JP7847501B2 - Method, program, and control system for multiple mobile robots. - Google Patents

Description

This invention relates to a method for controlling multiple mobile robots and a control system for multiple mobile robots.

Patent Document 1 describes a technology in which a management server maintains robot information of an autonomous mobile robot, periodically acquires location information from the autonomous mobile robot, and, upon receiving a distress notification from the autonomous mobile robot, instructs its subordinate robots to provide assistance. The rescue robots that receive the distress instruction then search for the robot in question, calculate the absolute position and orientation of the distressed robot from its relative position, and perform recovery of the distressed robot.

Japanese Patent Publication No. 2010-3240

Currently, multiple autonomous mobile robots are used within factories, logistics centers, and office buildings for purposes such as transporting goods, guiding people, and security. These autonomous mobile robots typically utilize laser sensors or cameras for self-positioning.

Autonomous mobile robots using SLAM (Simultaneous Localization and Mapping) technology estimate their own position by sensing feature points of the surrounding environment, such as wall shapes. However, in spaces with few features, or in environments where features change due to changes in the arrangement of surrounding objects, position estimation may fail, rendering the robot unable to move.

As an example of a recovery method for position estimation failures, Patent Document 1 is proposed. Patent Document 1 describes a technology in which a management server issues search instructions to an autonomous mobile robot, which then performs the search. However, it has become clear that there is room for improvement in the method of determining the area to be searched by the robot, and in particular, there is room for improvement in search efficiency.

This invention provides a control method and a control system for multiple mobile robots that can improve search efficiency compared to conventional methods.

The present invention includes multiple means for solving the above problems, but one example is a method for controlling multiple mobile robots equipped with sensors, comprising the steps of: determining a first search range for the first robot when the current position of the first robot is unknown, based on the past movement trajectory of the first robot, error information in the past position estimation of the first robot, and the amount of movement; transmitting a search command for the first search range to a second robot different from the first robot; and receiving sensor information from the second robot in the first search range and estimating the current position of the first robot , wherein in the step of estimating the current position of the first robot, sensor information from the second robot in the first search range is received, an unknown object is extracted, the second search range is limited to the area around the unknown object, a search command for the second search range is transmitted to the second robot, sensor information from the second robot in the second search range is received, the current position of the first robot is estimated, and the travel path of the third robot is set excluding the first and second search ranges .

According to the present invention, since the search efficiency can be improved compared to conventional methods, the work efficiency of autonomous mobile robots can be improved. Other issues, configurations, and effects will be clarified by the following description of the embodiments.

This is a block diagram showing an example configuration of a multi-mobile robot control system in an embodiment. This flowchart shows an example of the position recovery control process in the multiple mobile robot control method of the embodiment. This figure shows an example of a method for determining the search range in the multiple mobile robot control method of the embodiment. This figure shows an example of a method for determining the search range in the multiple mobile robot control method of the embodiment. This figure shows an example of a method for determining the search range in the multiple mobile robot control method of the embodiment. This figure shows an example of a method for determining the search range in the multiple mobile robot control method of the embodiment. This figure shows an example of a method for determining the search range in the multiple mobile robot control method of the embodiment. This figure shows an example of a method for determining the search range in the multiple mobile robot control method of the embodiment. This figure shows an example of limiting the search range using obstacle sensor information in the multiple mobile robot control method of the embodiment. This figure shows an example of a method for recognizing the first robot using the sensors of the second robot in the multiple mobile robot control method of the embodiment. This figure shows an example where the sensor information received by the first robot in the multiple mobile robot control method of the embodiment is from a fixed sensor.

The present invention describes an embodiment of a control method, program, and control system for multiple mobile robots, which allows for the recovery of position estimation using other autonomous mobile robots when one autonomous mobile robot fails to estimate its position. This embodiment is illustrated with reference to Figures 1 to 11. In the drawings used herein, identical or corresponding components are denoted by the same or similar reference numerals, and repeated descriptions of these components may be omitted.

