. Specifically, the physical information estimation unit 25 acquires driving force information from the driving force calculation unit 20.

In step ST56, the physical information estimation unit 25 estimates the physical information based on the waveform information added up in step ST54 and the driving force information acquired in step ST55.

In the third embodiment, the physical information estimation unit 25 estimates walking speed, walking rate, body inclination, body shaking, stride length, and muscle strength as physical information.

The walking speed is calculated by, for example, calculating the moving distance based on the information of the driving force and dividing by the moving time.

The walking rate means the number of steps per unit time. The walking rate is calculated by dividing the number of steps by the travel time. The number of steps is calculated based on the change information of the handle load. For example, when the user is moving straight, the right foot and the left foot are alternately put out in the forward direction while walking. The waveform information of the handle load of the user who is moving straight changes in conjunction with the walking cycle. Therefore, in the waveform information of the handle load, when the toes of the right foot or the left foot are separated from the ground, that is, when the toes are taken off, the peak point is convex in the ^{Fz + direction.} Therefore, the number of steps can be calculated by counting from one peak point to the next peak point as one step. The method of calculating the convex peak point may be calculated based on, for example, the point at which the amount of change in the handle load changes from an increase to a decrease, or a quadratic curve estimated by the least squares method and the estimated quadratic curve. It may be calculated based on the maximum value of.

The body tilt is calculated based on the handle load information. The body tilt is calculated based on the load bias caused by the tilt of the user's center of gravity. For example, for a user who walks with the center of gravity biased to the right, ^{the load in the Fx +} direction is calculated as the inclination of the body.

Body sway is calculated by calculating the sway frequency based on the total waveform information. Specifically, the physical information estimation unit 25 calculates the fluctuation frequency by performing frequency analysis of the handle load in the estimated movement direction of the user.

The stride is calculated based on the distance traveled between the toes and the takeoff. For example, the movement distance between the toe takeoff is calculated based on the waveform information of the handle load and the information of the driving force. As described above, the waveform information of the handle load and the walking cycle change in conjunction with each other. For example, the body information estimation unit 25 ^{counts the peak point convex in the Fz +} direction and the next peak point as one step in the waveform information of the handle load, and also determines the load in the Fx direction and / or the moment in the Mz direction. Identify whether it is the right foot or the left foot based on it. Next, the physical information estimation unit 25 calculates the movement distance for each step based on the driving force information.

Muscle strength is calculated from the bias of the load value for each foot position, the difference in stride length between the left and right, the difference in the amount of movement, and the like. For example, muscle strength is expressed on a 6-point scale (levels 0 to 5) for each foot muscle (for example, tibialis anterior muscle, fibula muscle, etc.) used for each walking motion of the user. The higher the number, the stronger the muscle strength.

In the third embodiment, the above-mentioned physical information data is calculated based on the information for 10 steps. Specifically, the average value of the data for 10 steps is calculated as physical information. The physical information is not limited to the average value of the data for 10 steps. For example, physical information is not limited to data for 10 steps, but is based on data for 1 step or more and less than 10 steps, data for steps more than 10 steps, or (data for 10 steps) × (data for multiple times). May be calculated. Further, the physical information may be calculated by a median value or the like in addition to the average value of the data for 10 steps.

The physical information data calculated in this way is stored in the physical information database 26. Further, the physical information stored in the physical information database 26 is updated with new information when the physical information is estimated.

FIG. 26A is an example of physical information stored in the physical information database 26 of the robot 61. As shown in FIG. 26A, as the physical information of the user A, the walking speed, the walking rate, the inclination of the body, the shaking of the body, the stride length, and the muscle strength of the foot are used.

FIG. 26B is another example of the physical information stored in the physical information database 26 of the robot 61. As shown in FIG. 26B, as physical information of the straight-ahead motion of the user A, walking speed, walking rate, average load in the moving direction, average load in the biased direction of the center of gravity, fluctuation frequency in the moving direction, fluctuation frequency in the left-right direction, stride length. , The muscle strength of the foot is used. Further, the physical information shown in FIG. 26B indicates that the input waveforms of the handle load are the sum of "No. 1" and "No. 3".

[Estimation of user's movement intention]
In the third embodiment, the handle load is corrected based on the physical information, and the user's movement intention is estimated based on the corrected handle load information. FIG. 27 shows an exemplary flowchart of the robot 61 user's movement intention estimation process.

As shown in FIG. 27, in step ST61, the detection unit 13 acquires the handle load information.

