CN110162058B

CN110162058B - AGV planning method and device

Info

Publication number: CN110162058B
Application number: CN201910478011.3A
Authority: CN
Inventors: 马飞; 林育阳; 陈思锐; 李沐阳
Original assignee: Xian Jiaotong Liverpool University
Current assignee: Xian Jiaotong Liverpool University
Priority date: 2019-06-03
Filing date: 2019-06-03
Publication date: 2022-04-05
Anticipated expiration: 2039-06-03
Also published as: CN110162058A

Abstract

The application relates to an AGV planning method and device, belonging to the technical field of automatic control, wherein the method comprises the following steps: acquiring a topological structure of an AGV drivable route, wherein the topological structure comprises at least two nodes and drivable routes among different nodes; acquiring initial state data of each AGV, wherein the initial state data comprises an initial position, a movement speed and an equipment identifier of each AGV; acquiring running task data of the AGV, wherein the running task data comprises a running starting point and a running finishing point of at least one running task; planning the AGV according to the topological structure, the initial state data and the driving task data based on the principle that the time for completing the driving task is shortest; the problem of low execution efficiency of the driving task caused by generation of the driving route of the AGV based on the shortest path principle can be solved; since the travel route of the AGV can be generated based on the principle that the travel task completion time is shortest, the execution efficiency of the travel task can be improved.

Description

AGV planning method and device

Technical Field

The application relates to an AGV planning method and an AGV planning device, and belongs to the technical field of automatic control.

Background

An Automated Guided Vehicle (AGV) is a transport Vehicle equipped with an electromagnetic or optical automatic guide device, which can travel along a planned guide path and has safety protection and various transfer functions. The AGV system is widely applied to the fields of industry, military, transportation, electronics and the like.

Before the AGV runs, the driving route of the AGV needs to be planned according to the topological map. The topological map is an abstract map which keeps the relative position relationship between points and lines correct and does not necessarily keep the shape, the area, the distance and the direction of the map correct; the path planning means that the AGV searches for a collision-free path from the start node to the target node according to a certain standard in an environment with obstacles.

The conventional AGV planning method is used for generating a driving route of the AGV based on a shortest path principle. However, in the AGV system, the travel speed and the time for executing the travel task may be different for each AGV, and in this case, even if the travel path of the AGV is shortest, the shortest travel time of the AGV may not be guaranteed, thereby reducing the execution efficiency of the travel task.

Disclosure of Invention

The application provides an AGV planning method and device, which can solve the problem that the execution efficiency of a driving task is low due to the fact that a driving route of the AGV is generated based on a shortest path principle. The application provides the following technical scheme:

in a first aspect, a method for AGV planning is provided, where the method includes:

acquiring a topological structure of an AGV drivable route, wherein the topological structure comprises at least two nodes and drivable routes among different nodes;

acquiring initial state data of each AGV, wherein the initial state data comprises an initial position, a movement speed and an equipment identifier of each AGV;

acquiring running task data of an AGV, wherein the running task data comprises a running starting point and a running ending point of at least one running task;

and planning the AGV according to the topological structure, the initial state data and the driving task data based on the principle that the time for completing the driving task is shortest.

Optionally, the planning an AGV according to the topology, the initial state data, and the travel task data based on the principle that the travel task completion time is shortest includes:

for each driving task in the driving task data, determining the driving time between different nodes according to the movement speed of a target AGV to execute the driving task and the drivable route between the different nodes;

obtaining the shortest running time of a running starting point of the running task reaching each node in a first set, wherein the first set is used for storing the nodes of which the shortest running time is not determined;

moving the first node corresponding to the shortest travel time in the first set into a second set, wherein the second set is used for storing the node with the determined shortest travel time;

acquiring a sum of the shortest travel time of the first node to reach each node in the first set and the travel time of the travel starting point to reach the first node;

determining whether the sum is greater than or equal to a shortest travel time for the travel start to reach each node in the first set;

when the sum is larger than or equal to the shortest driving time of the driving starting point to reach each node in the first set, updating the first node into a second node; the second node is a node corresponding to the shortest driving time when the driving starting point reaches each node in the first set; the step of obtaining the shortest running time of the running starting point of the running task reaching each node in the first set is executed again until the first set is empty;

and planning the AGV according to the running time corresponding to the path formed by each node in the second set and the waiting time required before the target AGV executes the running task.