In a control method, program, and control system for controlling multiple autonomously mobile first robots 100 and second robots 200 equipped with sensors, the search range is preferably determined using past movement trajectories, error information, and movement amounts of the lost robot collected periodically by a management server. The nearest second robot 200 is selected as the rescue robot, and an object resembling the lost robot is detected from the rescue robot's sensor information. By limiting the search range to an area with a high probability of presence, the lost robot can be quickly found and recognized. The absolute position of the recognized lost robot can be transmitted to the lost robot, allowing it to recover from position estimation failures by resetting its position.

The following embodiment uses a factory item transport robot operating in an indoor, flat environment as an example. We will explain an example where, of the two mobile robots shown in Figure 1, the first robot 100 is the one that failed to estimate its position, and the second robot 200 is the rescue robot.

The following describes this embodiment, starting with its configuration and then its processing flow.

<Structure>
Figure 1 is a diagram of the system configuration according to this embodiment. Below, we will first explain the overview of each of these devices, and then describe their details later when explaining the processing flow.

The first robot 100 shown in Figure 1 is equipped with a sensor 101 that acquires map information.

The first robot 100's information processing device 109 includes a sensor processing unit 105 that processes map information acquired by the sensor 101, a self-position calculation unit 106 that calculates its own position using the sensor 101 information and shared information, a position information acquisition unit 107 that acquires its own position based on information from wheel encoders and other components incorporated into the first robot 100, a position estimation failure determination unit 104 that determines a failure in position estimation from the information from the sensor 101 and the self-position calculation unit, and a travel control unit 108 that controls the movement of the first robot 100. Each of these units is composed of a program executed by the first robot 100's central processing unit 102.

The information transmission/reception unit 103 of the first robot 100 is connected to the network 8 in order to exchange map information with the management server 1.

The second robot, 200, is equipped with a sensor 201 that acquires map information.

The second robot 200's information processing device 209 includes a sensor processing unit 205 that processes map information acquired by the sensor 201, a self-position calculation unit 206 that calculates its own position using the sensor 201 information and shared information, a position information acquisition unit 207 that acquires its own position based on information from wheel encoders and other components incorporated into the second robot 200, a position estimation failure determination unit 204 that determines a failure in position estimation from the information from the sensor 201 and the self-position calculation unit, and a travel control unit 208 that controls the movement of the second robot 200. Each of these units is composed of a program executed by the second robot 200's central processing unit 202.

The information transmission/reception unit 203 of the second robot 200 is connected to the network 8 in order to exchange map information with the management server 1.

The management server 1 has an information processing device 7. This information processing device 7 of the management server 1 has an information transmission/reception unit 6, a main memory 5, a memory 4, and a map information display unit 3.
It comprises a first search range determination unit, a search command transmission unit, and an estimation unit. Memory 4 has a map information holding unit 41, a position range calculation unit 42, a search command management unit 43, and a coordinate transformation processing unit 44, which are programs executed by the central processing unit (CPU) 2 of the management server 1.

The map information exchanged between the network and the information transmission/reception unit 6 of the management server 1 is written to the main memory 5 by the map information storage unit 41. The main memory 5 is a storage medium and may be an HDD, SSD, or Flash memory. The network 8 may be configured as a wireless LAN, and the map information exchanged may use TCP/IP socket communication.

Furthermore, information communication regarding map data may be conducted directly with other robots connected to the network, and there may be two or more robots connected to the network.

The sensors 101 of the first robot 100 and 201 of the second robot 200 consist of at least one of the following: LiDAR, RGB-D camera, monocular camera, stereo camera, 360-degree camera, infrared camera, GPS, etc., and can be a combination of different types of sensors. It is assumed that the first robot 100 and the second robot 200 are capable of high-precision position estimation using these sensors 101 and 201.

