CN116974283A

CN116974283A - Material transportation control method and device, electronic equipment and storage medium

Info

Publication number: CN116974283A
Application number: CN202310971040.XA
Authority: CN
Inventors: 史树恒
Original assignee: Hebei Gaoda Eternal Plastic Products Co ltd
Current assignee: Hebei Gaoda Eternal Plastic Products Co ltd
Priority date: 2023-08-03
Filing date: 2023-08-03
Publication date: 2023-10-31

Abstract

The invention provides a material transportation control method, a material transportation control device, electronic equipment and a storage medium. The method comprises the following steps: receiving a transportation task instruction of a material, and determining the priority of each transportation task in a plurality of transportation tasks according to the transportation task instruction; distributing a plurality of transport tasks to each AGV, and selecting one transport task with the highest priority from the transport tasks of each AGV as a task to be executed of the AGV; for each AGV, carrying out path planning on a task to be executed of the AGV based on an A-based algorithm to obtain a planned path of the task to be executed of the AGV, so that the AGV executes the task to be executed according to the planned path; the evaluation function of the A-algorithm comprises a turning cost function, and the turning cost function is determined based on turning angles corresponding to each explored node on the planned path. The method and the device can improve the accuracy of AGV path planning and the overall efficiency of AGV material transportation.

Description

Material transportation control method and device, electronic equipment and storage medium

Technical Field

The invention relates to the technical field of intelligent logistics, in particular to a material transportation control method, a device, electronic equipment and a storage medium.

Background

In the automatic wave of logistics industry, along with three-dimensional storage, the storage robot becomes one of the hot problems of research. The storage robot is the most widely applied robot, is an intelligent trolley which can be movably guided in a storage workshop, can be advanced according to program setting, and is widely applied to the fields of production, manufacture and logistics. With the development of automated control technology and the application of automatic guided vehicle (Automated Guided Vehicle, AGV) technology, a key technology and difficulty are path planning and intelligent scheduling problems of the AGV.

AGV path planning is to abstract the working environment information in the production and transportation process into an electronic map, and search out a shortest path which does not conflict with surrounding obstacles on the map. When a plurality of AGVs of the prior art cooperated with each other, path planning is unreasonable, so that the problems of long time consumption and low efficiency of AGV transportation, conflict and untimely obstacle avoidance can be caused when a plurality of AGVs meet.

Disclosure of Invention

The embodiment of the invention provides a material transportation control method, a device, electronic equipment and a storage medium, which are used for solving the problem that an AGV (automatic guided vehicle) executes a transportation task according to a path planned in the prior art.

In a first aspect, an embodiment of the present invention provides a method for controlling material transportation, including:

receiving a transportation task instruction of a material, and determining the priority of each transportation task in a plurality of transportation tasks according to the transportation task instruction;

distributing the transport tasks to each AGV, and selecting one transport task with the highest priority from the transport tasks of each AGV as a task to be executed of the AGV;

and for each AGV, carrying out path planning on a task to be executed of the AGV based on an A-algorithm to obtain a planned path of the task to be executed of the AGV, so that the AGV executes the task to be executed according to the planned path, wherein an evaluation function of the A-algorithm comprises a turning cost function, and the turning cost function is determined based on turning angles corresponding to each explored node on the planned path.

In one possible implementation manner, the turning angle corresponding to each explored node is determined based on an included angle between a first vector and a second vector, wherein the first vector is a vector of a last explored node of a current node pointing to the current node, and the second vector is a vector of the current node pointing to a next node of the current node.

In one possible implementation, the turning cost function is:

wherein ,representing +.>The explored nodes; />Representing a current node; />Representing a turning cost coefficient; />Indicate->Turning angles corresponding to the explored nodes.

In one possible implementation manner, the evaluation function of the a-algorithm includes a flow cost function, where the flow cost function is based on the number of AGVs that include a target segment in the planned path of all AGVs and have not yet passed through the target segment, and the target segment is a segment between a current node and a next node to be explored.

In one possible implementation, the evaluation function is:

wherein ,represented at pass node->In the case of (a), the actual time cost of the optimal path from the start node to the target node; />Representing from the start node to the node +.>The actual time cost of the optimal path of (a); />For heuristic function, represent slave node +.>Estimated time cost of optimal path to target node; />The curve cost function; />Is a traffic cost function.

