CN112525196B

CN112525196B - AGV route planning and scheduling method and system based on multidimensional data

Info

Publication number: CN112525196B
Application number: CN202011319995.XA
Authority: CN
Inventors: 周军; 李建强; 高新彪; 杨桂林; 孟广辉
Original assignee: Shandong Alesmart Intelligent Technology Co Ltd
Current assignee: Shandong Alesmart Intelligent Technology Co Ltd
Priority date: 2020-11-23
Filing date: 2020-11-23
Publication date: 2023-04-28
Anticipated expiration: 2040-11-23
Also published as: CN112525196A

Abstract

The present disclosure provides a method and a system for planning and scheduling an AGV route based on multidimensional data, which acquire operation data and state data of the AGV, and construct a data model according to the acquired data; calculating the shortest running route of the AGV according to the current position and the end position of the AGV; according to the data model and the running task, the occupied time and the removal time of each section of route are obtained, and a time sequence table is formed; the AGV operates according to the shortest driving route and the time sequence table, dynamically updates the time sequence table according to the current operation state and the operation time, and evaluates and/or adjusts the route of the AGV according to the updated time sequence table; the method carries out compound processing on the acquired data, and calculates and improves the running route of the vehicle through a data model; according to the real-time running data of the vehicle, the model data is updated in real time, so that the dynamic planning and intelligent avoidance and unlocking of the vehicle are realized, and the accuracy of AGV route planning and scheduling is improved.

Description

AGV route planning and scheduling method and system based on multidimensional data

Technical Field

The disclosure relates to the technical field of AGV path planning and intelligent scheduling, in particular to an AGV path planning and scheduling method and system based on multidimensional data.

Background

The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.

Advanced manufacturing technology and factory logistics automation are new technical application fields which are paid great attention at home and abroad at present. Flexible processing, flexible assembly, and warehouse logistics automation are important components. In a processing and manufacturing production line, an assembly (such as an automobile and a component thereof) production line and a warehouse access conveying line, AGV equipment is increasingly adopted to realize the transportation and distribution of materials, but with the increase of the AGV equipment, the dispatching and the use efficiency optimization of AGV vehicles become more and more important problems. How to plan and optimize the running route of the AGV and reasonably arrange and schedule the vehicles becomes a continuously pursuing goal of each manufacturer.

The inventors of the present disclosure have found that the current optimal routing algorithms for AGVs are well established, such as the commonly used Dijkstra algorithm, floyd algorithm, A-algorithm, and optimization and modification of these algorithms. The optimal route of the vehicle operation is conveniently calculated by using the algorithms. However, the operation of multiple AGVs is a complex and coordinated process, and when multiple vehicles share the same route, there may be problems such as route occupation conflict, so that the operation efficiency of the vehicles is drastically reduced. There are several studies that optimize on existing route planning algorithms, some of which also add time estimates. Such as: a dynamic multi-path planning algorithm based on an A-algorithm. However, in actual running, service scenes such as vehicle faults, vehicle obstacle avoidance, on-site loading and unloading and the like determine that a time plan is continuously disturbed, and even if dynamic time update is added, only notification is acquired, so that the running delay of the vehicle is obtained, and real-time dynamic planning and scheduling of an AGV route cannot be realized timely; in addition, the traditional AGV scheduling is usually started from the optimization angle of a route algorithm or simple model data analysis, so that the problem of the efficiency of the operation of an AGV vehicle is solved, but the solution has a large limitation, the on-site operation is a complex and variable process, and any single-element-based analysis or algorithm improvement cannot solve the problem of the vehicle scheduling at the same time.

Disclosure of Invention

In order to solve the defects in the prior art, the present disclosure provides an AGV route planning and scheduling method and system based on multidimensional data, which performs composite processing on acquired data, and on one hand, calculates and improves the running route of a vehicle through a data model; on the other hand, according to the real-time running data of the vehicle, the model data is updated in real time, so that the dynamic planning and intelligent avoidance and unlocking of the vehicle are realized, and the accuracy of AGV route planning and scheduling is improved.

In order to achieve the above purpose, the present disclosure adopts the following technical scheme:

the first aspect of the present disclosure provides an AGV route planning and scheduling method based on multidimensional data.

