JP3539838B2 - Unmanned vehicle transfer control device and unmanned vehicle transfer control method - Google Patents

Description

となり、無人車＃２の評価値は、「１０.５秒」となる。この得られた無人車＃２の評価値「１０.５」は、評価値格納メモリへ記憶される。
【０１９７】
ここで、上記括弧内の移動時間の数値は、図１３（Ａ）に示されているノードとノードとの間の距離を、対応する図１２（Ｂ）に示されているノードからノードまでの区間における移動速度で除算された数値である。
【０１９８】
次に、配車割付部２は、待機無人車リスト｛＃２、＃３｝から、待機無人車リストの２番目にある無人車＃３を示すデータ「＃３」を読み出す。これにより、配車割付部２は、無人車＃３を搬送要求番号「３」に対する割付候補とする。無人車＃３の走行の目標点は、荷物を積み込むノード２２となる。そして、配車割付部２は、無人車＃３の目標点ノード２２を示すデータと、ノード２２における作業時間を示すデータとを計画指示データメモリ６に書き込む。
【０１９９】
また、配車割付部２は、計画指示データメモリ６へ無人車＃２の目標点がないことを示すデータを書き込む。このことにより、無人車＃２の待機状態は、継続される。
【０２００】
そして、配車割付部２は、搬送実行テーブルメモリ４に示される無人車＃１、無人車＃２および無人車＃３の走行経路のデータと、無人車データメモリ７に記憶されている無人車データとに基づき、図６に示す各無人車の運行計画条件の表を作成する。次に、配車割付部２は、この図６の示す運行計画条件の表のデータを計画指示データメモリ６へ書き込む。
【０２０１】
そして、配車割付部２は、運行計画部３を起動する。これにより、運行計画部３は、計画指示データメモリ６に示される図６に示す運行計画条件の表のデータに基づき、各無人車の運行に関する図７に示す経路計画を作成する。この図７に示す経路計画において、無人車＃２は、出発点と目標点とが同一であり、荷物の搬送に関する作業が無く、待機状態となっている。
【０２０２】
次に、運行計画部３は、この経路計画に基づき、各無人車が出発点から目標点まで運行する所要時間の見積もりを行う。この見積もりは、運行計画部３により、ノードからノードまでの各経路区間毎の所要時間（以下に示される括弧内の数値：単位は秒）が積算されて求められる。
【０２０３】

となり、無人車＃３の評価値は、「１２.７秒」となる。この得られた無人車＃３の評価値「１２.７」は、評価値格納メモリへ記憶される。
【０２０４】
次に、配車割付部２は、評価値格納メモリ５に記憶される待機中の無人車＃２および無人車＃３の評価値を読み出す。そして、無人車＃２の評価値と、無人車＃３の評価値とを比較し、数値の小さい方、すなわち無人車＃２を番号「２」の搬送要求に対応させる最適無人車とする。次に、配車割付部３は、番号「２」の搬送要求に対応させる無人車＃２のデータを搬送実行テーブルメモリ４へ書き込む。
【０２０５】
これにより、搬送実行テーブルメモリ４に記憶されていた図３に示す搬送実行テーブルは、図８に示す搬送実行テーブルへ変更される。上述したように、無人車搬送制御装置１は、待機無人車のなかから、搬送要求に対応して、最も最適な無人車を選択する。また、図８の示す表において、この時点では、無人車＃２は、移動を開始しておらず、番号「３」の搬送要求は、処理されていない。さらに、無人車＃３は、ノード２２において待機中である。
【０２０６】
【発明の効果】
請求項１記載の発明によれば、停止位置である複数のノードと、前記ノード間を接続する接続路からなる走行路を、与えられた走行経路に従い走行する複数の無人車の運行を、前記ノードの配列に関する情報に基づいて制御する無人車搬送制御装置において、所定のノードにおいて、停止している待機中の複数の無人車の情報を記憶する記憶手段と、この記憶手段から運行候補の待機中の無人車を抽出する抽出手段と、この抽出手段により抽出された待機中の無人車に与えられる運行先の目標ノードの情報と、運行中の他の無人車の前記走行路における走行経路の情報と、前記ノードの配置に関する情報とに基づき、前記走行路に対応して、無人車の走行をシミュレーションして、このシミュレーション結果により最適な走行経路を探索する探索手段と、この探索された走行経路に対する評価値を求める評価手段と、この評価手段による、待機中の無人車の評価値を記憶する記憶手段と、この記憶手段に記憶される、複数の運行候補の待機中の無人車のおのおのの前記評価値に基づき、最適な評価値を有する待機中の無人車を選択する選択手段とを具備してなるため、走行中の無人車の経路を考慮し、待機中の無人車のなかから、搬送要求の目標点まで最も早く到達可能な無人車を選択するので、搬送要求に対して無駄な待ち時間の少ない効率的な配車制御ができ、所定の走行路における無人車の搬送能力を向上させる効果がある。
【０２０７】
請求項２記載の発明によれば、停止位置である複数のノードと、前記ノード間を接続する接続路からなる走行路を走行する複数の無人車の運行を前記ノードの配列に関する情報に基づいて制御する無人車搬送制御方法において、所定のノードにおいて、停止している待機中の複数の無人車の情報を記憶手段に記憶する第一のステップと、第一のステップにおいて記憶手段に記憶された複数の無人車の中から運行候補の待機中の無人車を抽出する第二のステップと、この第二のステップにおいて抽出された待機中の無人車に与えられる、運行先の目標ノードの情報と、運行中の他の無人車の前記走行路における走行経路の情報と、前記ノードの配置に関する情報とに基づき、前記走行路に対応して、無人車の走行をシミュレーションする第三のステップと、第三のステップのシミュレーション結果により、最適な走行経路を探索する第四のステップと、この第四のステップで求められる走行経路に対する評価値を求め、得られた待機中の無人車の評価値を記憶手段に記憶する第五のステップと、第五のステップにおいて、前記記憶手段に記憶される、複数の運行候補の待機中の無人車おのおのの前記評価値に基づき、最適な評価値を有する待機中の無人車を選択する第六のステップとを有するため、走行中の無人車の経路を考慮し、待機中の無人車のなかから、搬送要求の目標点まで最も早く到達可能な無人車を選択するので、搬送要求に対して無駄な待ち時間の少ない効率的な配車制御ができ、所定の走行路における無人車の搬送能力を向上させる効果がある。
【０２０８】
請求項３記載の発明によれば、所定の時刻における前記複数の無人車の確定走行経路および与えられた作業内容を記憶する計画指示記憶手段と、前記各無人車の状態を監視する第１の処理と、与えられた作業を完了した無人車が発生する度に、前記計画指示記憶手段に新たな作業を設定し、前記第１の手段および前記第２の手段を起動して走行経路を探索させる第２の処理と、該探索の結果に基づいて前記各無人車に動作指示を与える第３の処理を並列かつ周期的に行うことで、前記複数の無人車の運行を制御する運行制御手段とを具備してなるため、時々刻々と変化する各無人車の状況に基づき、作業を完了した無人車に直ちに新たな作業および動作指示を与えることができるため、無人車の搬送効率を向上させることができるという効果がある。
【図面の簡単な説明】
【図１】本発明の一実施形態による無人車搬送制御装置１の構成を示すブロック図である。
【図２】本発明の一実施形態による無人車搬送制御装置１の配車割付の動作を示すフローチャートである。
【図３】無人車の割付け前の搬送実行テーブル４に記憶されている搬送実行テーブルを示す図である。
【図４】無人車搬送制御装置１における運行計画部３に記憶される運行計画条件の表を示す図である。
【図５】運行計画条件の表のデータに基づいて作成された経路計画を示す図である。
【図６】無人車搬送制御装置１における運行計画部３に記憶される運行計画条件の表を示す図である。
【図７】運行計画条件の表のデータに基づいて作成された経路計画を示す図である。
【図８】無人車の割付け後の搬送実行テーブル４に記憶されている搬送実行テーブルを示す図である。
【図９】従来の最適経路決定装置の構成を示すブロック図である。
【図１０】梯子型走行路を示す図である。
【図１１】無人車の走行状態を示す図である。
【図１２】図１０に示す梯子型走行路の座標および無人車の走行速度のデータを示す図である。
【図１３】図１０に示す梯子型走行路のコスト計算および従来例における最短経路選択結果を示す図である。
【図１４】図４１に示す走行路１０１のコストを示す図である。
【図１５】経路計画に用いる木を示した図である。
【図１６】運行計画部３の経路計画処理を示すフローチャートである。
【図１７】動作計画処理の動作例を示す運行図である。
【図１８】図１７の運行図をモデル化したペトリネット図である。
【図１９】運行計画部３の退避経路探索処理を示すフローチャートである。
【図２０】退避経路探索処理を示す運行図である。
【図２１】運行計画部３のデッドロック把握処理を示すフローチャートである。
【図２２】デッドロック把握処理の動作例を示す運行図である。
【図２３】運行計画部３の発火順序調整処理を示すフローチャートである。
【図２４】発火順序調整処理の動作例を示す図である。
【図２５】運行計画部３の迂回経路探索処理を示すフローチャートである。
【図２６】迂回経路探索処理の動作例を示す運行図である。
【図２７】運行計画部３の退避経路探索処理を示すフローチャートである。
【図２８】退避経路探索処理の動作例を示す運行図である。
【図２９】運行計画部３の動作計画処理（メイン処理）を示すフローチャートである。
【図３０】運行計画部３の動作計画処理（メイン処理）を示すフローチャートである。
【図３１】運行計画部３の動作計画処理（メイン処理）を示すフローチャートである。
【図３２】運行計画部３の動作例を示すペトリネット図である。
【図３３】運行計画部３の動作例を示すペトリネット図である。
【図３４】運行計画部３の動作例における結果を示す図である。
【図３５】運行管理制御装置１の動作例１における経路を示す図である。
【図３６】運行管理制御装置１の動作例１における運行図である。
【図３７】運行管理制御装置１の動作例１における運行計画図である。
【図３８】運行管理制御装置１の動作例２における経路を示す図である。
【図３９】運行管理制御装置１の動作例２における運行図である。
【図４０】運行管理制御装置１の動作例１における運行計画図である。
【図４１】無人搬送システムのシステム構成図である。
【符号の説明】
１ 無人車搬送制御装置
２ 配車割付部
３ 運行計画部
４ 搬送実行テーブルメモリ
５ 評価値格納メモリ
６ 計画指示データメモリ
７ 無人車データメモリ
８ 走行路データメモリ[0001]
TECHNICAL FIELD OF THE INVENTION
The present invention allocates vehicles to waiting unmanned vehicles in an unmanned transport system such as a factory. Unmanned vehicle transport It relates to a control device.
[0002]
[Prior art]
2. Description of the Related Art With an increase in production of an automatic transport system in a factory or the like, it is necessary to efficiently operate a plurality of unmanned transport vehicles (hereinafter, “unmanned vehicles”). However, when an infinite number of unmanned vehicles are thrown into a narrow running path, interference occurs between the unmanned vehicles, causing a reduction in the transport capacity. Therefore, there is a need for unmanned vehicle control that can avoid the interference.