In one possible implementation, the transportation task instruction includes a task issue time and a task importance level of each transportation task;

The priority of each transport task is determined by:

wherein ,priority for transportation tasks; />The task release time is the time length between the task release time and the current time; />The importance degree of the task; />For the pause waiting time, representing the pause waiting time of the AGV executing the transport task in the planned path; />、/> and />Is the weight;

the method further comprises the steps of:

when a plurality of AGVs execute a transport task and meet at a certain node, controlling the AGVs to sequentially pass through the node from high to low according to the priority, and stopping waiting by the rest AGVs each time by only one AGV;

and accumulating the pause waiting time of each AGV, and updating the priority of the transport task being executed by the AGV according to the pause waiting time.

In one possible implementation manner, for each AGV, the performing path planning on the task to be performed by the AGV based on an a-algorithm to obtain a planned path of the task to be performed by the AGV includes:

determining a starting node and a target node of a planned path based on the task to be executed;

searching the reachable adjacent nodes from the starting node, setting the starting node as a current node, calculating the cost function of the adjacent nodes of the current node, and selecting the adjacent node with the minimum cost function as an expansion node of a planning path;

Setting the expansion node as a new current node, repeating the node exploration step until no node can be expanded, and obtaining the planning path of the task to be executed.

In a second aspect, an embodiment of the present invention provides a material transportation control device, including:

the determining module is used for receiving a transportation task instruction of the material and determining the priority of each transportation task in the plurality of transportation tasks according to the transportation task instruction;

the selecting module is used for distributing the plurality of transport tasks to the AGVs of the respective movable guide vehicles, and selecting one transport task with the highest priority from the transport tasks of each AGV as a task to be executed of the AGV;

and the planning module is used for carrying out path planning on the task to be executed of each AGV based on an A algorithm to obtain a planned path of the task to be executed of the AGV, so that the AGV executes the task to be executed according to the planned path, wherein an evaluation function of the A algorithm comprises a turning cost function, and the turning cost function is determined based on turning angles corresponding to each explored node on the planned path.

In a third aspect, an embodiment of the present invention provides an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method according to the first aspect or any one of the possible implementations of the first aspect, when the computer program is executed by the processor.

In a fourth aspect, embodiments of the present invention provide a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method as described above in the first aspect or any one of the possible implementations of the first aspect.

The embodiment of the invention provides a material transportation control method, a device, electronic equipment and a storage medium. And then distributing a plurality of transport tasks to each AGV, and selecting one transport task with the highest priority from the transport tasks of each AGV as a task to be executed of the AGV. And for each AGV, carrying out path planning on the task to be executed of the AGV based on an A-based algorithm to obtain a planned path of the task to be executed of the AGV, so that the AGV executes the task to be executed according to the planned path. The evaluation function of the A-algorithm comprises a turning cost function, and the turning cost function is determined based on turning angles corresponding to each explored node on the planned path. According to the invention, the turning cost corresponding to the explored nodes in the planned path is accumulated, and the turning cost function is added into the evaluation function of the A-algorithm, so that the accuracy of AGV path planning and the overall efficiency of AGV material transportation are improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of an implementation of a method for controlling material transportation according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of a grid map of a method for controlling material transportation according to an embodiment of the present invention;

FIG. 3 is a schematic view of a turning angle of a method for controlling material transportation according to an embodiment of the present invention;

FIG. 4 is a schematic diagram of a turning cost of a method for controlling material transportation according to an embodiment of the present invention;

FIG. 5 is a flow cost schematic diagram of a method for controlling material transportation according to an embodiment of the present invention;

FIG. 6 is a schematic diagram of a material transportation control device according to an embodiment of the present invention;

fig. 7 is a schematic diagram of an electronic device according to an embodiment of the present invention.

Detailed Description

In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the following description will be made by way of specific embodiments with reference to the accompanying drawings.

Referring to fig. 1, a flowchart of an implementation of a method for controlling material transportation according to an embodiment of the present invention is shown, and details are as follows:

s101, receiving a transportation task instruction of the material, and determining the priority of each transportation task in the transportation tasks according to the transportation task instruction.