An AGV route planning and scheduling method based on multidimensional data comprises the following steps:

acquiring operation data and state data of the AGV, and constructing a data model according to the acquired data;

calculating the shortest running route of the AGV according to the current position and the end position of the AGV;

according to the data model and the running task, the occupied time and the removal time of each section of route are obtained, and a time sequence table is formed;

the AGV operates according to the shortest driving route and the time sequence table, dynamically updates the time sequence table according to the current operation state and the operation time, and evaluates and/or adjusts the route of the AGV according to the updated time sequence table.

As some possible implementations, the data models include a map route sub-model, a map vehicle sub-model, a map node and action sub-model, a task sub-model, and a simulation sub-model.

As some possible implementations, when the AGV starts for the first mission, the shortest route and the vehicle residence time of each route and point are planned according to the current position and the end position of the AGV;

when a task starts again, planning a shortest route according to the current position and the end position of the AGV, and checking whether the shortest route has intersection with the route of the previous time;

if the intersection exists, checking whether the vehicle runs in the same direction, if the vehicle runs in the opposite direction, planning a new route, evaluating the total running time of the new route and the running time waiting time of the original route, and taking the route with the shortest time as the running route.

As some possible implementations, the time synchronization of all outstanding routes behind the current AGV is adjusted according to the updated time sequence table;

and (3) the AGVs sharing the route with the current task re-evaluate the adjusted final arrival time, and trigger the route adjustment if the arrival time exceeds a preset threshold.

As a possible implementation manner, after the route planning is finished, checking whether each route section formed by the route is occupied by other vehicles and occupied directions, if the route section is occupied in the same direction, the current vehicles can continue to occupy, and if the current vehicles are occupied in different directions and occupied time conflicts, the current vehicles are regarded as occupied conflicts;

if the occupation conflict exists, setting the conflict route as non-passable, thereby planning other new routes, and if the passable route exists, evaluating the time of the new route;

if the whole time of the new route is less than the current route, adopting the new route, otherwise, selecting the current route;

and if no drivable route exists, selecting the current planned route as the driving route.

As some possible implementation manners, after the time sequence table is formed, performing primary locking on the route, wherein the primary locking only detects conflicts;

performing secondary locking, locking to the intersection, and after the secondary locking, prohibiting other vehicles from entering in a conflict time window;

the vehicle starts to run, and before the vehicle runs, the front section and the rear section of routes are occupied, after the front section and the rear section of routes are occupied successfully, the vehicle can run, the front section and the rear section of routes are occupied in an exclusive locking mode, and after the front section and the rear section of routes are locked, the vehicle in any direction is forbidden to enter the two sections of routes;

after the route is driven out, locking the next section, releasing the current section, if the locking fails, stopping and detecting whether the vehicle is deadlocked, if the vehicle is deadlocked, executing a deadlock release algorithm, and if the vehicle is not deadlocked, stopping and waiting for the route to be released.

As some possible implementation manners, when the multi-AGV operates, a locking and avoiding mode of sectional integral locking, front-back locking and mutual deadlock preferential release is adopted, and the method comprises the following steps:

in the running process of the vehicle, locking a route to be run to an intersection, and executing after the locking is successful;

if the locking is unsuccessful, stopping the vehicle and waiting if the occupied vehicle is in a mobile state, and continuously attempting to lock; if the occupied vehicle is in a stop state, considering the two to be deadlocked, and unlocking;

when unlocking, each process scans according to a unified rule, finds out the associated vehicles which affect occupation, finds out the vehicles which are mutually deadlocked, acts for the vehicles which are mutually deadlocked, and the vehicles which are not deadlocked are not moved first;

the vehicle is mutually deadlocked, the parking time is seen, the vehicle is stopped first and avoided, and if the vehicle is stopped first and has no avoidance route, the vehicle is stopped first and operated first.

A second aspect of the present disclosure provides an AGV route planning and scheduling system based on multidimensional data, comprising:

a data model construction module configured to: acquiring operation data and state data of the AGV, and constructing a data model according to the acquired data;

a route planning module configured to: calculating the shortest running route of the AGV according to the current position and the end position of the AGV;

a time sequence table generation module configured to: according to the data model and the running task, the occupied time and the removal time of each section of route are obtained, and a time sequence table is formed;

a planning scheduling module configured to: the AGV operates according to the shortest driving route and the time sequence table, dynamically updates the time sequence table according to the current operation state and the operation time, and evaluates and/or adjusts the route of the AGV according to the updated time sequence table.