[0003]
FIG. 9 shows a plurality of Unmanned car FIG. 11 is a block diagram showing a configuration of an optimum route determining device for an unmanned vehicle as a conventional example that controls traveling of a vehicle (see Japanese Patent Application Laid-Open No. 06-289929). FIG. 9 is a block diagram illustrating a configuration of the optimum route determination device 100.
[0004]
The map data memory 200 stores map data of the traveling road. That is, on the road Unmanned car Are stored, and the data of the connection relations of the nodes at which can be stopped. Also, Unmanned car The data memory 201 Unmanned car Is stored. The optimum route generator 202 is provided by a control station (not shown). Unmanned car When the transfer instruction is sent to the server, the starting node and the target node are determined from the given transfer instruction.
[0005]
The static graph generation unit 204 in the graph generator 203 calculates the cost of the route from the map data stored in the map data memory 200 and writes the cost data to the search graph memory 205. Further, the angle potential calculation unit 205 Unmanned car The angular potential cost is calculated from the speed data of the unmanned vehicle stored in the data memory 201 and written to the search graph data memory 205.
[0006]
Then, based on the cost data and the angular potential cost data stored in the search graph data memory 205, the optimum route generator 202 obtains the optimum route of the unmanned vehicle corresponding to the given transfer instruction.
[0007]
Next, with reference to FIG. 10 and FIG. 11, an operation of dispatch allocation to an unmanned vehicle in the above-described conventional example will be described. FIG. 10 is a diagram illustrating the structure of a ladder-type traveling path of an unmanned vehicle. Usually, most of the unmanned vehicles have regularity like a ladder type and a square lattice type shown in FIG.
[0008]
FIG. 11 is a diagram showing a situation at the time of dispatch request on the ladder-type traveling path of FIG. For example, an unmanned vehicle # 1, an unmanned vehicle # 2, and an unmanned vehicle # 3 are operating on the traveling path. Unmanned vehicle # 1 is traveling from node 18 to node 21 in the order of route node 18 → node 19 → node 20 → node 21. Unmanned vehicle # 2 is waiting at node 16. Similarly, unmanned vehicle # 3 is waiting at node 22.
[0009]
Hereinafter, allocation of vehicles performed by the optimal route determination device 100 shown in FIG. 9 under the above-described preconditions will be described.
Here, when a loading request for a load is generated in the node 20, the optimal route determination device 100 performs a vehicle allocation for loading the load. Under the above-described conditions, in response to this loading request, either the unmanned vehicle # 2 or the unmanned vehicle # 3 in standby is allocated to loading of luggage by the optimum route determination device 100. The problem here is which of unmanned vehicle # 2 and unmanned vehicle # 3 is allocated.
[0010]
First, at the time of vehicle allocation, the optimal route determination device 100 extracts a route that minimizes the moving distance from the starting point to the target point of the unmanned vehicle # 2 waiting. Similarly, the optimal route determination device 100 extracts a route that has the shortest moving distance from the starting point to the target point of the unmanned vehicle # 3 on standby.
[0011]
Next, the optimum route determination device 100 calculates an evaluation value of the extracted route based on the moving distance from the starting point to the target point. As a result, in the unmanned vehicle # 2 and the unmanned vehicle # 3, the optimal route determination device 100 allocates the luggage loading to the one having the better evaluation value.
[0012]
There are two types of evaluation values serving as the above-described allocation criteria: an evaluation value A based on distance and an evaluation value B based on time. When the vehicle allocation is performed using the evaluation value A, the obtained result is the route of the shortest moving distance of the unmanned vehicle from the departure point to the destination. In other words, the unmanned vehicles to be allocated are the unmanned vehicles that are waiting at the position where the integrated value of the moving distance from the starting point to the target point is minimum.
[0013]
In addition, when the vehicle allocation is performed using the evaluation value B, the obtained result is a route with the shortest travel time in the time required for the unmanned vehicle to travel from the starting point to the target point. That is, the unmanned vehicle to be assigned is an unmanned vehicle waiting at a position where the integrated value of the traveling time of the unmanned vehicle from the departure point to the target point is minimized.
[0014]
Here, based on the table of FIG. 12 showing the coordinate data of each node on the ladder-type traveling road of FIG. 10, the optimum route determination device 100 creates the search graph shown in FIG. This search graph is obtained when time is used as an evaluation value in 'msec' units.
[0015]
Further, the search graph when the distance is set to the evaluation value in the unit of 'mm' is a graph in which the cost for the route in the vertical direction of the search graph in FIG. 13 is replaced with '3333' to '1000'. The search graph shown in FIG. 13 shows evaluation values that are the basis for the optimal route determination device 100 to search for the optimal unmanned vehicle route.
[0016]
In the search graph shown in FIG. 13, the optimal route determination device 100 searches for an optimal route on which unmanned vehicles operate. However, the optimum route obtained on the ladder-type traveling road shown in FIG. 10 does not differ between the evaluation value A and the evaluation value B. Therefore, the route search in the following description is performed using the evaluation value B shown in FIG.
[0017]
The optimum route determination device 100 obtains the shortest route of the unmanned vehicle # 2 as “node 16 → node 17 → node 18 → node 19 → node 20” by the above-described route search. The integrated value of the travel time of this route is “9000 (msec)”.
[0018]
Further, the optimum route determination device 100 obtains the shortest distance of the unmanned vehicle # 3 as “node 22 → node 21 → node 20”. The integrated value of the travel time of this route is “6000 (msec)”. As a result, the optimal route determination device 100 allocates the unmanned vehicle # 3 to loading of luggage.
[0019]
[Problems to be solved by the invention]
However, conventional Unmanned vehicle transport The route of the unmanned vehicle # 3 allocated by the control device overlaps with the route operated by the unmanned vehicle # 1 shown in FIG. In the case of the situation shown in FIG. 11, the unmanned vehicle # 1 during traveling is permitted to enter the nodes up to the node 21 in order to prevent collision between the unmanned vehicles.
[0020]
Therefore, the unmanned vehicle # 1 enters the node 21 to perform work. Then, the unmanned vehicle # 3 waits at the node 22 until the unmanned vehicle # 1 finishes the work and leaves the node 21.
Conversely, when the unmanned vehicle # 3 enters the node 19, the unmanned vehicle # 1 stands by at the node 18.
[0021]
As described above, since the traveling route of the unmanned vehicle # 1 and the traveling route of the unmanned vehicle # 3 compete with each other, one of the unmanned vehicle # 1 and the unmanned vehicle # 3 completes the other operation. You will have to wait until you do.
[0022]
In other words, when the allocation of the allocation is performed to the unmanned vehicle # 3 having the traveling direction opposite to that of the traveling unmanned vehicle # 1, either the unmanned vehicle # 1 or the unmanned vehicle # 3 is assigned. One waits for the other to finish. Therefore, the transfer capability of the entire unmanned vehicle used for transfer is reduced.
[0023]
The present invention has been made under such a background, and based on the route of a traveling unmanned vehicle or the like, dispatching an unmanned vehicle that can reach a target point from a waiting unmanned vehicle in the shortest time. Can be assigned Unmanned vehicle transport It is an object to provide a control device.
[0024]
[Means for Solving the Problems]
The invention according to claim 1, the operation of a plurality of unmanned vehicles traveling along a traveling path including a plurality of nodes at a stop position and a connection path connecting the nodes according to a given traveling path, Control based on information about array Unmanned vehicle transport In the control device, at a predetermined node, storage means for storing information of a plurality of stopped unmanned vehicles that are stopped, extraction means for extracting unmanned vehicles waiting for operation from the storage means, Based on the information of the target node of the operation destination given to the waiting unmanned vehicles extracted by the means, information of the traveling route of the other unmanned vehicles in operation on the traveling path, and information on the arrangement of the nodes, Searching means for simulating the driving of an unmanned vehicle corresponding to the running path, and searching for an optimum running route based on the simulation result; Evaluation means for obtaining an evaluation value for the searched traveling route; Storage means for storing the evaluation values of the unmanned vehicles on standby by the evaluation means; and optimal evaluation based on the evaluation values of the plurality of unmanned vehicles on standby of a plurality of operation candidates stored in the storage means. Selection means for selecting a waiting unmanned vehicle having a value.
[0025]
The invention according to claim 2 controls the operation of a plurality of unmanned vehicles traveling on a traveling path including a plurality of nodes at a stop position and a connection path connecting the nodes based on information on an arrangement of the nodes. In the unmanned vehicle transportation control method, at a predetermined node, a first step of storing information of a plurality of unmanned vehicles that are stopped and waiting in a storage unit, and a plurality of information stored in the storage unit in the first step. A second step of extracting a waiting unmanned vehicle of an operation candidate from the unmanned vehicles, information on a target node of an operation destination given to the waiting unmanned vehicle extracted in the second step, and operation A third step of simulating the traveling of the unmanned vehicle corresponding to the traveling path based on the information on the traveling path of the other unmanned vehicle in the traveling path and the information on the arrangement of the nodes. And a fourth step of searching for an optimal traveling route based on the simulation result of the third step, and an evaluation value for the traveling route obtained in the fourth step is obtained, and the obtained evaluation of the waiting unmanned vehicle is obtained. A fifth step of storing the value data in the storage means, and in the fifth step, an optimum optimal value stored in the storage means, based on the evaluation value of each of the plurality of operation candidate waiting unmanned vehicles. A sixth step of selecting a waiting unmanned vehicle having an evaluation value.
[0026]
According to a third aspect of the present invention, in the unmanned vehicle transport control device according to the first aspect, a plan instruction storage unit that stores a determined traveling route of the plurality of unmanned vehicles and a given work content at a predetermined time; A first process for monitoring the state of each unmanned vehicle, and each time an unmanned vehicle that has completed a given operation is generated, a new operation is set in the plan instruction storage unit, and the first unit and the second unit are set. A second process of activating the second means and searching for a traveling route, and a third process of giving an operation instruction to each of the unmanned vehicles based on a result of the search are performed in parallel and periodically, so that the plurality of processes are performed. Operation control means for controlling the operation of the unmanned vehicle.
[0027]
BEST MODE FOR CARRYING OUT THE INVENTION
Hereinafter, embodiments of the present invention will be described with reference to the drawings. FIG. 1 is according to one embodiment of the present invention. Unmanned vehicle transport FIG. 2 is a block diagram illustrating a configuration of a control device 1. In this drawing, reference numeral 2 denotes a vehicle allocation unit, which is a processing device including a CPU (central processing unit), a ROM (read-on memory), a RAM (random access memory), and the like.
[0028]
The operation planning unit 3 is configured by a CPU or the like, and determines an optimal traveling route and an operation order of each unmanned vehicle.