In the embodiment of the invention, the general control equipment of the warehouse logistics receives a transport task instruction of the materials input manually, wherein the transport task instruction comprises information such as the types and the quantities of the materials to be transported. The master control equipment searches the storage position of the material and the number and the positions of the idle AGVs at the current moment according to the received information of the material to be transported, divides the transport tasks of the material into transport tasks of a plurality of AGVs based on the storage position and the number and the positions of the idle AGVs, and distributes the transport tasks to the corresponding AGVs.

In some embodiments, the shipping task instructions include a task issue time and a task importance level for each shipping task.

The priority of each transport task is determined by:

wherein ,priority for transportation tasks; />The task release time is the time length between the task release time and the current time; / >The importance degree of the task; />For the pause waiting time, representing the pause waiting time of the AGV executing the transport task in the planned path; />、/> and />Is the weight.

In the embodiment of the invention, the priority of the transport task is determined based on the task release time, the task importance degree and the AGV pause waiting time of the transport task, and the transport task with high priority is executed first. In general, when transportation tasks with different release times need to be transported at the same time, the transportation task with an earlier release time is preferentially considered, so that the situation that a certain transportation task is not executed for a long time is avoided, and therefore the release time of the task is considered in the priority of the transportation task. The importance degree of the transportation task is manually determined and input into the general control equipment of the logistics, for example, for some urgent transportation tasks, the importance degree of the transportation task can be manually input into a larger task, so that the priority of the transportation task can be improved. The pause waiting time length represents the pause waiting time length of the AGV which is executing the transport task in the planning path, and when the AGV does not start executing the transport task, the pause waiting time length of the AGV is zero, and the priority of the transport task is only determined by the task release time and the task importance degree.

In some embodiments, when it is monitored that multiple AGVs are performing a transport task that meets at a node, the AGVs are controlled to sequentially pass through the node from high to low in priority, passing only one AGV at a time, and the remaining AGVs pause waiting.

And accumulating the pause waiting time of each AGV, and updating the priority of the transport task being executed by the AGV according to the pause waiting time. In the case of a transport task, the greater the pause waiting time is, the higher the priority is, under the condition that the task release time and the importance degree of the task are fixed.

In the embodiment of the invention, the AGVs execute the transport task according to the planned path, when a plurality of AGVs execute the transport task simultaneously, the plurality of AGVs often travel to a certain node at the same time, or when a certain AGV is about to pass through the certain node, another AGV is passing through the certain node, and in this case, the AGVs may collide. At this time, the priorities of the transport tasks being executed by the AGVs may be compared, and the AGV with the higher priority of the transport task may pass through the node first, and the AGV with the lower priority may pause waiting. However, for an AGV with a lower priority for a transport task, the AGV may wait for a plurality of pauses when multiple nodes meet other AGVs, and thus the transport task of the AGV may not be completed later. According to the method and the device for updating the transport tasks, the pause waiting time of the AGV is accumulated, the priority of the transport tasks executed by the AGV is updated based on the pause waiting time of the AGV, and the priority of the transport tasks can be improved. When a certain node meets other AGVs next time, the priority of the transport tasks updated based on the pause waiting time of the AGVs is compared, and the situation that the transport time is too long due to the fact that the certain AGVs pause waiting for many times is avoided.

In some embodiments, there may be an traveling AGV on the path behind an AGV while the AGV pauses for waiting in the planned path of the AGV, with the two AGVs at risk of collision. To avoid this risk, a radar or sensor may be installed in front of or behind the AGV to monitor the distance between the present AGV and other AGVs. When the distance between the AGV and the rear AGV is equal to or smaller than a preset distance threshold value, two AGVs are judged to collide, and the rear AGV is controlled to wait in a pause mode. When the radar or the sensor detects that the distance between the AGV and the rear AGV is greater than the distance threshold, the AGV is restarted, the rear AGV is stopped, and the transport task is continuously executed.

S102, distributing a plurality of transport tasks to each AGV, and selecting one transport task with the highest priority from the transport tasks of each AGV as a task to be executed of the AGV.