In this embodiment, the second robot 200 has the same configuration as the first robot 100, but it may be a robot with a different configuration. For example, this also applies to communication between a crawler-equipped mobile robot and a humanoid service robot.

Furthermore, regarding the management server 1, there may be a physical server to implement its functions, or the programs may be stored within each robot, and their functions may be autonomously and distributedly executed on the central processing units 102 and 202 installed in the first robot 100 and the second robot 200, respectively.

If the functions of the management server 1 are located within the first robot 100 and the second robot 200, the system can be completed solely through information exchange between nearby robots by establishing an ad-hoc network between the first robot 100 and the second robot 200. This eliminates the need to install a wireless network infrastructure on the equipment. Furthermore, it reduces the communication load by decreasing the amount of data transmitted.

In contrast, if the management server 1 has an independent configuration, benefits such as simplification of the configuration of the first robot 100 and the second robot 200 can be obtained.

The shared map information is configured to be viewable by the user via the map information display unit 3 of the management server 1. The map information transmitted and received includes the acquisition time, robot ID, robot appearance information, self-position coordinates, estimation error in self-position estimation, odometry-based movement variables, recognition IDs of other robots, recognition confidence level and relative position information with the robot, lost detection flag, sensor type, and an obstacle map of the robot's surroundings acquired by the sensors.

The map information varies depending on the environment and scene in which the robot operates. For example, for a robot moving on a well-maintained indoor surface such as an office, warehouse, or factory, the self-position coordinates in the map information are three-dimensional (x, y, θ). In contrast, in unmaintained indoor environments with falling objects or uneven ground, or outdoors, six-dimensional (x, y, z, roll, pitch, yaw) coordinates are used.

Furthermore, the estimation error in the map information mentioned above refers to the estimated range of movement of a robot that failed to estimate its position. This error can be obtained using Monte Carlo position estimation calculations or the Kalman filter position calculation process.

This concludes the explanation of the system configuration of this embodiment. Next, the processing flow of this embodiment will be explained with reference to the information used.

<Processing Flow>
The processing flow of this embodiment will now be described. Figure 2 is a flowchart showing the operation control process for recovering from a position estimation failure in this embodiment. Preferably, this is processed by the information processing device 7 of the management server 1 shown in Figure 1.

Furthermore, in this embodiment, the multiple first robots 100 and second robots 200 output map information (their past movement trajectories, the estimation error range 307 in past position estimation (see Figure 4), and the amount of movement) to the management server 1 at a preferably regular interval while they are in operation.

In Figure 2, when the first robot 100 detects a failure in position estimation, as in step S200, the process transitions to step S201.

In step S200, the detection of a position estimation failure is performed by the position estimation failure determination unit 104 of the first robot 100. If the estimation error obtained by the self-position calculation unit 106 exceeds a preset threshold, it is determined that the position estimation has failed, and an alert is sent to the management server 1 to stop the robot's movement. In addition, if the position estimation fails due to a calculation error, it is also determined to be a position estimation failure.

Furthermore, in robot self-localization, mismatching of feature points can lead to the estimation result converging to an incorrect location. When the estimation error does not accurately reflect the actual error, methods that rely solely on estimation error information for lost location detection have the problem of producing false positives. Therefore, a method that determines failure in localization by comparing the estimation results from different types of localization estimation methods is desirable.

Next, in step S201, the management server 1 shares sensor information obtained from the other robots excluding the first robot 100, and attempts to recover the position of the first robot 100 using the absolute position of the first robot 100 observed by the sensors of the other robots. In step S202, it determines whether the position could be recovered using only the shared information. If the position can be recovered for reasons such as the first robot 100 being within the field of view of the sensors of the other robots, the process is completed.

Conversely, if it is determined that the first robot 100 cannot be recovered for reasons such as not being within the field of view of the other robot's sensors, the second robot 200 needs to move to a position where the lost robot can be observed in order to recover its position. This operation is called searching, and steps S203 onward represent the searching operation. Since estimation errors in the lost state are often inaccurate, in step S203, the physically possible range is set as the search range (first search range).