[0029]
Reference numeral 4 denotes a transfer execution table memory 4, which is a storage area for pooling data relating to work given to the unmanned transfer system. Reference numeral 5 denotes an evaluation value storage memory, which is used to determine the route of each unmanned vehicle planned by the operation planning unit 3 and the travel route. Unmanned car Is an area for storing a reservation sequence and the like for each node that can be stopped. The vehicle allocation unit 2 checks and refers to the data in the evaluation value storage memory 5 and outputs a traveling instruction to each unmanned vehicle.
[0030]
Reference numeral 6 denotes a plan instruction data memory, which stores data for instructing how to handle each unmanned vehicle, specifically, data of a route determination level, a target node (target point), and work time. . The operation planning unit 3 makes a route plan and an operation plan based on these data. Each data will be described below.
[0031]
Route determination level: Data indicating the determination status of the traveling route of each unmanned vehicle, and there are three types of “undetermined”, “determined to destination node”, and “determined to save destination node”.
"Undecided" indicates that the route from the current node to the destination node has not been determined, and therefore it is necessary to find the route.
[0032]
“Confirmed to destination node” means that the route from the current node to the destination node has been determined. As for the route after the destination node, there is a possibility that the route up to that point may be added by adding a save destination node in the operation planning unit 2. The save operation for determining the save destination node will be described later.
[0033]
“Determined to save destination node” means that the route from the current node to the save destination node via the target node is determined. With respect to the route after the save destination node, there is a possibility that the route to that route may be added by further adding the save destination node in the operation planning unit 3.
[0034]
Reference numeral 7 denotes a data memory relating to the state of each unmanned vehicle, and stores data relating to the state of the unmanned vehicle, such as whether the unmanned vehicle is in a standby state or an operation state.
[0035]
Reference numeral 8 denotes a travel path data memory, which stores the coordinates of each node where unmanned vehicles can be stopped on the travel path, and the data on the connection relationship, cost, and the like.
[0036]
The vehicle allocation unit 2 refers to the transfer execution table memory 4 and the unmanned vehicle data memory 7 and, when confirming the existence of an unassigned job and the existence of an unmanned vehicle having no job, plans based on the reference data. The route determination level, the target node, and the work time are set in the instruction data memory 6, and the operation planning unit 3 is started.
[0037]
Next, the operation of the operation planning unit 3 will be described.
First, the operation planning unit 3 obtains all routes connecting the departure node and the target node of each unmanned vehicle according to a route search instruction stored therein. Next, the costs of the respective routes are integrated from the costs (see FIG. 14) stored in the travel route data memory 8, and the route with the minimum cost is selected as the optimal route. However, when the route search instruction includes the direction information of the direction to be described later, the route that travels in the directed arc in the reverse direction is not selected. Similarly, when the route search instruction includes banned direction information to be described later, a route passing through the banned arc is not selected.
The route obtained by the above method and its cost are stored in its own internal storage unit. However, the route created here does not take into account the travel routes of other unmanned vehicles, and becomes an optimal route only when there is no competition of the travel routes.
[0038]
Further, the operation planning unit 3 obtains a traveling route without a reverse traveling section using a tree search method, and stores the result in its own internal storage unit.
The “tree” here has a configuration of branching downward as shown in FIG. Here, N1, N2,... Are branch points in which branch conditions are entered, and among them, the branch point N1 is a route branch point at which a branch starts. For example, if the branch point N2 is the current branch point, the branch point N1 is a parent branch point of the branch point N2, and the branch points N3 and N4 are child branch points of the branch point N2. The search is basically performed from the upper branch point to the lower branch point. If the search is not possible, the search temporarily returns to the parent branch point (hereinafter referred to as backtrack) and branches to another branch point.
[0039]
FIG. 16 is a flowchart showing a route planning process performed by the operation planning unit 3, and the following description will be made based on this diagram.
First, in step 1, the operation planning unit 3 starts a route planning process.
[0040]
In step SP2, the operation planning unit 3 obtains a traveling route of each unmanned vehicle based on a search instruction stored therein. Here, the operation planning unit 3 searches for a route by the above-described method, and stores the initial route as a result in its own internal storage unit. The search instruction includes a target node of an unmanned vehicle determined by data stored in the plan instruction data memory 6.
[0041]
In step SP3, the operation planning unit 3 empties the route branch point of the tree.
[0042]
In step SP4, the operation planning unit 3 obtains a section (reverse section) in which any two unmanned vehicles move in mutually opposite directions based on the traveling path of each unmanned vehicle stored therein. Perform for all combinations of unmanned vehicles.
[0043]
In step SP5, based on the result of step SP4, the operation planning unit 3 ends the process if there is no backward section (step SP17), and proceeds to the next step SP6 if there is a backward section. If there is no reverse section, the traveling route at that time is the final traveling route.
[0044]
In step SP6, the operation planning unit 3 adds up the costs in the reverse section of the route of each unmanned vehicle. Here, the cost of the reverse section is read from the road data memory 105. In the case where a conflict occurs in the reverse direction with a path of another plurality of unmanned vehicles in a certain reverse section, the cost is added up by the number of times of the conflict. However, when there is no other route connecting two adjacent points on the travel path, the section is not included in the reverse section.
[0045]
In step SP7, the operation planning unit 3 creates a set of competing unmanned vehicles in which codes assigned to the unmanned vehicles are arranged in descending order of the cost of the reverse traveling section.
[0046]
In step SP8, the operation planning unit 3 adds the branch point having the set of unmanned vehicles to the operation below the parent branch point. However, when this step SP8 is processed for the first time, the above-mentioned set of unmanned vehicles is set at the route branch point.
[0047]
In step SP9, the operation planning unit 3 determines an unmanned vehicle of interest from the set of competing unmanned vehicles. The unmanned vehicles of interest are sequentially selected from the first unmanned vehicle in the set of competitive unmanned vehicles arranged in descending order of cost. When there is no next unmanned vehicle, the operation planning unit 3 determines that there is no unmanned vehicle of interest.
[0048]
In step SP10, the operation planning unit 3 proceeds to the next step SP11 when there is no unmanned vehicle of interest in the processing of the previous step SP9, and branches to step SP13 when there is an unmanned vehicle of interest.
[0049]
In step SP11, the operation planning unit 3 checks whether the current branch point is located before the route branch point. If the current branch point is not the route branch point, the operation planning unit 3 proceeds to the next step SP12. If the route arrangement has failed in all of the competing unmanned vehicle sets, the entire process ends with the path arrangement failure (step SP17).
[0050]
In step SP12, the operation planning unit 3 shifts the processing of the current branch point to the parent branch point (backtrack), and returns to the processing of step SP9. At this time, the direction assigned when branching to the current branch point is canceled at this time.
[0051]
In step SP13, the operation planning unit 3 directs the reverse section of the route of the unmanned vehicle of interest in the traveling path in the direction opposite to the moving direction of the unmanned vehicle (assuming the traffic is one-way), and adds it to the direction information.
[0052]
In step SP14, the operation planning unit 3 issues a search instruction to the route search unit 110, and re-calculates the routes of all unmanned vehicles on the oriented traveling route.
[0053]
In step SP15, the operation planning unit 3 checks whether or not there is a route that is not obtained in the route search in the previous step SP14. If there is, the operation proceeds to the next step SP16, and if not, returns to step SP4. .
[0054]
In step SP16, the operation planning unit 3 cancels the direction of the traveling path performed in step SP13, and then returns to step SP9.
By the processing described above, a route of a plurality of unmanned vehicles having a small cost and having no reverse traveling section (hereinafter, a basic route) is obtained.
[0055]
The operation planning unit 3 temporally checks the movement of each unmanned vehicle based on the basic route stored in the internal storage unit, adjusts the order of movement, changes or adds the route as necessary, Plan the movement of all unmanned vehicles to the target node. The planning is performed by simulation using Petri nets. Before describing the operation planning process performed by the operation planning unit 3, various processes included in the Petri net and the operation plan will be described using specific examples.
[0056]
(1) Petri net
FIG. 17A is an operation diagram used for describing the Petri net. In FIG. 17A, unmanned vehicles # 1 and # 2 are on standby on nodes 2 and 6 of the traveling path 111, respectively. FIG. 17B is a diagram showing the departure node, the destination node, and the route to the destination node of each of the unmanned vehicles # 1 and # 2, and [] indicates the travel time between the nodes (seconds). Is shown. That is, unmanned vehicle # 1 moves from node 2 to nodes 3 and 4 in this order.
The travel time of each node is 1 second from node 2 to node 3 and 3 seconds between nodes 3 and 4. The same applies to unmanned vehicle # 2.
[0057]
FIG. 18 is a diagram in which the above-described operation diagram (FIG. 17A) is modeled by a Petri net. In this figure, P1, P2,..., P8 are node places, respectively, and correspond to the nodes 1 to 8 of the travel path 111, and indicate the occupied state of each node. In these places P1 to P8, when there is an unmanned vehicle at the corresponding node, a black token (black circle) is placed in a circle, and when a node is reserved, a white token (white circle) ) Is placed. In the initial state, since the unmanned vehicle # 1 is at the node 2 and the unmanned vehicle # 2 is at the node 6, a black token is placed in each of the places P2 and P6.
[0058]
Also, T12, T23,... Are transitions, and indicate the state of movement of the unmanned vehicle. In addition, the transition has one input arc entering it and one output arc exiting it, and these arcs connect two adjacent places. For example, the transition of the input node 5 and the output node 6 is T56, and conversely, the transition corresponding to the movement from the node 6 to the node 5 is T65. Further, when moving from the node 5 to the node 6, this transition T56 is fired to indicate that the unmanned vehicle is moving. Further, once a transition is fired, the transition continues for a finite time based on the moving time of the corresponding arc.
[0059]
The transition on the route to be moved from now on is arranged in the order of firing. This is called a scheduled transition sequence. In the case of the basic route shown in FIG. 17B, the scheduled transition sequences of unmanned vehicles # 1 and # 2 are as follows. become that way.
Transition sequence to be fired (unmanned vehicle # 1) = {T23, T34}
Transition sequence to be fired (unmanned vehicle # 2) = {T67, T73, T34}
[0060]
Next, processing related to the firing of the transition Tst will be described.
▲ 1 ▼ Conditions for enabling ignition
The transition Tst can be fired when there is a black token in the input place Ps, no black token in the output place Pt, and all preceding transitions (described later) have been fired before this.
[0061]
(2) Ignition process
When the ignitable transition Tst is ignited, the travel time from the node Ns to the node Nt is added to the current time, and set to the completion time of the unmanned vehicle together with the transition number. When working at the node Nt, the work time is further added to the completion time. Then, a white token is placed in the output place Pt.
[0062]
(3) Firing completion processing
A black token is removed from the input place Ps and a white token is removed from the output place Pt of the firing transition Tst, and the black token is placed in the place Pt. In addition, in the above-mentioned firing possible condition, when an unmanned vehicle passes through a single track section (a section without a detour), it is checked whether or not the transitions can be fired at once. For example, if the transition sequence to be fired is {T1, T2,..., Tn} and the transitions Ti, Ti + 1,..., Tj are single-line sections, the transition Ti-1 is the transition Ti-1. ,..., Tj can be fired only when all can be fired. This is to prevent the exit of another unmanned vehicle existing in the single track section from being blocked by the ignition of the transition Ti-1.