In the embodiment of the invention, the tasks to be executed in each AGV are arranged in descending order according to the priority, a task list is generated, a first transport task is selected from the task list as the task to be executed of the AGV, and the first transport task is removed from the task list. When the AGV receives a new transport task, all transport tasks in the AGV are rearranged according to the descending priority order, and an updated task list is generated.

S103, for each AGV, carrying out path planning on a task to be executed of the AGV based on an A-Star algorithm to obtain a planned path of the task to be executed of the AGV, so that the AGV executes the task to be executed according to the planned path, wherein an evaluation function of the A-Star algorithm comprises a turning cost function, and the turning cost function is determined based on turning angles corresponding to each explored node on the planned path.

Referring to fig. 2, a schematic diagram of a grid map of a material transportation control method according to an embodiment of the present invention is shown. The sensor can be utilized to collect environmental information and establish a grid map for the AGV to transport the material. As shown in fig. 2, the grid map is a map of the transportation environment divided into a number of blocks, called grids, which are connected together to form a model. The hatched area indicates that there is an obstacle, no passage is possible, and the white area indicates that passage is possible. The location of any grid in the grid map is represented by coordinates, and therefore the location of the AGV performing the transport may be represented by coordinates in the grid map.

In the embodiment of the invention, on a grid map, an A-type algorithm is adopted to plan the path of the AGV for executing the material transportation task. The algorithm A is actually a heuristic shortest path searching algorithm combining state space searching and greedy algorithm, and utilizes an evaluation function by combining heuristic concepts in the greedy algorithm on the basis of state space searching And evaluating each searched state node, calculating the best position, searching from the best node, and repeating the steps until the target node is searched. The algorithm A leads the search to have directionality, reduces a plurality of blind and invalid search operations, and improves the search efficiency. Different evaluation functions->Different effects can be produced, so the evaluation function is the key to the a algorithm.

In some embodiments, for each AGV, performing path planning on a task to be performed by the AGV based on an a-algorithm to obtain a planned path of the task to be performed by the AGV, including:

determining a starting node and a target node of a planned path based on a task to be executed; exploring the reachable adjacent nodes from the initial node, setting the initial node as a current node, calculating the cost function of the adjacent nodes of the current node, and selecting the adjacent node with the minimum cost function as an expansion node of the planning path; setting an expansion node as a new current node, repeating the node exploration step until no node can be expanded, and obtaining a planning path of a task to be executed.

In the embodiment of the invention, the traveling path of the AGV is planned according to the following steps:

And step 1, determining an initial node and a target node of an AGV planning path in a grid map based on a task to be executed of the AGV. And establishing an open list and a closed list, and adding the starting node and adjacent nodes which are reachable around the starting node into the open list.

Step 2, setting the starting node as a father node of the adjacent nodes, traversing the open list, calculating the evaluation functions of all the nodes in the open list, selecting the node with the smallest evaluation function as the current node, removing the current node from the open list, and adding the current node into the closed list.

Step 3, traversing all adjacent nodes of the current node, ignoring unreachable nodes, and judging whether the rest adjacent nodes are in an open list:

if the adjacent nodes which are not in the open list exist, adding the adjacent nodes which are not in the open list into the open list, and calculating the evaluation functions of all the adjacent nodes of the current node;

if the adjacent node of the current node is in the open list, calculating the actual cost of the adjacent node, and if the actual cost is lower than the original cost, setting the current node as a father node, and recalculating the cost function of the adjacent node of the current node.

And 4, selecting the node with the minimum cost function in the open list as the current node to be expanded, removing the current node from the open list, and adding the current node into the closed list.

Step 5, judging whether the target node is added to the open list:

if the target node is added into the open list, the path planning is completed, and the target node is reversely calculated to the starting node, namely the planned path for executing the transport task for the AGV;

if the target node is not in the open list, step 3 is performed.

In some embodiments, the evaluation function is:

In the embodiment of the invention, in order to ensure the high efficiency of the A-algorithm, the heuristic function is needed to beThe design is carried out so that the design is carried out,representing node->The actual cost to the target node if +.>The search range is small, the number of search nodes is small, and the quality of the obtained path is low although the efficiency is high. Therefore, claim- >At this time, the search range becomes large, the quality of the obtained path is significantly improved, and the heuristic function ++>Designed as the time it takes for the AGV to pass the length of manhattan distance from the current node to the target node.