In step ST62, the physical information estimation unit 25 acquires the physical information corresponding to the movement direction of the user based on the information of the handle load. Specifically, the physical information estimation unit 25 estimates the user's moving direction based on the handle load information. Next, the physical information estimation unit 25 acquires the physical information corresponding to the estimated movement direction of the user from the physical information database 26. The body information estimation unit 25 transmits the acquired body information to the load correction unit 18.

In step ST63, the load correction unit 18 sets a threshold value for whether or not to correct the handle load information based on the physical information. In this step ST63, the load correction unit 18 adjusts the strength of the correction by setting a threshold value for the correction of the handle load information according to the physical ability of the user.

In the third embodiment, the threshold value of whether or not to apply the correction means the threshold value of whether or not to apply the correction such as the inclination of the body (load applied in the inclination direction) and the shaking of the body (fluctuation frequency).

In the present specification, the strength of the correction means the ease with which the correction is applied. A high correction strength means that the correction is easy to be applied, and a low correction strength means that the correction is difficult to be applied.

For example, a user with high physical ability can walk as intended by the user without correcting the handle load. In this case, since the user wants to move quickly, even if a large load is applied in the straight direction, the handle load may be corrected to be small. In order to avoid this, the load correction unit 18 sets the threshold value high and lowers the strength of the correction. On the other hand, a user with low physical ability has a large inclination or sway of the body, and it is difficult to walk as intended by the user unless the handle load is corrected. In this case, the load correction unit 18 sets the threshold value low and increases the strength of the correction.

In the present specification, a user having high physical ability means, for example, a user having physical ability equal to or higher than the average physical ability of people of the same age as the user. Further, a user having low physical ability means, for example, a user having a physical ability lower than the average physical ability of people of the same age as the user.

In the third embodiment, the load correction unit 18 determines the height of the physical ability of the user based on the average physical information of a person of the same age as the user. Specifically, the load correction unit 18 uses the average value of the walking speed of people of the same age as the user (hereinafter referred to as "average walking speed of the same age") as the reference physical information, and the physical ability of the user. The height of is judged. For example, the load compensator 18 determines that the user's walking speed is higher than the average walking speed of the same age group, and that the user's walking speed is slower than the average walking speed of the same age group, the physical ability is low. judge.

The average physical information of a person of the same age as the user is stored in, for example, the physical information database 26. The average physical information of a person of the same age as the user means, for example, the average physical information of a person aged 63 when the user is 63 years old. The average physical information of a person of the same age as the user is stored in, for example, the physical information database 26. In the third embodiment, when the physical information estimation unit 25 transmits the physical information of the user to the load correction unit 18, the average physical information of a person of the same age as the user may be transmitted.

When the load correction unit 18 determines that the user's physical ability is high, that is, the walking speed of the user is equal to or higher than the average walking speed of the same age group, the load correction unit 18 sets a high threshold value for whether or not to correct the walking speed. On the other hand, when the load correction unit 18 determines that the physical ability of the user is low, that is, the walking speed of the user is slower than the average walking speed of the same age group, the load correction unit 18 sets a low threshold value for whether or not to correct the walking speed. As described above, in the third embodiment, the walking speed is used as a reference for determining the threshold value for whether or not to apply the correction.

As an example, a case where the load correction unit 18 determines that the walking speed of the user is equal to or higher than the average walking speed of the same age will be described. In this example, when the average value of the load in the straight direction of people of the same age as the user is 20N, the load correction unit 18 sets the threshold value for correcting the inclination of the body to 20N. Further, when the average value of the shaking in the straight direction of people of the same age as the user is 1.0 Hz, the load correction unit 18 sets the threshold value for correcting the shaking of the body to 1.0 Hz. In this way, it is possible to set a high threshold value for a user with high physical ability and make it difficult to apply correction.

Further, a case where the load correction unit 18 determines that the walking speed of the user is slower than the average walking speed of the same age will be described. In this example, in the case of a user with low physical ability, correction may always be performed according to the load tendency. It should be noted that a threshold value lower than the threshold value of the user having high physical ability may be set so that the correction can be easily applied to the user having low physical ability.

In step ST64, the load correction unit 18 determines whether or not the handle load information is equal to or greater than the threshold value set in step ST63. If the handle load information is equal to or greater than the threshold value, the process proceeds to step ST65 to correct the handle load information. If the handle load information is less than the threshold value, the process proceeds to step ST67.

As an example, when it is determined that the walking speed of the user is equal to or higher than the average walking speed of the same age group, the load compensating unit 18 receives a load of 20 N or more in the straight-ahead direction, or shakes in the straight-ahead direction. When the frequency becomes 0 Hz or higher, the process proceeds to step ST65. On the other hand, the load correction unit 18 proceeds to step ST67 when a load of less than 20 N is applied in the straight direction and the vibration in the straight direction is less than 1.0 Hz.