The method for determining the search area will be explained in detail below. Figures 3 through 8 show an example of the process for determining the search area and selecting a rescue robot.

First, as shown in Figure 3, the movement history information of the lost robot is used to determine the range in which the lost robot could physically exist. Since the estimation error is small at points where the position estimation was highly accurate, the estimation error history 302 is traced back from the current time, and points where the estimation error 301 falls below a reliable threshold are used as search reference points.

Furthermore, if we consider the position of the lost robot at that time as the search center coordinate 304, then we can say that the lost robot exists within a region 305 formed by a circle with radius r equal to the total distance 303 traveled by odometry from that point to the present time.

Furthermore, based on the estimated error range of the lost robot at the current time and the orientation between the search center coordinates and the estimated position at the current time, high-priority search areas are determined. For movement estimation, assuming a mobile robot such as a transport robot where human lifting is unlikely, wheel odometry is used to determine the movement amount.

As shown in Figure 4, the real-space search range is defined as the search range S a 311 (see Figure 7) in equation (1), where S ₁ is the region 305 where the lost robot can physically exist, S ₂ is the region 306 where the probability of the lost robot's existence is higher, and S ₃ is the estimated error range for the lost _robot 307.

Here, S _M is the drivable area 310 extracted from the map shape (see Figure 6). The definitions of areas _S1 , _S2 , and _S3 are shown below. The reliable position, which will be the search center coordinate 304, is the result of the position estimation at time _t1 of the point where the estimation error first falls below the threshold, obtained by tracing back the error history from the current time t (x _t1 , y _t1 , θ _t1 ). The radius r, which is the total distance traveled by odometry 303 from the reliable position to the current position, is obtained by equation (2).

The region 305, which is a circle with radius r centered on the trusted position (x _t1 , y _t1 ), is the region _S1 where the robot can physically exist. Furthermore, within the search range, a sector-shaped region 306, which is open at an angle φ around the vector connecting the trusted position and the current position, is set as the high-priority region _S2 , as it is a region with a high probability of existence. Similarly, the region that overlaps with the estimation error range of the robot's position at the current time obtained from the position estimation calculation is set as the high-priority region _S3 307.

The area to be searched is the intersection of the union of the set areas _S1 , _S2 , and _S3 and the drivable area _SM310 . Therefore, the search area is given by the search area _Sa311 in equation (1). The search area _Sa311 is illustrated in Figure 7. Alternatively, the management server 1 may store map coordinates where position estimation failures have occurred in the past, thereby creating areas on the map that are judged to be prone to loss, and these areas may be included in area _S2 and used as candidates for the search area.

Next, "points of interest" 308 and 309, as shown in Figure 5, are set as autonomous destination coordinates for the rescue robot to explore the search area obtained from equation (1). Points of interest 308 and 309 are placed discretely at equal intervals within the search area. The spacing between points of interest is set based on the practical recognition range of the sensors mounted on the rescue robot, so that the search area can be sufficiently sensed by the sensors.

In this embodiment, the distance d between the points of interest is defined as the distance at which the target robot can be recognized with high accuracy by the camera, and the cameras are arranged to fill the search range circle.

The key point is to determine the priority of the points of interest based on the overlap of the search range S. Point 308 in the region _S2 and _S3 of the search range _Sa is given first priority and is searched first, while point 309 in the region _S1 of the search range _Sa is given second priority.

Next, in step S204, the rescue robot to search the search area determined by the above procedure is selected. The selection method is explained in detail below.

At the current time t, the nearest robot to the search range center coordinate is extracted using the nearest neighbor method, and the rescue robot role is assigned. In this embodiment, the Euclidean distance from the search center coordinate is used to select the nearest rescue robot. However, in the case of confined spaces, the nearest point may not be the shortest in terms of actual travel distance. Therefore, it is desirable to assign the task to the robot with the shortest travel path obtained using a path planning algorithm such as a graph search algorithm.