[0063]
(2) Evacuation operation plan
The evacuation operation is an operation of moving (evacuating) the unmanned vehicle in the standby state to another node when there is another unmanned vehicle in the standby state after completing the work at the destination of the unmanned vehicle that is moving. is there. FIG. 19 is a flowchart of the evacuation route search processing for finding the evacuation route, which will be described below.
[0064]
When the evacuation operation is started, in step Sa2, the operation planning unit 3 examines nodes around a certain unmanned vehicle and obtains all nodes to which the unmanned vehicle can retreat. Here, a node that can be saved is a node that satisfies the following conditions.
(1) Movement to the node is not prohibited.
(2) It is not occupied by unmanned vehicles that are not in a standby state.
[0065]
Next, in step Sa3, the operation planning unit 3 adds up basic costs such as travel time for nodes that can be evacuated. However, a node having an unmanned vehicle in a standby state is selected as little as possible, for example, by multiplying the cost by 100.
[0066]
In step Sa4, the operation planning unit 3 sets the node having the lowest cost in the result of step Sa3 as the evacuation node, and adds a section from the current node to the evacuation node to the traveling route of the unmanned vehicle.
[0067]
FIG. 20 is an operation diagram showing an example of the above-described evacuation route search. In this figure, unmanned vehicles # 1 and # 3 are on standby (no scheduled movement), and unmanned vehicle # 2 is about to move to node 3. In this case, the unmanned vehicle # 1 that interferes with the unmanned vehicle # 2 is to be evacuated. The nodes where the unmanned vehicle # 1 can evacuate are the nodes 2 and 7, and the cost of the evacuation route to both nodes is checked.
[0068]
Although the travel time to the node 2 is 1 second, since the unmanned vehicle # 3 is waiting at the node 2, the basic cost is 100, which is 100 times the travel time. Further, the travel time to the node 7 is 4, and the basic cost is also 4. Therefore, the node 7 with the small cost is selected as the evacuation node, and the nodes 3 → 7 are added to the route of the unmanned vehicle # 1.
[0069]
(3) Deadlock
If a plurality of unmanned vehicles are concentrated in a narrow area and fall into a deadlock situation where the evacuation operation described above cannot proceed on the intended route, the firing order adjustment and the detour route described later Search and evacuation route search are performed. Here, a method of grasping the state of the deadlock will be described. FIG. 21 is a flowchart showing this deadlock grasp processing, which will be described below.
[0070]
First, in step Sb2, the operation planning unit 3 selects an unmanned vehicle that has not been subjected to the following processing, and checks whether the unmanned vehicle is on standby. If it is not a waiting unmanned vehicle, the process proceeds to step Sb3, and if it is waiting, the process branches to step Sb4.
[0071]
In step Sb3, the operation planning unit 3 sets the closest unmanned vehicle on the traveling route of the target unmanned vehicle as an obstructed unmanned vehicle, and proceeds to step Sb5.
[0072]
In step Sb4, the operation planning unit 3 sets all unmanned vehicles around the unmanned vehicle of interest as unattended unmanned vehicles, and proceeds to the next step Sb5.
[0073]
In step Sb5, the operation planning unit 3 checks whether or not the above processing has been completed for all unmanned vehicles, and if there are unprocessed unmanned vehicles, returns to step Sb2.
[0074]
In step Sb6, the operation planning unit 3 selects an unmanned vehicle that is not in an appropriate standby state, and the unmanned vehicle that is in the way of the unmanned vehicle, the unmanned vehicle that is in the way that the unmanned vehicle is in the way, and so on. And find two or more loops in it. This is performed for all combinations.
[0075]
In step Sb7, the operation planning unit 3 selects a loop including the largest number of unmanned vehicles among the obtained loops as a competition loop.
[0076]
In step Sb8, when the contention loop cannot be obtained in the processing of step Sb7, the operation planning unit 3 ends this processing due to failure in grasping the deadlock (step Sb10).
[0077]
In step Sb9, the operation planning unit 3 obtains, for each unmanned vehicle in the competition loop, an adjacent node to which the unmanned vehicle can move, that is, a node having no other unmanned vehicle, and ends this processing (step Sb10).
[0078]
FIG. 22 is a diagram showing an example of a deadlock on the traveling path 101, wherein FIG. 22A shows an operation diagram in an initial state, and FIG. 22B shows an operation diagram in a deadlock state. Also, in this figure, unmanned vehicles that are not waiting are shown in a shade, and unmanned vehicles that are waiting are shown in white. Here, the moving routes of the unmanned vehicles # 1 to # 6 are as follows.
Unmanned car # 1: 6 → 5 → 4
Unmanned vehicle # 2: 5 → 4
Unmanned car # 3: 17 → 18 → 4
Unmanned car # 4: 2 → 3 → 16
Unmanned car # 5: 16 → 17
Unmanned vehicle # 6: 15 → 16 → 17
When the unmanned vehicles # 1 to # 6 move by one section from the initial route in FIG. 22A according to the above route, a deadlock state as shown in FIG. Here, the unmanned vehicles # 2 and # 5 are in the standby state, but the above-described evacuation route cannot be found.
[0079]
Then, unmanned vehicles # 1 to # 6 are checked for unintended unmanned vehicles, and a result as shown in FIG. 22C is obtained. In this figure, for example, the unmanned vehicle # 1 disturbs the unmanned vehicle # 2, and the movable free node of the unmanned vehicle # 1 at the time of deadlock is the node 6. Based on this result, when looking for an unmanned unmanned vehicle or a loop of an unattended unmanned vehicle,
Loop 1: Unmanned vehicle # 1 (5) → # 2 (4)
Loop 2: Unmanned vehicle # 3 (18) → # 2 (4)
Loop 3: Driverless car # 6 (16) → # 5 (17)
Loop 4: Unmanned vehicle # 3 (18) → # 2 (4) → # 4 (3) → # 6 (16) → # 5 (17) → # 3
Are obtained. Then, the loop 4 including the largest number of unmanned vehicles is selected as the competition loop. The parentheses indicate the current position (node) of each unmanned vehicle at the time of deadlock.
[0080]
(4) Firing order adjustment
When the deadlock described above occurs, the firing order of the transitions is adjusted first, and an attempt is made to eliminate them. In other words, it seeks whether deadlock can be avoided by changing the reservation order of the nodes. FIG. 23 is a flowchart showing the firing order adjustment processing, which will be described below.
[0081]
First, in step Sc2, the operation control unit 3 counts the number of times that other unmanned vehicles pass through the current node of each unattended unattended vehicle belonging to the competition loop in the future.
[0082]
In step Sc3, the operation control unit 3 checks whether the number of times of travel is “0”. If the number of travels is “0”, the operation control unit 3 ends the process (step Sc9). Proceed to Sc4.
[0083]
In step Sc4, the operation control unit 3 checks whether or not the number of passes of each node is a new state different from the time of the previous firing order adjustment processing. If this is the case, the process ends (step Sc9). This is because if the state becomes complicated, the firing order may be changed many times and eventually return to the previous state.
[0084]
In step Sc5, the operation control unit 3 checks whether or not the unmanned vehicle there is passing through the current node of the unmanned vehicle obstructing itself, and if not, proceeds to the next step Sc6. If so, the process ends (step Sc9). This is to check the confluence (node) because unmanned vehicles that have passed through the same section cannot be driven first afterwards.
[0085]
In step Sc6, the operation control unit 3 sets the number of times of travel, which is the result of step Sc2, as the evaluation value. However, the operation control unit 3 increases the above-described evaluation value when an unmanned vehicle obstructing a certain unmanned vehicle is in a standby state. Further, the processing of the above steps Sc2 to Sc6 is performed for all nodes where there is a non-standby unmanned vehicle belonging to the competition loop.
[0086]
In step Sc, the operation control unit 3 selects a node having the smallest evaluation value of each node obtained in step Sc6 as a competitive node.
[0087]
In step Sc8, the operation control unit 3 adds to the transition ignition control data so that the unmanned vehicle that interferes with the unmanned vehicle at the competing node passes first. The transition firing control data regulates the order of movement of the unmanned vehicles at the specific node, thereby restricting the transition firing.
[0088]
The firing order adjustment processing described above will be described with reference to the above-described operation diagram of FIG. First, the number of times of travel is determined for each of the nodes 18, 3, and 16 (FIG. 22 (b)) where the unmanned vehicles # 3, # 4, and # 6 in the competition loop are located. As a result, the node 16 is selected once and the other nodes are selected 0 times, so that the node 16 is selected as a conflicting node.
[0089]
Transitions having this node 16 as an output destination are transitions T15.16 and T3.16, and the firing order is reversed. That is, transition T3 · 16 is set as the preceding transition, and fired before transition T15 · 16. Thereby, before moving unmanned vehicle # 6 to node 16, unmanned vehicle # 4 can be moved to node 16 and unmanned vehicle # 2 waiting can be evacuated to node 3 since node 3 is vacant. As described above, by adjusting the firing order of the transition, the deadlock can be sometimes eliminated.
[0090]
FIG. 24 is a diagram showing the final result of this specific example. FIG. 24A shows the route from the departure node to the destination node of the unmanned vehicles # 1 to # 6, and FIG. Is shown. The parentheses in (a) of the same figure indicate a target node, and the path after this node is the above-described evacuation path. The node reservation sequence ((b) in the figure) shows the order in which unmanned vehicles reserve each node. In this figure, for example, node 5 is reserved in the order of unmanned vehicles # 2 and # 1. .
[0091]
(5) Planning for detour operation
If the deadlock situation cannot be resolved according to the basic route, a plan is made so that an appropriate unmanned vehicle takes a detour route. FIG. 25 is a flowchart showing the bypass route search process, which will be described below.
[0092]
First, in step Sd2, the operation control unit 3 searches for an unmovable unattended vehicle belonging to a contention loop and has a movable adjacent node. Here, if there is a movable adjacent node, the operation control unit 3 proceeds to the next step Sd3. If there is no adjacent node, the operation control unit 3 ends the process because the detour route search has failed (step Sd9).
[0093]
In step Sd3, the operation control unit 3 temporarily prohibits the following arcs from passing through the unmanned vehicle selected in the previous step.
(1) Arc from the current node of the unmanned vehicle to the next node
{Circle over (2)} Arcs that cannot move from the current node
[0094]
In step Sd4, the operation control unit 3 sets the current node as a departure node and the next node as a target node.
[0095]
In step Sd5, the operation control unit 3 performs a route search process (step Sa1) based on the settings in steps Sd3 and Sd4.
[0096]
In step Sd6, the operation control unit 3 releases the prohibition of the passage of the arc performed in step Sd3.