Further, when it is determined that the walking speed of the user is slower than the average walking speed of the same age, the load correction unit 18 proceeds to step ST65 when the shaking in the straight-ahead direction becomes 0 Hz or more. In the third embodiment, since the threshold value for correcting the shaking in the straight-ahead direction is set to 0 Hz, the correction is substantially applied when a handle load is applied.

In step ST65, the load correction unit 18 corrects the handle load information based on the load tendency data (fluctuation frequency, etc.). Since step ST65 is the same as the load correction process of the first and second embodiments, the description thereof will be omitted.

In step ST66, the user movement intention estimation unit 19 estimates the user's movement intention (movement direction, movement speed) based on the corrected handle load information. Since step ST66 is the same as the process of estimating the user movement intention in the first and second embodiments, the description thereof will be omitted.

In step ST67, the load correction unit 18 estimates the user's movement intention without correcting the handle load information. Since step ST67 is the same as the process of estimating the user movement intention in the first and second embodiments, the description thereof will be omitted.

In this way, the robot 61 provides walking support according to the physical ability of the user by setting a threshold value for whether or not to correct the load based on the physical information.

[effect]
According to the walking support robot 61 according to the third embodiment, the following effects can be obtained.

According to the walking support robot 61 according to the third embodiment, a threshold value for whether or not to correct the load is set based on the physical information. With such a configuration, the strength of the correction can be adjusted, so that walking support can be performed according to the physical ability of the user.

For example, a user with high physical ability can walk as intended by the user without making corrections. In this case, the robot 61 can prevent the handle load from being overcorrected by increasing the threshold value for whether or not to apply the correction. For example, if the user wants to go straight quickly, it is possible to suppress control contrary to the user's intention that the speed is suppressed by the correction even though the handle load is applied.

In addition, a user with low physical ability may apply a load unintended by the user to the steering wheel. In this case, the robot 61 makes it easier to correct the handle load by lowering the threshold value for whether or not to apply the correction. As a result, even a user with low physical ability can walk as intended by the user.

In the third embodiment, an example in which the walking speed is used as a reference for determining the threshold value for whether or not to apply the correction has been described, but the present invention is not limited to this. The criteria for determining the threshold value may be set based on physical information, and for example, walking speed, walking rate, stride length, muscle strength, load bias, load swing, or a combination thereof may be used. By using these items as a reference for determining the threshold value, the strength of the correction can be set more finely.

Further, as a criterion for determining the threshold value, an average value, a median value, or the like of physical information (walking speed, walking rate, etc.) of people of the same age as the user may be used.

In the third embodiment, when it is determined that the physical ability of the user is low, that is, the walking speed of the user is slower than the average walking speed of the same age group, the threshold value for correcting the body sway in the straight direction is set to 0 Hz. As explained, but not limited to this. When the physical ability of the user is low, for example, the threshold value for correcting the body shaking may be set to a different value such as 0.5 Hz, or the threshold value for correcting the body tilt may be set to 10 N in the straight direction. ..

In the third embodiment, when it is determined that the user's physical ability is high, that is, the walking speed of the user is equal to or higher than the average walking speed of the same age group, the average value of the body inclination load of people of the same age group as the user is 20 N. An example of setting an average value of 1.0 Hz of body shaking as a threshold has been described, but the present invention is not limited to this. When the user's physical ability is high, for example, the threshold value may be set to a different value, or the threshold value may be set not to be corrected.

In the third embodiment, an example in which the load correction unit 18 determines the height of the user's physical ability based on the physical information of people of the same age as the user to set a threshold value according to the user's physical ability has been described. However, it is not limited to this.

As an example, an example in which the load correction unit 18 sets a threshold value according to the physical ability of each age group will be described. In this example, the threshold is set for each age group. For example, in the age group of 50 to 60 years old, the age group of 60 years old to less than 70 years old, and the age group of 70 years old to less than 80 years old, the threshold value of the load in the traveling direction is set to the average value of 15N, 10N, and 5N for each age group. , The threshold value of the shaking in the traveling direction may be set to the average values of 1.0 Hz, 1.2 Hz, and 1.4 Hz for each age group.

In this example, the load correction unit 18 may determine which age the user has physical ability based on the physical information of the user, and set a threshold value according to the age. For example, when the load correction unit 18 determines that the user's physical ability corresponds to the physical ability of the age group 60 to 70 based on the user's physical information (walking speed, walking rate, etc.), the load in the traveling direction The threshold value of is set to 10N, and the threshold value of shaking in the traveling direction may be set to 1.2Hz.