Wherein the Manhattan distance is, wherein ,/>Represents the abscissa of the current node, +.>Representing the ordinate of the current node, +.>Represents the abscissa of the target node, +.>Representing the ordinate of the target node +.>Indicating the speed of the AGV. Manhattan distance indicates that the AGV can only move up, down, left, right, and in the grid map.

In some embodiments, the turn cost function is determined based on a turn angle corresponding to each explored node on the planned path, the turn angle corresponding to each explored node being determined based on an angle between the first vector and the second vector. For the current node, the first vector is the vector of the last explored node of the current node pointing to the current node, and the second vector is the vector of the current node pointing to the next node of the current node. For a node in the explored path, the first vector is the vector that the one explored node points to on the node, and the second vector is the vector that the node points to the next explored node.

Referring to fig. 3, a schematic diagram of a turning angle of a material transporting method according to an embodiment of the present invention is shown. As shown in fig. 3, node a represents a start node of a planned path, node B, node C, node D, and node E represent explored nodes, solid lines between explored nodes represent planned road segments, and node E represents a last node among explored nodes at the current time, i.e., a current node. The node E is used as a current node, the A-algorithm search path needs to search for adjacent nodes of the current node E, the node F represents one of the adjacent nodes of the current node E, namely the next node to be searched for of the current node E, and a dotted line between the current node E and the node F represents the path to be searched for.

For the current node E, the last explored node D of the current node E points to the current node E to form a first vector of the turning angle of the current node EThe current node E points to the next node F to be explored of the current node E to form the current nodeSecond vector of turning angle of point E +.>. Let the coordinates of the current node E in the grid map be +.>The coordinates of node D are +.>The coordinates of node F are +.>. Then the first vector is +. >The second vector is->The included angle between the first vector and the second vector represents the turning angle of the AGV at the current node E, and the expression is as follows:

for other nodes in the planned path, no turn exists for the AGV at the initial node A, i.e. both the turning angle and the turning cost of the AGV at the node A are zero. For node B, the last explored node A of node B points to node B, forming a first vector of turn angles at node BNode B points to the next explored node C of node B, forming a second vector of turning angles at node B>. The turning angle at node B is +.> and />Is included in the bearing. The turning angle of the AGV at other nodes in the explored path is as calculated by node B above. The included angle between the first vector and the second vector represents the turning angle of the AGV at the node, the larger the turning angle is, the larger the turning cost determined based on the turning angle is, and the longer the time consumed by the AGV for turning at the node is. If the AGV does not have a turn at a certain node, as shown by node D in FIG. 3, the turning angle at node D is zero and the turning cost is zero.

In some embodiments, the turn cost function is:

wherein ,representing +. >The explored nodes; />Representing a current node; />Representing a turning cost coefficient; />Indicate->Turning angles corresponding to the explored nodes.

In this embodiment, the turning cost coefficient may be regarded as a unit turning angle cost, which may be equivalent to one weight coefficient. Because the evaluation function in the algorithm a contains the turning cost, the distance cost and the flow cost, when the three costs are quantized, the situation that the numerical values are not uniform may exist. Therefore, a coefficient needs to be set in each part of cost function so that the numerical values of each cost part are uniform. That is, the turning cost coefficient is set as required, and is equivalent to a weight coefficient in the turning cost function, and at the same time, the value of the turning cost function is unified with the values of other functions in the evaluation function when the actual application is performed.

Referring to fig. 4, a schematic diagram of turning cost of a material transportation method according to an embodiment of the present invention is shown. Most AGV path planning algorithms generally take path length as an evaluation index, but the time cost of increasing path steering caused by excessive path turning times is rarely considered. As shown in fig. 4, assuming that a is a start node and B is a target node, there are two paths from a to B, a dotted line in fig. 4 represents a path 1, a solid line in fig. 4 represents a path 2, and manhattan distances of the two paths are equal and are both shortest distances. However, path 2 has multiple turns compared to path 1, and the AGV takes time to turn, so path 1 is more optimal in distance and time. It is necessary to consider the turn cost in the path planning process of the AGV.