[0097]
In step Sd7, the operation control unit 3 checks whether or not a detour route is obtained in the route search processing in step Sd5. If there is a detour route, the process proceeds to the next step Sd8. When the search fails, this process ends (step Sd9).
[0098]
In step Sd8, the operation control unit 3 replaces the route in the target section with the detour route and ends the process (step Sd9).
[0099]
FIG. 26 is a diagram exemplifying a deadlock situation, and FIG. 26A is an operation diagram showing a deadlock state. Hereinafter, the detour route search processing described above will be described with reference to FIG.
[0100]
First, assuming that the unmanned vehicle # 1 is selected first, an attempt is made to prohibit the passage of arcs of the nodes 4 → 3, 4 → 18 and obtain a detour route. In this case, the detour route of the unmanned vehicle # 1 can be a route of nodes 5 → 6 → 20 → 19 → 18 → 17... However, since the nodes 18 → 17 go backwards with the route of the unmanned vehicle # 4, In general, this route is not selected. However, in the second trial of the operation plan (FIG. 29) to be described later, the reverse traveling section may not be prohibited and may be selected.
[0101]
Next, if a detour route search is performed with the unmanned vehicle # 2, nodes 3 → 2 → 1 → 15 → 16 are obtained. Then, this route becomes a detour route and is inserted between the routes (3 → 16) of the unmanned vehicle # 2 (FIG. 26 (b)).
[0102]
(6) Evacuation operation plan
If the detour operation cannot be performed in a deadlock situation, an appropriate unmanned vehicle temporarily retreats to another node (evacuates), gives way to another unmanned vehicle, and then moves again on the original route. FIG. 27 shows a save route search showing the save operation, which will be described below.
[0103]
First, in step Se1, the operation control unit 3 checks whether there is an adjacent node that can move for unmanned vehicles (non-standby unmanned vehicles) belonging to the competition loop and not yet arriving at the target node. The process proceeds to step Se3, and if there is no evacuation route search, the process ends with a failure in search of the escape route (step Se10).
[0104]
In step Se3, the operation control unit 3 temporarily prohibits the passage of the arc from the current node to the next node of the unmanned vehicle.
[0105]
In step Se4, the operation control unit 3 sets the current node as a departure node and all other nodes as target nodes.
[0106]
In step Se5, the operation control unit 3 performs a route search process (step Sa1) under the conditions set in steps Se3 and Se4.
[0107]
In step Se6, the operation control unit 3 selects the route with the lowest cost among all the routes obtained in step Se5, and sets the target node as a save node. However, in the selection of the evacuation node, nodes existing in the single track section are excluded.
[0108]
In step Se7, the operation control unit 3 releases the traffic prohibition performed in step Se3.
[0109]
In step Se8, if there is no evacuation node as a result of step Se6, the operation control unit 3 ends this processing due to failure in evacuation route search (step Se10). If there is an evacuation node, the operation control unit 3 proceeds to the next step Se9. Proceed to.
[0110]
In step Se9, the operation control unit 3 inserts the route from the current node to the evacuation node, and further from that to the current node (evacuation route), into the current route, and ends this processing.
[0111]
FIG. 28A is an operation diagram illustrating a deadlock situation, and the escape route search process will be described based on this diagram.
First, assuming that the unmanned vehicle # 1 is selected first, a route search is performed with nodes 4 → 3 temporarily prohibited, and the node 5 is selected as the evacuation node with the lowest cost. Here, as the movable adjacent node of the unmanned vehicle # 1, there is a node 18 in addition to the node 5, but the node 5 having a small cost (see FIG. 14) is selected as the evacuation node.
[0112]
Next, a route from the node 5 to the node 4 is searched, and the nodes 4 → 5 → 4 are determined as the escape routes. Finally, the evacuation route is inserted into the original route to obtain the route of the unmanned vehicle # 1 (nodes 4 → 5 → 4 → 3 → 2) (FIG. 28 (b)).
[0113]
(7) Operation plan
The operation planning unit 3 determines the route of all the unmanned vehicles and plans the moving order by using the various processes (1) to (6) described above. 29, 30, and 31 are flowcharts showing the operation planning process, which will be described below.
First, in step Sf1 (see FIG. 29), the operation planning unit 3 models a traveling route using a Petri net.
[0114]
In Step Sf2, the operation planning unit 3 sets 1 to the number of trials.
[0115]
In step Sf3, the operation planning unit 3 sets the route of each unmanned vehicle to the basic route obtained by the route planning.
[0116]
In step Sf4, the operation planning unit 3 checks the number of trials. If the number of trials is “1”, the operation proceeds to the next step Sf5. If the number of trials is other than “1”, the operation proceeds to step Sf6.
[0117]
In step Sf5, the operation planning unit 3 prohibits the passage of all arcs that are reverse to each of the above-described basic routes. As a result, the reverse section does not occur in the processing described below. Further, when the process returns due to the loop (1) and the number of trials becomes 2 (see FIG. 31, step Sf31), step Sf5 is not executed, and the restriction of the passage prohibition is not added in the route search process.
[0118]
In step Sf6, the operation planning unit 3 initializes the current time to 0.
[0119]
In step Sf7, the operation planning unit 3 places each unmanned vehicle at the starting point, and obtains a firing sequence from each route.
[0120]
In step Sf8, the operation planning unit 3 initializes the completion time of each unmanned vehicle to −1.
[0121]
In step Sf9, the operation planning unit 3 initializes the firing transition sequence to empty. This firing transition sequence is a sequence of transitions that actually fire, and does not always coincide with the transition sequence to be fired.
[0122]
In step Sf10, the operation planning unit 3 checks whether or not the completion time is the same as the current time for each unmanned vehicle. If the completion time is the same, the operation planning unit 3 proceeds to the next step Sf11. The process proceeds to Sf12.
[0123]
In step Sf11, the operation planning unit 3 extracts the first transition from the scheduled transitions of all unmanned vehicles that satisfy the condition of step Sf10, and performs a firing completion process.
[0124]
In step Sf12, the operation planning unit 3 checks whether or not the completion time is before the current time for each unmanned vehicle. If the completion time is before the current time, the operation planning unit 3 proceeds to the next step Sf13. In this case, the process proceeds to step Sf20.
[0125]
In step Sf13, the operation planning unit 3 checks whether or not the first transition of the transition to be fired can be fired. If it can be fired, the process proceeds to step Sf14. If not, the process proceeds to step Sf17. .
[0126]
In step Sf14, the operation planning unit 3 takes out all the transitions that can be fired in step Sf13 and performs a firing process.
[0127]
In step Sf15, the operation planning unit 3 updates the completion time by adding the travel time of the fired transition to the completion time.
[0128]
In step Sf16, the operation planning unit 3 registers (adds) the transitions for which the firing processing has been performed in step Sf14 to the corresponding firing transition sequences, and advances the processing to step Sf20.
[0129]
On the other hand, in step Sf17, the operation planning unit 3 checks whether or not there is a movable adjacent node in the unattended unmanned vehicle corresponding to the transition that cannot be fired. In other words, the operation planning unit 3 checks whether or not an unmanned vehicle obstructing the operation of a certain unmanned vehicle can be expelled to another node. As a result, when the eviction is possible, the process proceeds to the next step Sf18, and when the eviction is not possible, the process proceeds to step Sf20.
[0130]
In step Sf18, the operation planning unit 3 checks whether or not the unmanned vehicle enabled to be kicked out in step Sf17 is on standby. If the unmanned vehicle is on standby, the process proceeds to the next step Sf19. Proceeds to step Sf20.
[0131]
In step Sf19, the operation planning unit 3 performs an evacuation route process (FIG. 19, step Sa1) on the unmanned vehicles satisfying the conditions of steps Sf17 and Sf18, and obtains an evacuation route.
[0132]
In step Sf20, the operation planning unit 3 checks whether the completion time of the ignition transition corresponding to all the unmanned vehicles is before the current time, and if it is before the current time, proceeds to step 22; The process proceeds to step Sf21.
[0133]
In step Sf21, the operation planning unit 3 finds the unmanned vehicle having the most recent completion time from all the unmanned vehicles, and sets the completion time to the current time. Then, the process returns to step Sf10 (FIG. 29).
[0134]
In step Sf22, the operation planning unit 3 checks whether or not all of the unscheduled vehicle's scheduled firing sequences are empty. If the sequence is empty, the process proceeds to step Sf32 (see FIG. 31). The process proceeds to Sf23.
[0135]
In step Sf23, the operation planning unit 3 checks the state of the deadlock by the deadlock grasping process (see FIG. 21, step Sb1) described above.
[0136]
In step Sf24, the operation planning unit 3 adjusts the firing order of each transition by the above-described firing order adjustment processing (see step Sc1 in FIG. 23) based on the competition loop obtained in the processing in step Sf23.
[0137]
In step Sf25, the operation planning unit 3 checks whether or not the adjustment of the firing order in the previous step Sf has been successful. If the adjustment has failed, the operation planning unit 3 proceeds to the next step Sf26. In the case of success, the deadlock has been resolved. In this case, the process returns to step Sf6 (see FIG. 29).
[0138]
In step Sf26, the operation planning unit 3 searches for a detour route by a detour route search process (see FIG. 25, step Sd1).
[0139]
In step Sf27, the operation planning unit 3 checks whether or not the detour route search in the previous step was successful. If the search was unsuccessful, the operation planning unit 3 proceeds to the next step Sf28. If the search was successful, step Sf6 (see FIG. 29). Return processing to
[0140]
In step Sf28, the operation planning unit 3 searches for an escape route by searching for an escape route (see FIG. 27, step Se1).
[0141]
In step Sf29, the operation control unit 3 checks whether or not the evacuation route search in the previous step has succeeded. If the evacuation route search has failed, the operation control unit 3 proceeds to the next step Sf30, and if successful, proceeds to step Sf6 (see FIG. 29). Back.
[0142]
In step Sf30, the operation planning unit 3 checks the current number of trials. If the number is "1", the operation planning unit 3 proceeds to the next step Sf31. Sf34).
[0143]
In step Sf31, the operation planning unit 3 increases the number of trials to 2, and then returns the process to step Sf3 (see FIG. 29).
[0144]
In step Sf32, the operation planning unit 3 is executed when the operation plan of the unmanned vehicle is successful, and sets the current route to the final route of the unmanned vehicle.
[0145]
In step Sf33, the operation planning unit 3 creates an order of the unmanned vehicles occupying each node (node reservation sequence) based on the firing transition sequence, and ends the entire process (step Sf34).
[0146]
(8) Petri net simulation processing example
Next, a specific example of the above Petri net simulation will be described under the conditions of the operation diagram (a) and the route (b) in FIG. FIG. 32 is a Petri net diagram showing the process of simulation.
[0147]
First, in the initial state (FIG. 32A), first, a transition T23 corresponding to the unmanned vehicle # 1 and a transition T67 corresponding to the unmanned vehicle # 2 are fired by the firing process (step Sf14). Their completion times are each 1 or 2 seconds. Then, white tokens are placed in places P3 and P4.