Furthermore, to avoid impacting other robots' transport tasks, it is desirable to select rescue robots based on task availability and travel routes.

Next, in step S205, as shown in Figure 8, the coordinates of the search target initially set for the rescue robot are determined by the nearest neighbor method, with the point of interest 308, located closest to the rescue robot's coordinates, being the first-priority point of interest 312. Upon receiving the coordinates, the rescue robot generates a movement path to the point of interest and performs autonomous movement.

During this autonomous search, the rescue robot continuously determines, as in step S206, whether or not it has successfully identified the lost robot based on the sensing results.

When the first robot 100 enters the sensor's field of view via sensing, the system transitions from step S206 to step S210, correcting the absolute position of the first robot 100 and recovering from being lost.

In contrast, if the first robot 100 cannot be detected within the sensor's field of view in step S206, the process proceeds to step S207 to determine whether the entire search range (second search range) has been explored. Then, the process of limiting the search range using obstacle sensor information is executed in step S208 until the search range is completed. The method of limiting the search range using obstacle sensor information in step S208 is described in detail below.

Figure 9 shows an example of limiting the focus of a rescue robot using obstacle sensor information. As shown in Figure 9, in search operations, for the rescue robot to locate the lost robot and accurately recognize its position and orientation, it needs to physically approach the robot so that the sensors can achieve sufficient recognition accuracy.

Therefore, due to these limitations on approach distance and sensor field of view, observing each point of interest individually is inefficient. To address this, obstacle sensors with a wide observable area, such as LiDAR, are used to extract obstacles not initially recorded in the map shape obtained by SLAM. Objects whose occupied area closely matches the external shape (template) of the target robot are recognized as "objects likely to be the target robot," and only their surrounding areas are kept as points of interest. Points of interest in map areas where unoccupied areas are identified based on obstacle sensor information are then deleted, thereby limiting the search range.

Specifically, by taking the difference between the obstacle occupancy map 400 generated by the rescue robot's sensors and the known map shapes 402, new obstacles 401 that were not present during map creation are extracted. Furthermore, among the extracted unknown obstacles, obstacles 110 with an occupancy area close to the size of the target robot are selected and designated as search targets. Only the points of interest around the extracted unknown object 403 that resembles a robot are retained, and points of interest in other unoccupied areas within the obstacle map are deleted.

In addition, unknown objects 403 can be obtained by extracting objects with a high similarity to the robot's appearance using point cloud pattern matching or deep learning-based image recognition, or by performing processes such as edge extraction from the point cloud using line Hough transform.

Thus, it is desirable that the sensor information from the second robot 200 in the second search range be the position information of the first robot 100 as recognized by the second robot 200. Furthermore, it is desirable that this position information be estimated using one of the following methods: template matching using the external appearance information of the first robot 100, point cloud matching, AR marker image recognition, or deep learning-based recognition.

Next, we will explain step S209. If the target robot cannot be found even after searching the entire search area generated in step S203, the process transitions from step S207 to step S209. Specifically, in step S209, the radius r (total travel distance 303) of the search area 305 is expanded, and then the process returns to step S203 to continue the search.

If the search area is wide, the search area may be divided into several regions, and multiple robots may be used to conduct the search.

Furthermore, if the robot group fails to successfully complete the search within a certain time frame, a notification can be sent to the administrator to provide assistance.

Next, step S206 will be explained using Figure 10. Figure 10 shows an example of recognizing the first robot 100 using the sensor results of the second robot 200.

The recognition of the first robot 100 may be performed using one of the following methods: deep learning using the robot's external appearance information, LiDAR point cloud matching 501, pattern matching of image features, image recognition using AR markers 500, or recognition using sound, light, or beacons. These recognition methods require the determination of relative position and orientation. Furthermore, the recognition processing using these sensors may be performed by the sensor processing units 105 and 205 within each robot, or by the management server 1.