In the embodiment of the invention, the turning cost function of the current node is the sum of turning costs corresponding to all explored nodes on the planned path, and the turning cost is the product of the turning cost coefficient and the turning angle at the node. As shown in fig. 3, when the node F is explored, the turning cost function of the current node E is the sum of the turning costs of the node a, the node B, the node C and the node E, wherein the turning costs of the node a and the node D are zero, and the turning cost of the node E is the turning cost of the AGV at the node E in the process of reaching the node F from the node D through the node E. The addition of the turning cost function to the path planning evaluation function can intuitively represent the sum of the turning costs of the path from the starting node a through the current node E to the node F. Planning based on the method can shorten the time for planning the path and improve the efficiency of the AGV for executing the transport task.

In some embodiments, the evaluation function of the a-algorithm includes a flow cost function, where the flow cost function is based on the number of AGVs that have not yet passed the target road segment and include the target road segment in the planned paths of all AGVs, and the target road segment is a road segment between the current node and the next node to be explored.

Referring to fig. 5, a flow cost schematic diagram of a material transportation control method according to an embodiment of the present invention is shown. As shown in fig. 5, the node H represents the last node explored in the planned path at the current time, i.e., the current node, the node G is the last explored node of the node H, and the nodes I, J and K are neighboring nodes of the current node H. And searching adjacent nodes of the current node H based on the evaluation function, and selecting a node with the smallest evaluation function in the adjacent nodes as a next searched node. The solid line between node G and node H represents the explored path, node H forms three dashed lines with node I, node J and node K, respectively, representing the path to be explored.

In the embodiment of the invention, the road section formed by the current node H and the adjacent nodes is regarded as a target road section, and three target road sections HI, HJ and HK are obtained. Because a plurality of AGVs simultaneously execute a material transportation task, planned paths of some AGVs executing the transportation task may pass through a target road section, and if the planned paths overlap with the target road section being explored for a plurality of times, the situation that the AGVs are jammed on the target road section may occur. Therefore, when the next node is explored, the AGV flow of the target road section corresponding to the node needs to be considered, and the larger the AGV flow of the target road section is, the more the AGVs passing through the target road section in a period of time are, the greater the probability that a plurality of AGVs are jammed or even collide is, and the more time is consumed for the AGVs to execute the transportation task.

Further, to avoid excessive number of AGVs and excessive flow on the planned path, the path of the planned AGVs within the set time before the current time can be obtained, whether each planned path coincides with any one of the three target road segments in fig. 5 is judged, and the position of the AGVs corresponding to the planned path with the coincidence is monitored. At the current time, the AGVs have begun to perform the transport task, some AGVs may have passed the target link, and some AGVs have not passed the target link. The method comprises the steps of obtaining the position of each AGV, eliminating the AGVs which pass through a target road section, counting the number of the AGVs which pass through the target road section in a period of time for each target road section, wherein the more the number of the AGVs is, the greater the flow of the target road section in a set period of time is, the higher the probability that the AGVs are crowded in the target road section is, and therefore the higher the time cost of the AGVs passing through the target road section is. The flow cost function of a certain node is the product of the number of AGVs of a target road section formed by the current node and the node in a set time and the flow cost coefficient. For example, the flow cost function at node I in fig. 5 is the product of the number of AGVs that pass the target segment HI in a set period of time and the flow cost coefficient. The flow cost function is added to the evaluation function of the path planning, so that a road section with large flow can be avoided when the path is planned, and the overall transport efficiency of the AGV is improved.

According to the embodiment of the invention, the priority of each transportation task in the plurality of transportation tasks is determined by receiving the transportation task instruction of the material and according to the transportation task instruction. And then distributing a plurality of transport tasks to each AGV, and selecting one transport task with the highest priority from the transport tasks of each AGV as a task to be executed of the AGV. And for each AGV, carrying out path planning on the task to be executed of the AGV based on an A-based algorithm to obtain a planned path of the task to be executed of the AGV, so that the AGV executes the task to be executed according to the planned path. The evaluation function of the A-algorithm comprises a turning cost function, and the turning cost function is determined based on turning angles corresponding to each explored node on the planned path. According to the invention, the turning cost corresponding to the explored nodes in the planned path is accumulated, and the turning cost function is added into the evaluation function of the A-algorithm, so that the accuracy of AGV path planning and the overall efficiency of AGV material transportation are improved.