[0148]
Next, in step Sf21, the operation planning unit 3 updates the current time to the completion time of the unmanned vehicle # 1, that is, one second, and returns the process to step Sf10. The operation planning unit 3 places the black token in the place P3 by excluding the black token from the place P2 and the white token from the place P3 by the firing completion process of step Sf11 (see FIG. 32 (b)). This state means that unmanned vehicle # 1 has arrived at node 3. Here, the transition T67 during firing is surrounded by a rectangle.
[0149]
Next, in step Sf14, the operation planning unit 3 fires the transition T34 corresponding to the unmanned vehicle # 1, and sets the completion time of the unmanned vehicle # 1 to 4 seconds in step Sf15.
[0150]
Next, in step Sf21, the operation planning unit 3 updates the current time to 3 seconds of the completion time of the unmanned vehicle # 2, and performs the firing completion process of the transition T67 in step Sf11 which returns again via step Sf10. .
[0151]
In step Sf13, the transition T73 of the unmanned vehicle # 2 is checked, but it cannot be fired because there is a black token in the output place P3. This is because driverless vehicle # 1 occupies node 3. Unmanned vehicle # 1 is currently moving from node 3 to 4, and cannot be kicked out. Therefore, the unmanned vehicle # 2 waits at the node 7. In step Sf21, the current time is updated to the completion time of the driverless vehicle # 1, that is, 4 seconds.
[0152]
In step Sf14, the ignition processing of the transition T34 is performed, and the unmanned vehicle # 1 arrives at the target node 4. Next, since the unmanned vehicle # 1 has released the node 3, the transition T73 is fired in step Sf14 (FIG. 32D).
[0153]
At the point in time when the ignition processing of the transition T73 is completed (FIG. 33A), in step Sf14, the operation planning unit 3 attempts to ignite the last transition T34 but cannot ignite. Multiply.
[0154]
By the evacuation route search process (step Sf19), the evacuation route (node 4 → 8) of the unmanned vehicle # 1 is obtained, and the corresponding transition T48 is added to the transition to be fired, and fires.
Thereafter, the simulation proceeds by the same processing. When the simulation proceeds to FIG. 33C, there is no transition to be fired, and the processing enters step Sf32.
From the firing sequence of the transition = {T23 (# 1), T67 (# 2), T34 (# 1), T73 (# 2), T48 (# 1), T34 (# 2)} By examining the preceding relations of the cars, a node reservation sequence as shown in FIG.
[0155]
From this node reservation sequence, an operation plan diagram as shown in FIG. In this figure, unmanned vehicles # 1 and # 2 correspond to solid and broken lines, respectively. Arrows indicate the movement of each unmanned vehicle, and horizontal lines indicate periods during which nodes are reserved. For example, the unmanned vehicle # 1 moves from the node 2 to the node 3 in one second, and further moves to the node 4 in three seconds. During this time, the node 3 is reserved for the unmanned vehicle # 1 from time 0 to time 4.
[0156]
Example 1 of overall operation
The operation of the operation management control device 1 (FIG. 1) in the transport path 101 shown in FIG. 41 will be described below. In the following drawings, portions corresponding to those in FIG. 41 are denoted by the same reference numerals, and description thereof will be omitted. FIG. 35A shows a starting point and a target point in this operation example.
First, the operation planning unit 3 searches for a transport route (initial route) of each of the unmanned vehicles # 1 to # 5 based on a search instruction stored therein (FIG. 35 (a)), and the result is an initial value. 36 (a) is an operation diagram showing the initial route, in which the routes of the unmanned vehicles # 1 to # 7 are indicated by a dotted line, a long one-dot chain line, and a two-dot chain line, respectively. , Dashed lines, broken lines, solid lines, and long broken lines.
[0157]
In the initial route shown in FIG. 36 (a), the operation planning unit 3 determines that the sections between the nodes 2, 3 and between the nodes 4 to 6, and between the nodes 8 to 10 are in the reverse direction. The direction of the specific section of the traveling route is determined, and the route search process is performed again. The above operation is performed until the backward section disappears, and a basic route as shown in FIGS. 35 (b) and 36 (b) is obtained. The operation planning unit 3 stores the basic route in its own storage unit.
[0158]
The operation planning unit 3 performs the above-described operation planning process (see FIGS. 29, 30, and 31) based on the basic route. During this process, the operation planning unit 3 simultaneously searches for a route such as a detour route. The departure node, the target node, and the no-go section are stored in the storage unit inside the operation planning unit 3.
By the above processing, a final route as shown in FIGS. 35 (c) and 36 (c) is obtained. In this final route, an evacuation route (node 20 → 6) of unmanned vehicle # 1 is added to the basic route (FIG. 35 (b)). FIG. 37 is an operation plan diagram showing the movement of each of the unmanned vehicles # 1 to # 7 at this time.
[0159]
Overall operation example 2:
Next, an operation example in the case where the passage between the nodes 20 and 21 of the transport path 101 is prohibited will be described. However, the current position and the destination of each of the unmanned vehicles # 1 to # 7 in this operation example are the same as those in the above-described operation example 1 (FIG. 35A). Also, in this case, since there is no bypass route other than the route connecting the nodes 6, 7, the nodes 7, 8 and the nodes 21, 22, they are not included in the reverse direction section.
[0160]
Here, the same processing as in the operation example 1 is performed. First, the route search unit 110 obtains an initial route as shown in the operation diagram of FIG. Next, the route planning unit 109 creates a basic route as shown in FIGS. 38 (b) and 39 (b). In these figures, the reverse traveling section on the initial route (FIG. 39 (a)) is eliminated.
[0161]
Then, the operation planning unit 3 creates a final route as shown in FIG. 38 (c) and FIG. 39 (c). In this final route, the evacuation route of the unmanned vehicle # 1 (nodes 20 → 6) and the evacuation route of the unmanned vehicle # 5 (nodes 8 → 22 → 23 → 24 → 10) are different from the basic route (FIG. 39B). → 9) has been added. FIG. 40 is an operation plan diagram at this time. In this diagram, the node reservations for the unmanned vehicles # 1 to # 7 are indicated by the same line types as in FIG.
[0162]
To summarize the above description, the operation planning unit 3 refers to the plan instruction data memory 6 and the unmanned vehicle data memory 7 to create a travel route for each unmanned vehicle. This creation operation is performed immediately on the unmanned vehicle on which the work has been completed, without waiting for the completion of the work on another unmanned vehicle. Therefore, when the operation planning unit 3 obtains the basic route (minimum cost route without a reverse section), there are already some routes for which traveling is determined.
[0163]
At the time of activation, the operation planning unit 3 can instruct the information of the direction setting and the traffic prohibition for a specific route as the search condition. Further, the operation planning unit 3 obtains an initial route (a route that does not consider competition between unmanned vehicles and minimizes costs) only for unmanned vehicles whose route determination level in the plan instruction data memory 6 is “undetermined”. .
[0164]
On the other hand, for an unmanned vehicle whose route determination level in the plan instruction data memory 6 is “determined to the destination node” or “determined to the evacuation destination node”, the route to the destination node or the evacuation destination node has already been determined. The route is a fixed condition.
[0165]
The operation planning unit 3 checks the temporal movement of each unmanned vehicle based on the obtained basic route, adjusts the moving order, changes or adds the route as necessary, and performs the operation of all the unmanned vehicles. Plan the most efficient operation to the target node.
[0166]
One of the operation plans is an evacuation operation. The evacuation operation is an operation of moving (evacuating) the unmanned vehicle in the standby state to another node when there is another unmanned vehicle in the standby state after completing the work at the destination of the unmanned vehicle that is moving. is there. A node that can be a save destination is a node that satisfies the following conditions.
(1) Movement to the node is not prohibited.
(2) It is not occupied by unmanned vehicles that are not in a standby state.
[0167]
The addition of the evacuation route in the operation planning unit 3 is performed, if necessary, only after reaching the destination node. Further, when the route to the save destination node is determined, a new save destination node and route are not inserted before the save destination node. That is, it is necessary to always grasp to what extent the travel route of each unmanned vehicle is determined, and the above-mentioned “route determination level” stored in the plan instruction data memory 6 is referred to.
[0168]
Another operation plan includes exclusion of the same operation route between unmanned vehicles operating in the opposite direction. The operation planning unit 3 considers the traveling route of another unmanned vehicle and sets the direction of the reverse direction of the route of the unmanned vehicle toward the target node as the direction opposite to the traveling direction of the unmanned vehicle (one-way). Then, the operation planning unit 3 recalculates the route of the unmanned vehicle as one of the direction information, and excludes the reverse route between the unmanned vehicles from the operation plan.
[0169]
Further, as another operation plan, there is a search for a detour route when a plurality of unmanned vehicles are concentrated at one place and the movement of the unmanned vehicles cannot be taken. At this time, the operation planning unit 3 searches for a route in which a suitable unmanned vehicle detours around another route, that is, bypasses a route where other unmanned vehicles are concentrated.
[0170]
According to the operation management control device 1, before the unmanned vehicles start moving, the traveling routes and traveling orders of all the unmanned vehicles can be obtained in advance in consideration of the interference between the unmanned vehicles. Even when the vehicle may interfere frequently on the traveling road, smooth movement is possible, and therefore, the transfer efficiency of the unmanned vehicle can be improved.
[0171]
In addition, the status of each unmanned vehicle is periodically monitored, and when an unmanned vehicle that has completed the work occurs, a new operation is set for the unmanned vehicle and a traveling route search is performed in consideration of the state of the other unmanned vehicles. . Therefore, under the situation of each unmanned vehicle that changes moment by moment, a new operation and operation instruction can be immediately given to the unmanned vehicle that has completed the operation, so that the transport efficiency of the unmanned vehicle can be improved.
[0172]
Next, referring to FIGS. 1 and 2, Unmanned vehicle transport The operation of the control device 1 will be described. FIG. Unmanned vehicle transport 5 is a flowchart illustrating a vehicle allocation process performed by the control device 1. Here, the allocation of vehicles indicates the following operations. When a new baggage transfer process occurs, the vehicle allocation unit 2 activates the operation control unit 3. Then, the operation control unit 2 evaluates the traveling route of the unmanned vehicle waiting at the predetermined node, and stores the data of the evaluation result in the evaluation value storage memory 5.
[0173]
Then, the vehicle allocation unit 2 selects the most suitable unmanned vehicle from the unmanned vehicles waiting at the predetermined node based on the evaluation result stored in the evaluation result storage memory 5. Hereinafter, the above-described processing will be described.
[0174]
First, in step S1, the vehicle allocation unit 2 refers to the transfer execution table 4. As a result, the vehicle allocation unit 2 reads the data of the transport request to be executed next from the transport execution table 4. Here, the data read by the vehicle allocation unit 2 is the highest priority request stored in the transport execution table 4.