Optionally, the planning the AGVs according to the travel time corresponding to the path formed by each node in the second set and the waiting time required by the target AGV before executing the travel task includes:

and determining a target AGV with the shortest sum of the waiting time required before the running task is executed and the running time as the AGV for executing the running task to obtain the AGV running route.

Optionally, the speed of movement of the AGV includes a straight travel speed and a turning speed of the AGV.

Optionally, the obtaining the topology of the AGV drivable path includes:

displaying a topology input interface;

and receiving a topology input operation acted on the topology input interface to obtain the topology structure.

Optionally, the acquiring the travel task data of the AGV includes:

displaying a task input interface;

receiving a starting point input operation and an end point input operation acting on the task input interface;

and generating a running task according to the running starting point indicated by the starting point input operation and the running end point indicated by the end point input operation, and obtaining the running task data.

Optionally, after the AGV is planned according to the topology, the initial state data, and the travel task data based on the principle that the travel task completion time is shortest, the method further includes:

and displaying the planned AGV driving route, the waiting time of each node in the corresponding driving route of each AGV and the theoretical time for reaching each node.

Optionally, before planning the AGV according to the topology, the initial state data, and the travel task data based on the principle that the travel task completion time is shortest, the method further includes:

determining whether the topology is accurate;

and displaying a topological structure correction prompt when the topological structure is inaccurate, wherein the topological structure correction prompt is used for prompting a user to correct the topological structure.

In a second aspect, an AGV planning apparatus is provided, the apparatus comprising:

the device comprises:

the AGV comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a topological structure of the drivable route of the AGV, and the topological structure comprises at least two nodes and the drivable route between different nodes;

the second acquisition module is used for acquiring initial state data of each AGV, wherein the initial state data comprises an initial position, a movement speed and an equipment identifier of each AGV;

the third acquisition module is used for acquiring the running task data of the AGV, wherein the running task data comprises a running starting point and a running finishing point of at least one running task;

and the route planning module is used for planning the AGV according to the topological structure, the initial state data and the driving task data on the basis of the principle that the time for completing the driving task is shortest.

Optionally, the route planning module is configured to:

The beneficial effect of this application lies in: acquiring a topological structure of an AGV drivable route, wherein the topological structure comprises at least two nodes and drivable routes among different nodes; acquiring initial state data of each AGV, wherein the initial state data comprises an initial position, a movement speed and an equipment identifier of each AGV; acquiring running task data of the AGV, wherein the running task data comprises a running starting point and a running finishing point of at least one running task; planning the AGV according to the topological structure, the initial state data and the driving task data based on the principle that the time for completing the driving task is shortest; the problem of low execution efficiency of the driving task caused by generation of the driving route of the AGV based on the shortest path principle can be solved; since the travel route of the AGV can be generated based on the principle that the travel task completion time is shortest, the execution efficiency of the travel task can be improved.

The foregoing description is only an overview of the technical solutions of the present application, and in order to make the technical solutions of the present application more clear and clear, and to implement the technical solutions according to the content of the description, the following detailed description is made with reference to the preferred embodiments of the present application and the accompanying drawings.

Drawings

FIG. 1 is a flow chart of an AGV planning method according to one embodiment of the present application;

FIG. 2 is a schematic diagram of a topology input interface provided by one embodiment of the present application;

FIG. 3 is a schematic view of a status input interface provided by one embodiment of the present application;

FIG. 4 is a schematic diagram of a task input interface provided by one embodiment of the present application;

FIG. 5 is a schematic view of a task input interface provided by another embodiment of the present application;

FIG. 6 is a schematic diagram of a topology provided by one embodiment of the present application;

FIG. 7 is a schematic diagram showing an AGV travel route according to one embodiment of the present application;

FIG. 8 is a block diagram of an AGV planning apparatus according to one embodiment of the present application.

Detailed Description

The following detailed description of embodiments of the present application will be described in conjunction with the accompanying drawings and examples. The following examples are intended to illustrate the present application but are not intended to limit the scope of the present application.

Optionally, in the present application, a control end of the AGV is taken as an execution subject of each step in each embodiment as an example for description, where the control end may be an electronic device with processing capability, such as a computer, a mobile phone, a tablet computer, and a server, and the present embodiment does not limit the type of the device in the control end.

FIG. 1 is a flow chart of an AGV planning method according to one embodiment of the present application. The method at least comprises the following steps:

step 101, a topological structure of an AGV drivable route is obtained, wherein the topological structure comprises at least two nodes and drivable routes between different nodes.