It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.

The following are device embodiments of the invention, for details not described in detail therein, reference may be made to the corresponding method embodiments described above.

Fig. 6 is a schematic structural diagram of a material transportation control device according to an embodiment of the present invention, and for convenience of explanation, only a portion related to the embodiment of the present invention is shown, as shown in fig. 6, the material transportation control device 6 includes: a determination module 601, a selection module 602 and a planning module 603.

The determining module 601 is configured to receive a transportation task instruction of a material, and determine a priority of each transportation task of a plurality of transportation tasks according to the transportation task instruction.

In one possible implementation, the determining module 601 is specifically configured to: the priority of each transport task is determined by:

wherein ,priority for transportation tasks; />The task release time is the time length between the task release time and the current time; />The importance degree of the task; />For the pause waiting time, representing the pause waiting time of the AGV executing the transport task in the planned path; />、/> and />Is the weight.

When it is monitored that a plurality of AGVs execute a transport task to meet at a certain node, the AGVs are controlled to sequentially pass through the node from high to low according to priority, only one AGV passes through each time, and the rest of AGVs pause waiting.

And the selecting module 602 is configured to allocate the plurality of transport tasks to the respective mobile guided vehicles AGVs, and for each AGV, select, from the transport tasks of the AGV, a transport task with the highest priority as a task to be executed by the AGV.

And a planning module 603, configured to, for each AGV, perform path planning on a task to be executed of the AGV based on an a-algorithm, to obtain a planned path of the task to be executed of the AGV, so that the AGV executes the task to be executed according to the planned path, where an evaluation function of the a-algorithm includes a turning cost function, and the turning cost function is determined based on a turning angle corresponding to each explored node on the planned path.

In one possible implementation, the planning module 603 is specifically configured to: and determining the turning angle corresponding to each explored node based on the included angle of a first vector and a second vector, wherein the first vector is the vector of the last explored node of the current node pointing to the current node, and the second vector is the vector of the current node pointing to the next node of the current node.

In one possible implementation, the planning module 603 is specifically configured to: the turning cost function is determined as follows:

In one possible implementation, the planning module 603 is specifically configured to: and determining an evaluation function of the A-algorithm to comprise a flow cost function, wherein the flow cost function is based on the fact that a planned path of all AGVs comprises a target road section, the number of AGVs which do not pass through the target road section is determined, and the target road section is a road section between a current node and a next node to be explored.

In one possible implementation, the planning module 603 is specifically configured to: determining the evaluation function as:

wherein ,represented at pass node->In the case of (a), the actual time cost of the optimal path from the start node to the target node; />Representing from the start node to the node +.>The actual time cost of the optimal path of (a); />For heuristic function, represent slave node +.>Estimated time cost of optimal path to target node; / >The curve cost function; />Is a traffic cost function.

In one possible implementation, the planning module 603 is specifically configured to: determining a starting node and a target node of a planned path based on the task to be executed; and exploring the reachable adjacent nodes from the starting node, setting the starting node as a current node, calculating the cost function of the adjacent nodes of the current node, and selecting the adjacent node with the minimum cost function as an expansion node of the planning path.

Fig. 7 is a schematic diagram of an electronic device according to an embodiment of the present invention. As shown in fig. 7, the electronic device 7 of this embodiment includes: a processor 70, a memory 71, and a computer program 72 stored in the memory 71 and executable on the processor 70. The processor 70, when executing the computer program 72, implements the steps of the various embodiments of the material handling control method described above, such as steps S101 through S103 shown in fig. 1. Alternatively, the processor 70, when executing the computer program 72, performs the functions of the modules in the apparatus embodiments described above, such as the functions of the modules 601 to 603 shown in fig. 6.

By way of example, the computer program 72 may be partitioned into one or more modules/units that are stored in the memory 71 and executed by the processor 70 to complete the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing the specified functions, which instruction segments are used for describing the execution of the computer program 72 in the electronic device 7. For example, the computer program 72 may be split into modules 601 to 603 shown in fig. 6.