Furthermore, the sensor used to recognize the position of the first robot 100 may be a fixed sensor such as a surveillance camera, as shown in Figure 11; it is sufficient that the sensor's position is known.

Finally, step S210 will be explained. In step S210, the absolute position is calculated from the relative position of the recognized rescue robot and the first robot 100 using coordinate transformation, and transmitted to the first robot 100. Using AR marker recognition as an example, the position of the robot recognized by the rescue robot is converted to an absolute position using the following coordinate transformation formula and transmitted to the robot.

The coordinate transformation is performed by the coordinate transformation processing unit 44 of the management server 1. The coordinate transformation to calculate the absolute position p _A = (x, y, θ) of the first robot 100, with map origin as map, can be expressed as equations (3) and (4) using a homogeneous transformation matrix T ∈ R ^{(4 × 4)} that includes translation and rotation. B_sensor is the mounting position of the robot sensor, B_marker is the marker installation position, and B_detect_marker is the recognized marker position.

Observation of the first robot 100 (A) from the rescue robot (B) is defined as follows: (3)

Furthermore, even if the rescue robot is observed not only by the rescue robot itself, but also by the sensors of the first robot 100, its absolute position can be calculated using the following equation (4).

Observations from the first robot 100 (A) to the rescue robot (B) showed:

The absolute positions obtained are transmitted to the first robot 100 by the management server 1. The first robot 100 probabilistically fuses the multiple received absolute positions, resets its own position to the most plausible location, and recovers from position estimation failures.

In this embodiment, the invention was explained using two mobile robots, which represent the minimum configuration for carrying out the invention. However, if there are three or more robots, the sensor information of a third robot will also be used. For example, equations (3) and (4) remain valid even if B is replaced with a third robot. Furthermore, depending on the relative positions of the robots, the third robot may also function as a search robot.

In this embodiment, it is assumed that the rescue robot can always estimate its position with high accuracy. However, in reality, when entering a space with few distinguishing features, the accuracy of the rescue robot's position estimation may deteriorate. Therefore, to prevent this, it is desirable to deploy a third robot capable of observing the rescue robot.

Furthermore, rescue robots, by possessing different sensors and processing methods than lost robots—for example, GPS or ceiling feature-based position estimation—are less likely to fail to estimate their position during the search.

Furthermore, the search area is assumed to be an area where position estimation failures are likely to occur. Therefore, by setting the third robot's travel path to exclude the first and second search areas, and by planning a path that keeps other robots as far away as possible, the rate of robots being lost can be reduced. In this case, even after the search is complete, the area where lost robots with failed position estimations were found can be set to be avoided as much as possible.

This concludes the explanation of the processing flow in this embodiment.

Furthermore, the present invention is not limited to this embodiment and includes various modifications.

For example, the present invention includes a control method for an autonomous mobile robot. This control method may be performed by the mobile robot itself, by another device, or by the mobile robot and other devices working together.

Furthermore, the present invention also includes a computer program for executing a control method for a mobile robot and a medium for storing it.

Next, the effects of this embodiment will be described.

The control method for the multiple robots in the above-described embodiment is a method for controlling multiple first robots 100 and second robots 200 equipped with sensors 101 and 201. When the current position of the first robot 100 is unknown, a first search range for the first robot 100 is determined based on the first robot 100's past movement trajectory, the estimation error range 307 in the past position estimation of the first robot 100, and the amount of movement. A search command for the first search range is transmitted to a second robot 200, which is different from the first robot 100. Sensor information from the second robot 200 within the first search range is received, and the current position of the first robot 100 is estimated.

For example, in the step of estimating the current position of the first robot 100, sensor information from the second robot 200 is received within the first search range, an unknown object is extracted, the second search range is limited to the area around the unknown object, a search command for the second search range is sent to the second robot 200, sensor information from the second robot 200 within the second search range is received, and the current position of the first robot 100 is estimated.