In one example, the topology is obtained by the control end based on a human-computer interaction technology. At the moment, the control end displays a topology input interface; and receiving a topology input operation acted on the topology input interface to obtain a topology structure.

Wherein, the topology input operation can be the operation of inputting the path length between nodes; alternatively, the operation of mapping nodes and labeling path lengths between nodes may also be performed, such as: dragging the mouse to draw the nodes, and marking the path length between the nodes, wherein the embodiment does not limit the operation type of the topology input operation.

Referring to the topology input interface shown in fig. 2, the first row of the topology input interface comprises two integer data n and m, n representing the number of nodes of the topology and m representing the number of paths in the topology. In FIG. 2, n is 36 and m is 37. The first line is followed by m lines, each line of data represents a path, each line of data comprises two integer data s, e and one floating point data w, wherein s represents a starting point of the corresponding path, e represents an end point of the corresponding path, and w represents a length of the corresponding path.

In another example, the topology is stored in the control end and read by the control end.

Step 102, obtaining initial state data of each AGV, wherein the initial state data comprises an initial position, a movement speed and a device identification of each AGV.

The initial position is a position where the AGV does not start executing the travel task.

Optionally, the speed of movement of the AGV includes a straight travel speed and a turning speed of the AGV. The straight-going speed can be the average speed of the AGV in straight-going; the turn speed may be the average speed of the AGV as it travels a curve.

The device identifier may be a vehicle identification code, a serial number, a device number, and the like of the AGV, and the type of the device identifier is not limited in this embodiment.

In one example, the initial state data is derived by the control end based on human-computer interaction techniques. At the moment, the control end displays a state input interface; and receiving state input operation acted on the state input interface to obtain initial state data.

Wherein the state input operation may be an operation of inputting an initial position, a movement speed, and a device identification.

Referring to the status input interface shown in FIG. 3, the first row of the status input interface includes a profiling data n, where n represents the number of AGVs and is 10 in FIG. 3. N rows after the first row, each row representing the initial position, speed of movement, and equipment identification of one AGV. Each of the n rows includes two integer data s, v and one character string i. Wherein s represents the initial position of the corresponding AGV, v represents the movement speed of the corresponding AGV, and i represents the equipment identifier of the corresponding AGV.

In another example, the initial state data is stored in the control terminal and read by the control terminal.

Step 103, acquiring the driving task data of the AGV, wherein the driving task data comprises a driving starting point and a driving end point of at least one driving task.

Optionally, the travel task data includes static task data and dynamic task data. The static task data refers to task data acquired by each AGV before executing a travel task. Dynamic task data refers to task data that is acquired after at least one AGV begins to perform a travel task.

In one example, acquiring travel task data for an AGV includes: displaying a task input interface; receiving a starting point input operation and an end point input operation acting on a task input interface; and generating a running task according to the running starting point indicated by the starting point input operation and the running end point indicated by the end point input operation, and obtaining running task data.

Referring to the task input interface shown in fig. 4, the first row of the task input interface includes a shaping data n, n represents the number of travel tasks, and n is 30 in fig. 4. N rows following the first row, each row representing a travel task. Each of the n rows comprises two integer data s, e. Where s denotes a travel start point corresponding to the travel task, and e denotes a travel end point corresponding to the travel task.

Referring to the task input interface shown in fig. 5, the task input interface includes a start point input control 51, an end point input control 52, and a task generation control 53. Receiving a start point input operation acting on the start point input control 51, receiving an end point input operation acting on the end point input control 52; then, upon receiving a task generation operation to act on the task generation control 53, a travel task is generated, and travel task data is obtained.

In another example, the running task data is stored in the control terminal and read by the control terminal.

And step 104, planning the AGV according to the topological structure, the initial state data and the driving task data based on the principle that the time for completing the driving task is shortest.

Optionally, based on the principle that the travel task completion time is shortest, the AGV is planned according to the topology, the initial state data and the travel task data, and the method at least includes the following steps:

step 1, for each driving task in the driving task data, determining the driving time between different nodes according to the movement speed of a target AGV to execute the driving task and the drivable route between different nodes.

Assume that the drivable path in the topology is as shown in fig. 6, which includes 5 nodes A, B, C, D and E. Wherein, A is respectively connected with B, D, B is respectively connected with A, C and D, C is respectively connected with B, D, E, D is respectively connected with A, B, C, E, and E is respectively connected with C, D.