The electronic device 7 may be a computing device such as a desktop computer, a notebook computer, a palm computer, a cloud server, etc. The electronic device 7 may include, but is not limited to, a processor 70, a memory 71. It will be appreciated by those skilled in the art that fig. 7 is merely an example of the electronic device 7 and is not meant to be limiting as the electronic device 7 may include more or fewer components than shown, or may combine certain components, or different components, e.g., the electronic device may further include an input-output device, a network access device, a bus, etc.

The processor 70 may be a central processing unit (Central Processing Unit, CPU), or may be another general purpose processor, a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a Field-programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory 71 may be an internal storage unit of the electronic device 7, such as a hard disk or a memory of the electronic device 7. The memory 71 may be an external storage device of the electronic device 7, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device 7. Further, the memory 71 may also include both an internal storage unit and an external storage device of the electronic device 7. The memory 71 is used for storing the computer program and other programs and data required by the electronic device. The memory 71 may also be used for temporarily storing data that has been output or is to be output.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.

In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/electronic device and method may be implemented in other manners. For example, the apparatus/electronic device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present invention may also be implemented by implementing all or part of the flow of the method of the above embodiment, or by instructing the relevant hardware by a computer program, where the computer program may be stored in a computer readable storage medium, and the computer program may implement the steps of each of the method embodiments for controlling material transportation when executed by a processor. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium may include content that is subject to appropriate increases and decreases as required by jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is not included as electrical carrier signals and telecommunication signals.

The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.

Claims

1. A method of controlling material transport, comprising:

distributing the plurality of transport tasks to each AGV, and selecting one transport task with the highest priority from the transport tasks of each AGV as a task to be executed of the AGV;

for each AGV, carrying out path planning on a task to be executed of the AGV based on an A-algorithm to obtain a planned path of the task to be executed of the AGV, so that the AGV executes the task to be executed according to the planned path, wherein an evaluation function of the A-algorithm comprises a turning cost function, and the turning cost function is determined based on turning angles corresponding to each explored node on the planned path; the evaluation function of the A-algorithm comprises a flow cost function, wherein the flow cost function is based on the fact that a planned path of all AGVs comprises a target road section, the number of AGVs which do not pass through the target road section is determined, and the target road section is a road section between a current node and a next node to be explored.

2. The method of claim 1, wherein the turn angle for each explored node is determined based on an angle between a first vector that is the vector of a current node to which a last explored node of the current node points and a second vector that is the vector of a next node to which the current node points.

3. The method of claim 1, wherein the turn cost function is:

wherein ,representing +.>The explored nodes; />Representing a current node; />Representing a turning cost coefficient;indicate->Turning angles corresponding to the explored nodes.

4. The method of claim 1, wherein the evaluation function is:

wherein ,represented at pass node->In the case of (a), the actual time cost of the optimal path from the start node to the target node; />Representing from the start node to the node +.>The actual time cost of the optimal path of (a); />For heuristic function, represent slave node +.>Estimated time cost of optimal path to target node; />The curve cost function; / >Is a traffic cost function.

5. The method of claim 1, wherein the shipping task instructions include a task publication time and a task importance level for each shipping task;

the priority of each transport task is determined by:

the method further comprises the steps of:

6. The method of claim 1 wherein for each AGV, performing path planning on a task to be performed by the AGV based on an a-algorithm to obtain a planned path of the task to be performed by the AGV, including:

7. A material transport control device, comprising:

the system comprises a planning module, a planning module and a control module, wherein the planning module is used for carrying out path planning on a task to be executed of each AGV based on an A algorithm to obtain a planned path of the task to be executed of the AGV, so that the AGV executes the task to be executed according to the planned path, an evaluation function of the A algorithm comprises a turning cost function, and the turning cost function is determined based on turning angles corresponding to each explored node on the planned path; the evaluation function of the A-algorithm comprises a flow cost function, wherein the flow cost function is based on the fact that a planned path of all AGVs comprises a target road section, the number of AGVs which do not pass through the target road section is determined, and the target road section is a road section between a current node and a next node to be explored.

8. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of the preceding claims 1 to 6 when the computer program is executed.

9. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method according to any of the preceding claims 1 to 6.