[0175]
Then, the vehicle allocation unit 2 searches the unmanned vehicle data memory 7 in response to the read transport request to confirm the waiting unmanned vehicle. As a result, the vehicle allocation unit 2 extracts all unmanned vehicles on standby at the predetermined node. Then, the vehicle allocation unit 2 creates a standby unmanned vehicle data list of the extracted unmanned vehicles on standby in the storage unit in the vehicle allocation unit 2.
[0176]
Next, in step S2, the vehicle allocation unit 2 extracts unmanned vehicle data from the head of the standby unmanned vehicle data list created in step 1 and sets it as an allocation candidate for a transport request.
[0177]
The dispatch allocation unit 2 retrieves the data of the node that transfers the package, which is the first target point of the transport request, and the data of the working time required for loading the package at this node, and retrieves the unmanned unmanned vehicle. In the plan instruction data memory 6. Here, the vehicle allocation unit 2 writes data indicating that there is no target point in a part of the data list corresponding to another waiting unmanned vehicle in the waiting unmanned vehicle data list.
[0178]
Next, in step S3, the vehicle allocation unit 2 activates the operation planning unit 3. Then, the operation planning unit 3 extracts data corresponding to the waiting unmanned vehicles extracted from the waiting unmanned vehicle list from each of the travel path data memory 8, the unmanned vehicle data memory 7, and the plan instruction data memory 6, and executes the operation plan. Collect as condition data.
[0179]
Then, the operation planning unit 3 obtains all routes connecting the node at the start point and the node at the target point of the corresponding unmanned vehicle based on the operation plan conditions. As a result, the operation planning unit 3 integrates the costs of the respective routes from the costs shown in FIG. 13 stored in the travel route data memory 8 and selects the route with the minimum integrated cost as the optimal route. I do.
[0180]
In addition, the operation planning unit 3 performs the process of creating the route plan in the operation of the operation planning unit 3 described above, that is, when obtaining the above-described route, the reverse traveling section with respect to the route of the traveling unmanned vehicle and The processing for obtaining the above-described route is performed while changing the moving order of the unmanned vehicles based on the traveling time of the unmanned vehicles while considering the interference.
[0181]
Next, in step S4, the operation planning unit 3 estimates the time required for the corresponding unmanned vehicle to reach the node at the target point based on the obtained route, and integrates the estimated time.
Then, the operation planning unit 3 causes the evaluation storage memory 5 to store the largest required time among the integrated values as the evaluation value of the selected route. As a result, the vehicle allocation unit 2 can read the evaluation value of the unmanned vehicle extracted from the waiting unmanned vehicle list from the evaluation value storage memory 5.
[0182]
Next, in step S5, the vehicle allocation unit 2 reads data of the next unmanned vehicle in the standby unmanned vehicle standby list. Here, if there is no unmanned vehicle data in the waiting unmanned vehicle list, the vehicle allocation unit 2 advances the process to step S6.
[0183]
If the data of the unmanned vehicles exists in the waiting unmanned vehicle list, the vehicle allocation unit 2 returns the process to step S2.
Here, assuming that unmanned vehicle data remains in the waiting unmanned vehicle list, in step 2, the dispatch allocation unit 2 extracts the data of the next unmanned vehicle from the waiting unmanned vehicle list and sets it as an allocation candidate for the transport request.
[0184]
The dispatch allocation unit 2 retrieves the data of the node that transfers the package, which is the first target point of the transport request, and the data of the working time required for loading the package at this node, and retrieves the unmanned unmanned vehicle. In the plan instruction data memory 6.
[0185]
Here, the vehicle allocation unit 2 writes data indicating that there is no target point in a part of the data list corresponding to another waiting unmanned vehicle in the waiting unmanned vehicle data list.
[0186]
Then, the vehicle allocation unit 2 repeats the processing from step S2 to step S5 until the unmanned vehicle data in the standby unmanned vehicle list becomes empty.
Finally, the vehicle allocation unit 2 proceeds to step S6 when confirming that the waiting unmanned vehicle list is empty in step S5.
[0187]
Next, in step S6, the vehicle allocation unit 2 compares the evaluation values of the respective unmanned vehicles in the waiting unmanned vehicle list obtained from the evaluation value storage memory 5. Thereby, the vehicle allocation unit 2 determines the allocation candidate with the smallest evaluation value (the required time on the route of the unmanned vehicle) among the unmanned vehicles currently on standby as the optimal unmanned vehicle for the transportation request, and sets the transfer execution table memory 4 To be stored.
[0188]
Next, an example of an actual unmanned vehicle allocation process will be described using a ladder-type traveling path shown in FIG. In the situation of the unmanned vehicle shown in FIG. 11, it is assumed that the transfer execution table memory 4 stores data of the transfer execution table shown in FIG.
[0189]
Here, the number “1” in the column of “NO.” Is the number of the transport request assigned to the unmanned vehicle # 1. The transfer request of the number “1” indicates the flow of the work of loading the load at the node 18, transporting the loaded load from the node 18 to the node 21, and unloading the load at the node 21. And now, the unmanned vehicle # 1 is loading the luggage at the node 18 and running between the node 18 and the node 21.
[0190]
The numbers “2” and “3” in the “NO.” Column are the numbers of transport requests for which transport processing has not been assigned to unmanned vehicles. First, the vehicle allocation unit 2 selects the transport request with the number “2” as a request with a high priority. At the same time, the vehicle allocation unit 2 refers to the unmanned vehicle data memory 7 to confirm that the unmanned vehicle # 2 and the unmanned vehicle # 3 are on standby. 3｝ is created in the storage unit in the vehicle allocation unit 2.
[0191]
Next, the vehicle allocation unit 2 reads out data “# 2” indicating the unmanned vehicle # 2 at the top of the standby unmanned vehicle list from the standby unmanned vehicle list {# 2, # 3}. Accordingly, the vehicle allocation unit 2 sets the unmanned vehicle # 2 as an allocation candidate for the transport request number “2”. The target point for traveling of the unmanned vehicle # 2 is the node 20 for loading the luggage. Then, the vehicle allocation unit 2 writes the data indicating the target point node 20 of the unmanned vehicle # 2 and the data indicating the working time at the node 20 into the plan instruction data memory 6.
[0192]
Further, the vehicle allocation unit 2 writes data indicating that there is no target point of the unmanned vehicle # 3 in the plan instruction data memory 6. Thereby, the waiting state of the unmanned vehicle # 3 is continued.
[0193]
The vehicle allocation unit 2 stores the data on the travel routes of the unmanned vehicles # 1, # 2, and # 3 shown in the transfer execution table memory 4, and the unmanned vehicle data stored in the unmanned vehicle data memory 7. Based on the above, a table of the operation plan conditions of each unmanned vehicle shown in FIG. 4 is created. Next, the vehicle allocation unit 2 writes the data of the operation plan condition table shown in FIG.
[0194]
Then, the vehicle allocation unit 2 activates the operation planning unit 3. Thereby, the operation planning unit 3 creates the route plan shown in FIG. 5 relating to the operation of each unmanned vehicle based on the data of the operation plan condition table shown in FIG. 4 stored in the plan instruction data memory 6. In the route plan shown in FIG. 5, the unmanned vehicle # 3 has the same starting point and target point, has no work related to the transport of luggage, and is in a standby state.
[0195]
Next, the operation planning unit 3 estimates the time required for each unmanned vehicle to operate from the starting point to the target point based on the route plan. This estimate is obtained by the operation planning unit 3 by integrating the required time for each route section from node to node (numerical value in parentheses shown below: unit is seconds).
[0196]

And the evaluation value of the unmanned vehicle # 2 is “10.5 seconds”. The obtained evaluation value “10.5” of driverless vehicle # 2 is stored in the evaluation value storage memory.
[0197]
Here, the numerical value of the movement time in the parentheses indicates the distance between the nodes shown in FIG. 13A, and the distance between the nodes shown in FIG. It is a numerical value divided by the moving speed in the section.
[0198]
Next, the vehicle allocation unit 2 reads data “# 3” indicating the second unmanned vehicle # 3 in the standby unmanned vehicle list from the standby unmanned vehicle list {# 2, # 3}. Thereby, the vehicle allocation unit 2 sets the unmanned vehicle # 3 as an allocation candidate for the transport request number "3". The target point for traveling of the unmanned vehicle # 3 is the node 22 for loading the load. Then, the vehicle allocation unit 2 writes the data indicating the target point node 22 of the unmanned vehicle # 3 and the data indicating the working time at the node 22 into the plan instruction data memory 6.
[0199]
Further, the vehicle allocation unit 2 writes data indicating that there is no target point of the unmanned vehicle # 2 in the plan instruction data memory 6. Thereby, the waiting state of the unmanned vehicle # 2 is continued.
[0200]
The vehicle allocation unit 2 stores the data on the travel routes of the unmanned vehicles # 1, # 2, and # 3 shown in the transfer execution table memory 4, and the unmanned vehicle data stored in the unmanned vehicle data memory 7. Based on the above, a table of the operation plan conditions of each unmanned vehicle shown in FIG. 6 is created. Next, the vehicle allocation unit 2 writes the data of the operation plan condition table shown in FIG.
[0201]
Then, the vehicle allocation unit 2 activates the operation planning unit 3. Thereby, the operation planning unit 3 creates the route plan shown in FIG. 7 relating to the operation of each unmanned vehicle based on the data of the operation plan condition table shown in FIG. In the route plan shown in FIG. 7, the unmanned vehicle # 2 has the same starting point and target point, has no work related to the transport of the load, and is in a standby state.
[0202]
Next, the operation planning unit 3 estimates the time required for each unmanned vehicle to operate from the starting point to the target point based on the route plan. This estimate is obtained by the operation planning unit 3 by integrating the required time for each route section from node to node (numerical value in parentheses shown below: unit is seconds).
[0203]

And the evaluation value of driverless vehicle # 3 is “12.7 seconds”. The obtained evaluation value “12.7” of driverless vehicle # 3 is stored in the evaluation value storage memory.
[0204]
Next, the vehicle allocation unit 2 reads the evaluation values of the waiting unmanned vehicles # 2 and # 3 stored in the evaluation value storage memory 5. Then, the evaluation value of the unmanned vehicle # 2 is compared with the evaluation value of the unmanned vehicle # 3, and the smaller numerical value, that is, the unmanned vehicle # 2 is determined as the optimum unmanned vehicle corresponding to the transport request of the number "2". Next, the vehicle allocation unit 3 writes data of the unmanned vehicle # 2 corresponding to the transport request of the number “2” into the transport execution table memory 4.
[0205]
Thereby, the transfer execution table shown in FIG. 3 stored in the transfer execution table memory 4 is changed to the transfer execution table shown in FIG. As mentioned above, Unmanned vehicle transport The control device 1 selects the most optimal unmanned vehicle from the waiting unmanned vehicles according to the transport request. Also, in the table shown in FIG. 8, at this point, the unmanned vehicle # 2 has not started moving, and the transport request of the number “3” has not been processed. Further, unmanned vehicle # 3 is waiting at node 22.