For each AGV, the control end calculates the running time between different nodes according to the path length between the nodes (refer to the path length marked above each path) and the movement speed of the AGV (comprising the straight speed and the turning speed).

And 2, acquiring the shortest running time of the running starting point of the running task to reach each node in the first set.

In this embodiment, two sets, a first set and a second set, are preset at the control end. The first set is used for storing nodes of which the shortest driving time is not determined; the second set is used to store the nodes for which the shortest travel time has been determined. The initialization first set includes all nodes except the travel start point, and the initialization second set includes only the travel start point.

Assuming that the driving start point is a, the first set is initialized as: { B, C, D, E }; the second set is { A }.

The control end acquires the shortest travel time of A to each node in the first set, namely the shortest travel time of A to B, C, D and E respectively. At this time, since a arrives at C and E without a travelable route, the travel time for a to arrive at C and E is ∞. And if the travel time from A to B is less than the travel time from A to D, the travel time from A to B is the shortest travel time.

And 3, moving the first node corresponding to the shortest travel time in the first set into the second set.

Assuming that the travel time from a to B in fig. 6 is the shortest travel time, point B is moved into the second set, where the first set is: { C, D, E }; the second set is { A, B }.

And 4, acquiring the sum of the shortest running time of the first node reaching each node in the first set and the running time of the running starting point reaching the target node.

Based on the above example, the control end acquires the sum of the travel time from B to C + the travel time from a to B, the sum of the travel time from B to D + the travel time from a to B, and the sum of the travel time from B to E + the travel time from a to B.

And 5, determining whether the sum value is larger than or equal to the shortest driving time of the driving starting point to reach each node in the first set.

And the control end acquires the shortest running time of the running starting point reaching each node in the first set. Based on the above example, the control end acquires the travel time from a to C (travel time from a to C via D), the travel time from a to D, and the travel time from a to E (travel time from a to E via D), and then determines the minimum value from the respective travel times, resulting in the shortest travel time. And finally, the control end compares the sum value in the step 4 with the shortest running time, and determines the sum value and the shortest running time.

Optionally, when the sum is greater than or equal to the shortest travel time from the travel starting point to each node in the first set, performing step 6; and when the sum value is smaller than the shortest driving time of the driving starting point reaching each node in the first set, executing the step 2 again until the first set is empty, and stopping.

Step 6, updating the first node into a second node, wherein the second node is a node corresponding to the shortest driving time when the driving starting point reaches each node in the first set; and executing the step of obtaining the shortest running time of the running starting point of the running task reaching each node in the first set again until the first set is empty.

Assuming that the shortest travel time determined in step 5 is the travel time from a to D, and the shortest travel time is greater than or equal to the sum value in step 4, updating B in the second set to D, where the first set is: { B, D, E }; the second set is { A, D }.

And 7, planning the AGV according to the running time corresponding to the path formed by each node in the second set and the waiting time required before the target AGV executes the running task.

Since the operating state and the initial position of each AGV are different, the time to start performing the travel task is different. In this embodiment, the target AGV that has the shortest sum of the waiting time and the travel time before the travel task is executed is determined as the AGV that executes the travel task, and the travel route of the AGV is obtained.

The waiting time required by the target AGV to execute the running task comprises the time of the target AGV running from the initial position to the running starting point; and/or the length of time the target AGV has performed the last travel task.

Optionally, the client plans the AGV and further obtains theoretical time and waiting time of the AGV reaching each node on the driving route, and the client records the theoretical time and the waiting time.

Optionally, before planning the AGV according to the topology, the initial state data, and the travel task data based on the principle that the travel task completion time is shortest, the method further includes: determining whether the topology is accurate; and displaying a topological structure correction prompt when the topological structure is inaccurate, wherein the topological structure correction prompt is used for prompting a user to correct the topological structure.

Optionally, determining whether the topology is accurate comprises: it is determined whether redundant nodes and/or redundant paths exist in the topology.

Optionally, after the planned route of the AGVs is determined, the control end displays the planned AGV driving route, the waiting time of each node in the corresponding driving route of each AGV, and the theoretical time for reaching each node.