According to the present invention, when the first robot 100 fails to estimate its own position and attempts to recover the position estimation process through a coordinated search with the second robot 200, the search range can be limited to an area with a high probability of the lost robot being present at an early stage, thereby improving search efficiency. Therefore, compared to conventional methods, the position recovery of the lost robot can be performed quickly without requiring human intervention.

Furthermore, by using a second robot 200 that is the nearest to the first or second search range, or a second robot 200 with an available task, the search efficiency can be further improved, or the decrease in robot work efficiency can be minimized.

Furthermore, since the sensor information received by the first robot 100 is from fixed sensors located in the operating areas of the second robot 200, the third robot, or the first robot 100 and the second robot 200, it is possible to obtain more information with higher accuracy when resetting its own position, thereby further improving the accuracy of recovery from position estimation failures.

Furthermore, if the first robot 100 cannot be found even after searching the second search range, the first search range can be expanded to minimize human intervention.

Furthermore, the system includes a step in which multiple first robots 100 and second robots 200 output their past movement trajectories, past position estimation error ranges 307, and movement amounts to the management server 1. This allows for pre-determining information for determining the search area in the event of a position estimation failure, enabling faster determination of a search area with a high probability of discovery.

Furthermore, by having multiple first robots 100 and second robots 200 periodically output their movement trajectories, estimated error ranges 307, and movement amounts to the management server 1, the information necessary for determining the search range becomes unbiased, enabling the determination of a search range that contributes to finding lost robots that have failed to estimate their position with a high probability.

Furthermore, the sensor information from the second robot 200 in the second search range is the position information of the first robot 100 as recognized by the second robot 200. In particular, this position information is estimated using one of the following methods: template matching using the external appearance information of the first robot 100, point cloud matching, AR marker image recognition, or deep learning recognition. This allows for reliable position estimation with a small amount of information.

Furthermore, each step is executed autonomously and in a distributed manner on an independent central processing unit 2, or on central processing units 102 and 202 mounted on the first robot 100 and the second robot 200, respectively. This reduces the equipment configuration on the mobile robot side, as well as the load on the management server and communication.

Furthermore, by setting the third robot's travel path to exclude the first and second search ranges, it is possible to suppress the occurrence of additional lost robots that fail to estimate their position, thereby more reliably avoiding a decrease in work efficiency.

<Other>
It should be noted that the present invention is not limited to the embodiments described above, and various modifications and applications are possible. The embodiments described above are explained in detail for the purpose of clearly illustrating the present invention, and are not necessarily limited to those having all the configurations described.

1...Management Server 2...Central Processing Unit 3...Map Information Display Unit 4...Memory 5...Main Memory 6...Information Transmission/Reception Unit 7...Information Processing Unit (First Search Range Determination Unit, Search Command Transmission Unit, Estimation Unit)
8...Network 41...Map information holding unit 42...Position range calculation unit 43...Search command management unit 44...Coordinate transformation processing unit 100...First robot (mobile robot)
101, 201...Sensors 102, 202...Central Processing Unit 103, 203...Information Transmission/Reception Unit 104, 204...Position Estimation Failure Determination Unit 105, 205...Sensor Processing Unit 106, 206...Self-Position Calculation Unit 107, 207...Position Information Acquisition Unit 108, 208...Road Control Unit 110...Obstacle 200...Second Robot (Mobile Robot)
301...Estimated error 302...Estimated error history 303...Total distance traveled 304...Search center coordinates 305, 306, 310, 311...Region 307...Estimated error range 308, 309, 312...Point of interest 400...Obstacle occupancy map 401...Obstacle 402...Map shape 403...Unknown object 500...AR marker 501...Point cloud matching

Claims (12)