[0206]
【The invention's effect】
According to the first aspect of the present invention, the operation of a plurality of unmanned vehicles traveling on a traveling path including a plurality of nodes at a stop position and a connection path connecting the nodes according to a given traveling path, Control based on information about array of nodes Unmanned vehicle transport In the control device, at a predetermined node, storage means for storing information of a plurality of stopped unmanned vehicles that are stopped, extraction means for extracting unmanned vehicles waiting for operation from the storage means, Based on the information of the target node of the operation destination given to the waiting unmanned vehicles extracted by the means, information of the traveling route of the other unmanned vehicles in operation on the traveling path, and information on the arrangement of the nodes, Search means for simulating the traveling of an unmanned vehicle corresponding to the travel path, searching for an optimal travel path based on the simulation result, evaluation means for obtaining an evaluation value for the searched travel path, and evaluation means Storage means for storing the evaluation value of the waiting unmanned vehicle according to the above, and a plurality of operation candidates based on the evaluation value of each of the waiting unmanned vehicles stored in the storage means. And selecting means for selecting a waiting unmanned vehicle having an optimum evaluation value, taking into account the route of the traveling unmanned vehicle, and selecting a target of the transfer request from among the waiting unmanned vehicles. Since an unmanned vehicle that can reach the point at the earliest point is selected, efficient dispatch control can be performed with less useless waiting time for a transfer request, and there is an effect of improving the transfer capability of the unmanned vehicle on a predetermined traveling path.
[0207]
According to the invention described in claim 2, the operation of the plurality of unmanned vehicles traveling on the traveling path including the plurality of nodes at the stop positions and the connection paths connecting the nodes is performed based on the information on the arrangement of the nodes. In the unmanned vehicle transport control method for controlling, in a predetermined node, a first step of storing information on a plurality of stopped unmanned vehicles in a standby state in a storage unit, and storing the information in the storage unit in the first step Operation candidates from multiple unmanned vehicles Waiting The second step of extracting unmanned vehicles and the second step extracted in this second step Waiting Given to the unmanned vehicle, information on the target node of the operation destination, information on the traveling route of the other traveling unmanned vehicle on the traveling route, and information on the arrangement of the node, based on the traveling route, A third step of simulating the traveling of the unmanned vehicle, a fourth step of searching for an optimal traveling route based on the simulation result of the third step, and an evaluation value for the traveling route obtained in the fourth step. Asked, obtained Evaluation value of unmanned vehicles waiting A fifth step of storing in the storage unit, and in the fifth step, a plurality of operation candidates stored in the storage unit. Waiting unmanned vehicles Have an optimal evaluation value based on the evaluation value of each Waiting And the sixth step of selecting an unmanned vehicle.In consideration of the route of the unmanned vehicle that is traveling, from among the unmanned vehicles that are waiting, select the unmanned vehicle that can reach the target point of the transfer request earliest. Therefore, efficient vehicle allocation control with less wasteful waiting time for a transportation request can be performed, and there is an effect of improving the transportation capability of unmanned vehicles on a predetermined traveling path.
[0208]
According to the third aspect of the present invention, plan instruction storage means for storing the determined traveling routes of the plurality of unmanned vehicles and the given work content at a predetermined time, and the first monitoring means for monitoring the state of each of the unmanned vehicles. Each time an unmanned vehicle that completes the process and the given work occurs, a new work is set in the plan instruction storage means, and the first means and the second means are activated to search for a travel route. Operation control means for controlling the operation of the plurality of unmanned vehicles by performing, in parallel and periodically, a second process to perform the operation and a third process for giving an operation instruction to each of the unmanned vehicles based on the result of the search. Therefore, based on the situation of each unmanned vehicle that changes moment by moment, a new operation and operation instruction can be immediately given to the unmanned vehicle that has completed the operation, thereby improving the transport efficiency of the unmanned vehicle. The effect of being able to A.
[Brief description of the drawings]
FIG. 1 according to one embodiment of the present invention. Unmanned vehicle transport FIG. 2 is a block diagram illustrating a configuration of a control device 1.
FIG. 2 according to one embodiment of the invention. Unmanned vehicle transport 4 is a flowchart illustrating an operation of the vehicle allocation by the control device 1.
FIG. 3 is a diagram showing a transfer execution table stored in a transfer execution table 4 before the unmanned vehicles are allocated.
FIG. 4 Unmanned vehicle transport It is a figure showing a table of an operation plan condition memorized by operation plan part 3 in control device 1.
FIG. 5 is a diagram showing a route plan created based on data of a table of operation plan conditions.
FIG. 6 Unmanned vehicle transport It is a figure showing a table of an operation plan condition memorized by operation plan part 3 in control device 1.
FIG. 7 is a diagram showing a route plan created based on data of a table of operation plan conditions.
FIG. 8 is a diagram showing a transfer execution table stored in the transfer execution table 4 after the unmanned vehicles are allocated.
FIG. 9 is a block diagram showing a configuration of a conventional optimum route determination device.
FIG. 10 is a view showing a ladder-type traveling path.
FIG. 11 is a diagram showing a traveling state of an unmanned vehicle.
FIG. 12 is a diagram showing coordinates of a ladder-type traveling road shown in FIG. 10 and data of a traveling speed of an unmanned vehicle.
13 is a diagram showing the cost calculation of the ladder-type traveling road shown in FIG. 10 and the result of the shortest path selection in the conventional example.
FIG. 14 is a diagram showing a cost of a traveling path 101 shown in FIG. 41.
FIG. 15 is a diagram showing a tree used for route planning.
FIG. 16 is a flowchart showing a route planning process of the operation planning unit 3;
FIG. 17 is an operation diagram illustrating an operation example of an operation planning process.
FIG. 18 is a Petri net diagram obtained by modeling the operation diagram of FIG. 17;
FIG. 19 is a flowchart showing an evacuation route search process of the operation planning unit 3;
FIG. 20 is an operation diagram illustrating an evacuation route search process.
FIG. 21 is a flowchart showing deadlock grasp processing of the operation planning unit 3;
FIG. 22 is an operation diagram showing an operation example of deadlock grasp processing.
FIG. 23 is a flowchart showing a firing order adjustment process of the operation planning unit 3.
FIG. 24 is a diagram illustrating an operation example of a firing order adjustment process.
FIG. 25 is a flowchart showing a detour route search process of the operation planning unit 3;
FIG. 26 is an operation diagram illustrating an operation example of a detour route search process.
FIG. 27 is a flowchart showing an evacuation route search process of the operation planning unit 3;
FIG. 28 is an operation diagram illustrating an operation example of an evacuation route search process.
FIG. 29 is a flowchart showing an operation planning process (main process) of the operation planning unit 3;
FIG. 30 is a flowchart showing an operation planning process (main process) of the operation planning unit 3;
FIG. 31 is a flowchart showing an operation planning process (main process) of the operation planning unit 3;
32 is a Petri net diagram showing an operation example of the operation planning unit 3. FIG.
FIG. 33 is a Petri net diagram showing an operation example of the operation planning unit 3;
FIG. 34 is a diagram illustrating a result in an operation example of the operation planning unit 3;
FIG. 35 is a diagram showing a route in an operation example 1 of the operation management control device 1.
36 is an operation diagram in an operation example 1 of the operation management control device 1. FIG.
FIG. 37 is an operation plan diagram in an operation example 1 of the operation management control device 1.
38 is a diagram showing a route in an operation example 2 of the operation management control device 1. FIG.
39 is an operation diagram in an operation example 2 of the operation management control device 1. FIG.
40 is an operation plan diagram in an operation example 1 of the operation management control device 1. FIG.
FIG. 41 is a system configuration diagram of an unmanned transport system.
[Explanation of symbols]
1 Unmanned vehicle transport Control device
2 Vehicle allocation section
3 Operation Planning Department
4 Transfer execution table memory
5 Evaluation value storage memory
6 Planning instruction data memory
7 Unmanned vehicle data memory
8 Track data memory

Claims (3)

Controlling the operation of a plurality of unmanned vehicles traveling along a given traveling route on a traveling path composed of a plurality of nodes at a stop position and a connection path connecting the nodes based on information on the arrangement of the nodes. In unmanned vehicle transfer control device,
In a predetermined node, storage means for storing information of a plurality of unmanned vehicles that are stopped and waiting,
Extracting means for extracting the waiting unmanned vehicles of the operation candidates from the storage means;
The information of the target node of the operation destination given to the waiting unmanned vehicles extracted by the extraction means, the information of the traveling route of the other unmanned vehicles in operation on the traveling path, and the information on the arrangement of the nodes are included in the information. Searching means for simulating the traveling of the unmanned vehicle based on the traveling path and searching for an optimal traveling route based on the simulation result;
Evaluation means for obtaining an evaluation value for the searched traveling route;
Storage means for storing the evaluation value of the waiting unmanned vehicle by the evaluation means;
Selecting means for selecting a waiting unmanned vehicle having an optimal evaluation value based on the evaluation value of each of a plurality of operation candidate waiting unmanned vehicles stored in the storage means;
An unmanned vehicle transfer control device characterized by comprising: In the unmanned vehicle transport control method for controlling the operation of the plurality of unmanned vehicles traveling on the traveling path including the plurality of nodes that are the stop positions and the connection paths connecting the nodes based on information on the arrangement of the nodes,
In a predetermined node, a first step of storing information of a plurality of stopped unmanned vehicles in a standby state in storage means,
A second step of extracting a waiting unmanned vehicle of an operation candidate from the plurality of unmanned vehicles stored in the storage means in the first step;
The information on the target node of the operation destination, the information on the traveling route of the other unmanned vehicles in operation on the traveling path, and the arrangement of the nodes are given to the unmanned vehicles on standby extracted in the second step. A third step of simulating the traveling of the unmanned vehicle based on the information and corresponding to the traveling path;
A fourth step of searching for an optimal traveling route based on the simulation result of the third step;
A fifth step of obtaining an evaluation value for the traveling route obtained in the fourth step, and storing data of the obtained evaluation value of the waiting unmanned vehicle in storage means;
In a fifth step, a sixth step of selecting a waiting unmanned vehicle having an optimum evaluation value based on the evaluation value of each of the plurality of waiting unmanned vehicles of the operation candidates stored in the storage means. Steps and
An unmanned vehicle transfer control method, comprising: Plan instruction storage means for storing the determined traveling routes of the plurality of unmanned vehicles and the given work content at a predetermined time;
A first process for monitoring the state of each of the unmanned vehicles, and each time an unmanned vehicle that has completed a given operation is generated, a new operation is set in the plan instruction storage unit, and the first unit and the first unit The second process for activating the second means to search for a traveling route and the third process for giving an operation instruction to each of the unmanned vehicles based on the result of the search are performed in parallel and periodically, so that The unmanned vehicle transportation control device according to claim 1, further comprising an operation control unit that controls operation of the plurality of unmanned vehicles.

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