Referring to fig. 7, a planned route 71 of each AGV, a waiting time 72 of each AGV at each node in the corresponding travel route, and a theoretical time 73 to reach each node are shown. Wherein, for each device Identification (ID) of AGVs in fig. 7, the travel route of the AGVs includes a route from the initial position to the travel start point shown in the first row and a route from the travel start point to the travel end point shown in the second row; the waiting time period comprises the waiting time period of each node on the route from the initial position to the driving starting point shown in the first row and the waiting time period of each node on the route from the driving starting point to the driving terminal shown in the second row; the theoretical time includes the theoretical time to reach each node on the route from the initial position to the travel start point shown in the first row, and the theoretical time to reach each node on the route from the travel start point to the travel end point shown in the second row.

In summary, according to the AGV planning method provided by this embodiment, a topological structure of an AGV drivable route is obtained, where the topological structure includes at least two nodes and drivable routes between different nodes; acquiring initial state data of each AGV, wherein the initial state data comprises an initial position, a movement speed and an equipment identifier of each AGV; acquiring running task data of the AGV, wherein the running task data comprises a running starting point and a running finishing point of at least one running task; planning the AGV according to the topological structure, the initial state data and the driving task data based on the principle that the time for completing the driving task is shortest; the problem of low execution efficiency of the driving task caused by generation of the driving route of the AGV based on the shortest path principle can be solved; since the travel route of the AGV can be generated based on the principle that the travel task completion time is shortest, the execution efficiency of the travel task can be improved.

In addition, the occupied time intervals of the nodes in each driving route can be recorded during the planning of the AGV paths, and the path with the shortest task completion time is selected from the available paths, so that the AGV paths can be ensured not to conflict.

In addition, the acquisition mode of the topological structure and the driving task data is simple, and the efficiency of acquiring the topological structure and the driving task data can be improved.

FIG. 8 is a block diagram of an AGV planning apparatus according to one embodiment of the present application. The device at least comprises the following modules: a first acquisition module 810, a second acquisition module 820, a third acquisition module 830, and a route planning module 840.

A first obtaining module 810, configured to obtain a topology of a drivable route of an AGV, where the topology includes at least two nodes and drivable routes between different nodes;

a second obtaining module 820, configured to obtain initial state data of each AGV, where the initial state data includes an initial position, a movement speed, and an equipment identifier of each AGV;

a third obtaining module 830, configured to obtain driving task data of the AGV, where the driving task data includes a driving start point and a driving end point of at least one driving task;

and the route planning module 840 is used for planning the AGV according to the topological structure, the initial state data and the driving task data based on the principle that the time for completing the driving task is shortest.

Optionally, the route planning module 840 is configured to:

For relevant details reference is made to the above-described method embodiments.

It should be noted that: in the AGV planning apparatus provided in the above embodiment, only the division of the above function modules is used for illustration when AGV planning is performed, and in practical application, the function distribution may be completed by different function modules as needed, that is, the internal structure of the AGV planning apparatus is divided into different function modules to complete all or part of the above described functions. In addition, the AGV planning apparatus and the AGV planning method provided by the above embodiments belong to the same concept, and specific implementation processes thereof are described in detail in the method embodiments and are not described herein again.

The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims

1. An AGV planning method, comprising:

planning the AGV according to the topological structure, the initial state data and the driving task data based on the principle that the time for completing the driving task is shortest;

based on the principle that the completion time of the driving task is shortest, the AGV is planned according to the topological structure, the initial state data and the driving task data, and the method comprises the following steps:

2. The method of claim 1, wherein said planning the AGVs according to the travel time corresponding to the route formed by each node in the second set and the waiting time required by the target AGV before executing the travel task comprises:

3. The method of claim 1 wherein the speed of movement of the AGV includes a straight travel speed and a turn speed of the AGV.

4. A method according to any one of claims 1 to 3, wherein said obtaining the topology of the AGV travelable route comprises:

displaying a topology input interface;

5. A method according to any one of claims 1 to 3, wherein said obtaining travel task data of the AGV comprises:

displaying a task input interface;

6. The method according to any of claims 1 to 3, wherein after the AGV is planned based on the shortest travel task completion time and according to the topology, the initial state data and the travel task data, the method further comprises:

7. The method according to any of claims 1 to 3, wherein before planning the AGV according to the topology, the initial state data and the travel task data based on the shortest travel task completion time principle, the method further comprises:

determining whether the topology is accurate;

8. An AGV planning apparatus, the apparatus comprising:

the route planning module is used for planning the AGV according to the topological structure, the initial state data and the driving task data on the basis of the principle that the time for completing the driving task is shortest;