A method for controlling multiple mobile robots equipped with sensors,
When the current position of the first robot is unknown, the first steps include determining a first search range for the first robot based on the past movement trajectory of the first robot, error information in the past position estimation of the first robot, and the amount of movement,
The steps include transmitting a search command for the first search range to a second robot, which is different from the first robot,
The step of receiving sensor information from the second robot in the first search range and estimating the current position of the first robot ,
In the step of estimating the current position of the first robot,
The system receives sensor information from the second robot within the first search range, extracts an unknown object, limits the second search range to the area around the unknown object, and transmits a search command for the second search range to the second robot.
The sensor information from the second robot in the second search range is received, and the current position of the first robot is estimated.
A method for controlling multiple mobile robots , wherein the travel path of the third robot is set excluding the first and second search ranges . In the control method for multiple mobile robots according to claim 1 ,
A method for controlling multiple mobile robots, wherein the second robot is the mobile robot closest to the first search range or the second search range, or a mobile robot that is available for a task. In the control method for multiple mobile robots according to claim 1 ,
A method for controlling multiple mobile robots, wherein the sensor information received by the first robot is information from fixed sensors located in the area where the second robot, the third robot, or the mobile robot operates. In the control method for multiple mobile robots according to claim 1 ,
A method for controlling multiple mobile robots, wherein if the first robot cannot be found even after searching the second search range, the first search range is expanded. In the control method for multiple mobile robots according to claim 1 ,
A method for controlling multiple mobile robots, comprising the step of each mobile robot outputting its past movement trajectory, error information in past position estimation, and amount of movement to a management server. In the control method for multiple mobile robots according to claim 5 ,
A method for controlling multiple mobile robots, wherein each of the multiple mobile robots periodically performs the step of outputting the movement position trajectory, error information, and movement amount to the management server. In the control method for multiple mobile robots according to claim 1 ,
A method for controlling multiple mobile robots, wherein the sensor information from the second robot in the second search range is the position information of the first robot as recognized by the second robot. In the control method for multiple mobile robots according to claim 7 ,
The position information is information estimated by one of the following methods using the appearance information of the first robot: template matching, point cloud matching, AR marker image recognition, or deep learning recognition. A method for controlling multiple mobile robots. In the control method for multiple mobile robots according to claim 1 ,
A method for controlling multiple mobile robots, wherein each of the aforementioned steps is performed autonomously and in a distributed manner on an independent computer or on a computer mounted on the mobile robot. In the control method for multiple mobile robots according to claim 1,
The method further comprises the step of positioning the third robot in a location where the second robot can be observed.
A method for controlling multiple mobile robots. A computer that controls multiple mobile robots equipped with sensors,
When the current position of the first robot is unknown, the first steps include determining a first search range for the first robot based on the past movement trajectory of the first robot, error information in the past position estimation of the first robot, and the amount of movement,
The steps include transmitting a search command for the first search range to a second robot, which is different from the first robot,
The step of receiving sensor information from the second robot in the first search range and estimating the current position of the first robot ,
In the step of estimating the current position of the first robot,
The system receives sensor information from the second robot within the first search range, extracts an unknown object, limits the second search range to the area around the unknown object, and transmits a search command for the second search range to the second robot.
The sensor information from the second robot in the second search range is received, and the current position of the first robot is estimated.
A program that sets the travel path of the third robot, excluding the first and second search ranges . A system for controlling multiple mobile robots equipped with sensors,
When the current position of the first robot is unknown, a first search range determination unit determines a first search range for the first robot based on the past movement trajectory of the first robot, error information in the past position estimation of the first robot, and the amount of movement;
A search command transmission unit transmits a search command for the first search range to a second robot, which is different from the first robot,
It includes an estimation unit that receives sensor information from the second robot in the first search range and estimates the current position of the first robot,
The estimation unit,
The system receives sensor information from the second robot within the first search range, extracts an unknown object, limits the second search range to the area around the unknown object, and transmits a search command for the second search range to the second robot.
The sensor information from the second robot in the second search range is received, and the current position of the first robot is estimated.
A multi-robot control system that sets the travel path of a third robot excluding the first and second search ranges .