In the third embodiment, an example of setting the threshold value when the physical ability of the user is high and when the physical ability is low has been described, but the present invention is not limited to this. For example, when it is determined that the physical ability of the user is low, as described above, a threshold value set according to the physical ability of each age group may be set. For example, when it is determined that the physical ability of the user is low, the threshold value may be changed stepwise according to the physical ability of each age group.

In the third embodiment, the physical information estimation unit 25 has described an example of estimating physical information based on the waveform information added in step ST54 and the driving force information acquired in step ST55, but the present invention is limited to this. Not done. For example, the body information estimation unit 25 may estimate the body information based on the waveform information added up in step ST54 and the rotation amount of the rotating body 16 measured by the actuator control unit 21.

[User notification section]
FIG. 28 shows another control block diagram showing the control configuration of the walking support of the robot 61. As shown in FIG. 28, the robot 61 may include a user notification unit 27.

The user notification unit 27 notifies the user of at least one of the physical information and the user's movement intention. Specifically, the user notification unit 27 acquires the physical information estimated from the physical information estimation unit 25. Further, the user notification unit 27 acquires information on the user's movement intention from the user movement intention estimation unit 19.

The user notification unit 27 is composed of, for example, an LED, a display, a speaker, or the like. The user notification unit 27 may be composed of an LED, a display, a speaker, or a combination thereof.

The case where the user notification unit 27 has an LED will be described. For example, when the user notification unit 27 acquires the physical information, the load may be corrected based on the physical information, and the LED may be turned on when the user's movement intention is estimated. The information to be presented may be identified according to the lighting pattern of the LED.

The case where the user notification unit 27 has a display will be described. When the user notification unit 27 acquires physical information, for example, a message such as "Your walking speed is XX", "Walking rate is XX", or "Right leg muscle strength is weak" is displayed on the display. It may be displayed. In addition, when the user notification unit 27 corrects the load based on physical information and estimates the user's movement intention, for example, "supports according to you" and "changes control according to you" on the display. , "Strengthen the brakes", "Suppress shaking", "Stabilize" and so on. The message displayed on the display is not limited to these.

The case where the user notification unit 27 has a speaker will be described. When the user notification unit 27 acquires physical information, for example, the speaker outputs voices such as "Your walking speed is XX", "Walking rate is XX", and "Right leg muscle strength is weak". You may. In addition, when the user notification unit 27 corrects the load based on physical information and estimates the user's movement intention, for example, the speaker "supports according to you" and "changes the control according to you". , "Strengthen the brakes", "Suppress shaking", "Stabilize" and so on. The sound output by the speaker is not limited to these.

By providing the user notification unit 27 in this way, the user can visually and / or aurally acquire physical information or information on walking support of the robot.

When the user notification unit 27 notifies, the user himself / herself grasps daily physical information, which leads to motivation to maintain and improve physical ability or to arouse attention during walking.

Further, by notifying the user notification unit 27, the user can grasp the control state of the robot 61, and it is possible to adapt to a large change in the operation feeling such that the brake is strengthened.

Although the present disclosure has been described in each embodiment with some detail, the disclosure content of these embodiments should vary in the details of the configuration. In addition, changes in the combination and order of elements in each embodiment can be realized without departing from the scope and ideas of the present disclosure.

The correction of the handle load based on the load tendency data described in the first to third embodiments is an example, and is not limited thereto. As the correction of the handle load based on the load tendency data, various well-known correction methods may be adopted. As the correction method, for example, regarding the fluctuation in the direction of the center of gravity, a method of smoothing with a moving average according to the degree of fluctuation, a method of removing the fluctuation by smoothing with a median filter, and a frequency analysis are performed. A method of cutting or reducing a specific frequency may be adopted.

The present disclosure is applicable to a walking support robot and a walking support method that support walking of a more comfortable user.

1, 51, 61 Walking support robot 11 Main body 12 Handle 13 Detection 14 Moving device 15 Load trend data generation 16 Rotating body 17 Driving unit 18 Load correction unit 19 User movement intention estimation unit 20 Driving force calculation unit 21 Actuator control Unit 22 Actuator 23 User load trend extraction section 24 Load trend map 25 Physical information estimation section 26 Physical information database 27 User notification section

Claims (17)

A walking support robot that supports the user's walking
With the main body
A handle portion provided on the main body portion and which can be grasped by the user, and a handle portion.
A detection unit that detects the load applied to the handle unit and
A moving device having a rotating body and controlling the rotation of the rotating body according to a load detected by the detection unit to move the walking support robot.
Based on the past load data within a predetermined period applied to the handle portion acquired while the walking support robot is moving, load tendency data indicating the tendency of the load applied to the handle portion is generated , and the load tendency of the user is generated. A load trend data generator that sends data to a database that stores data and updates the load trend data,
With
The moving device is
Based on the load tendency data, a load correction unit that corrects the value of the load detected by the detection unit so as to reduce the load not intended by the user, and a load correction unit.
A user movement intention estimation unit that estimates the user's movement intention based on the load value corrected by the load correction unit, and a user movement intention estimation unit.
A driving force calculation unit that calculates a driving force based on the user's movement intention,
An actuator control unit that controls the amount of rotation of the rotating body based on the driving force calculated by the driving force calculation unit, and an actuator control unit.
Have,
Walking support robot. The load tendency data generation unit generates load tendency data for each movement motion of the walking support robot, and generates load tendency data.
The load correction unit corrects the value of the load based on the load tendency data corresponding to the movement operation of the walking support robot when the load is detected.
The walking support robot according to claim 1. When the load tendency data corresponding to the movement motion of the walking support robot becomes equal to or higher than a predetermined threshold value, the load correction unit corrects the load value based on the load tendency data.
The walking support robot according to claim 2. The load tendency data is a fluctuation frequency calculated from the past load data.
The load correction unit corrects the value of the load by filtering the frequency component of the fluctuation from the load detected by the detection unit.
The walking support robot according to any one of claims 1 to 3. The load tendency data is an average load value calculated from the past load data.
The load correction unit corrects the value of the load based on the average load value.
The walking support robot according to any one of claims 1 to 3. The load correction unit corrects the load value by subtracting the average load value from the load detected by the detection unit.
The walking support robot according to claim 5. In addition
It is provided with a physical information estimation unit that estimates the physical information of the user.
The load correction unit sets a threshold value for whether or not to correct the load value based on the body information estimated by the body information estimation unit.
The walking support robot according to any one of claims 1 to 6. In addition
The walking support robot according to claim 7, further comprising a user notification unit that notifies the user of the physical information. The user notification unit notifies the user of the user's intention to move.
The walking support robot according to claim 8. In addition
A storage unit for storing a control table showing the correspondence between the load applied to the handle unit and the rotation amount of the rotating body is provided.
The actuator control unit drives and controls the rotating body with a rotation amount corresponding to the load detected by the detection unit by the control table stored in the storage unit.
The control table is updated by modifying the value of the load based on the load tendency data.
The walking support robot according to claim 1. The detection unit detects a plurality of axial loads applied to the handle unit, and receives the load.
The moving device controls the rotation of the rotating body to switch the moving operation of the walking support robot according to the magnitude of the load applied to each of the plurality of axial directions.
The walking support robot according to any one of claims 1 to 10. The walking support robot according to claim 11, wherein the moving motion includes a straight-ahead motion, a backward motion, and a turning motion of the walking support robot. The walking support robot according to claim 12, wherein the user movement intention estimation unit estimates the turning radius in the turning motion based on the value of the load corrected by the load correction unit. It is a walking support method that supports the user's walking using a walking support robot.
A step of detecting the load applied to the handle portion of the walking support robot by the detection unit,
A step of moving the walking support robot by controlling the rotation of the rotating body of the walking support robot by a moving device according to the load detected by the detection unit.
The load trend data generation unit, the walking aid on the basis of the historical loading data within a predetermined period of time it took for the handle portion which has been acquired during the movement of the robot, the load trend data which indicates the tendency of load applied to the handle portion and generate, steps and sent to the database that stores load trend data of the user to update the load trend data,
A step of correcting the value of the load detected by the detection unit by the load correction unit so as to reduce the load not intended by the user based on the load tendency data.
A step of estimating the user's movement intention by the user movement intention estimation unit based on the load value corrected by the load correction unit.
A step of calculating a driving force by a driving force calculation unit based on the user's movement intention,
A step in which the actuator control unit controls the amount of rotation of a rotating body provided in the moving device of the walking support robot based on the driving force.
Walking support methods, including. In addition
A step of estimating the physical information of the user by the physical information estimation unit based on the load information detected by the detection unit, the rotation amount information of the rotating body, and the driving force information of the moving device. Including
The step of correcting the load value is to correct the load value based on the physical information.
The walking support method according to claim 14. In addition
The walking support method according to claim 15, further comprising a step of notifying the user of the physical information by a user notification unit. The notifying step notifies the user of the user's intention to move.
The walking support method according to claim 16.

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