A third aspect of the present disclosure provides an AGV route planning and scheduling simulation system based on multidimensional data, comprising:

a simulation data acquisition module configured to: according to the route and the running speed of the vehicle, displaying the running effect of the vehicle in an animation mode, and simultaneously updating the data of the data model in real time to obtain the simulation data of the vehicle;

a simulation optimization module configured to: obtaining a simulation report according to the simulation data, obtaining the efficiency ratio of the current task to the current vehicle and the route through the simulation report, and optimizing the route according to the obtained efficiency ratio.

A fourth aspect of the present disclosure provides a computer readable storage medium having stored thereon a program which when executed by a processor performs the steps in the AGV route planning and scheduling method based on multidimensional data according to the first aspect of the present disclosure.

A fifth aspect of the present disclosure provides an electronic device comprising a memory, a processor and a program stored on the memory and executable on the processor, which when executed implements the steps in the AGV route planning and scheduling method based on multidimensional data according to the first aspect of the present disclosure.

Compared with the prior art, the beneficial effects of the present disclosure are:

1. the method, system, medium or electronic device described in the present disclosure integrates route planning of a vehicle into the entire driving process. Continuously updating the running path and time of the vehicle in the running process, and dynamically adjusting the running route according to the time; through a specific algorithm, the collision of routes is avoided as much as possible, and when the collision of the routes is encountered, the vehicle can avoid autonomously, so that the collision is solved; the method can effectively solve the problems of low route planning efficiency, easy conflict and easy deadlock at present.

2. The method, the system, the medium or the electronic equipment disclosed by the disclosure can truly simulate the execution efficiency of tasks and the utilization rate of vehicles based on a model of multidimensional data, and the departure time, the execution process and the ending time of each task can be clearly seen by utilizing the simulation function; the occupation time, the occupation rate and the like of each route can be seen; the problems that the current domestic dispatching system basically has no simulation function, the traditional calculation mode is utilized, the calculation amount is large, the calculation result is inaccurate, the display is not vivid and the like are solved.

3. The method, the system, the medium or the electronic equipment disclosed by the disclosure are used for carrying out composite processing on the acquired data, and calculating and improving the running route of the vehicle through a data model; according to the real-time running data of the vehicle, the model data is updated in real time, so that the dynamic planning and intelligent avoidance and unlocking of the vehicle are realized, and the accuracy of AGV route planning and scheduling is improved.

4. According to the method, the system, the medium or the electronic equipment, on the premise of the same task frequency, the same vehicle and the same task, the running efficiency of dynamic route planning and the common route planning are tested, the task completion time is improved by 25%, the vehicle waiting time is reduced by 40%, the common occupancy rate of the route is reduced by 20%, and the running efficiency is greatly improved.

Additional aspects of the disclosure will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the disclosure.

Drawings

The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate and explain the exemplary embodiments of the disclosure and together with the description serve to explain the disclosure, and do not constitute an undue limitation on the disclosure.

Fig. 1 is a diagram of a multi-dimensional data model provided in embodiment 1 of the present disclosure.

Fig. 2 is a schematic diagram of a route deadlock unlocking process provided in embodiment 1 of the present disclosure.

Fig. 3 is a route planning and operation flow chart provided in embodiment 1 of the present disclosure.

Fig. 4 is a simulation parameter setting and simulation result provided in embodiment 3 of the present disclosure.

Detailed Description

The disclosure is further described below with reference to the drawings and examples.

It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments in accordance with the present disclosure. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.

Embodiments of the present disclosure and features of embodiments may be combined with each other without conflict.

Example 1:

the embodiment 1 of the disclosure provides an AGV route planning and intelligent scheduling method based on multidimensional data, which comprises the following steps:

step S1: and (3) establishing a data model, wherein the model data at least comprises a route length, a vehicle running speed, a reversing speed, a steering speed, a minimum working electric quantity, a charging time, a loading time, an unloading time and an electric quantity consumption, as shown in fig. 1.

Step S2: and calculating the shortest running route of the vehicle by using the Floyd algorithm.

Step S3: and calculating the estimated occupied time and the release time of each section of route according to the model and the task to form a time sequence table.

Step S4: in the actual running process, the sequence table is updated in real time, so that the occupied time of the route is corrected and updated continuously in the theoretical and actual running.

Step S5: when a route conflict is encountered, a new route is dynamically planned, the expected time and the current waiting time of the new route are evaluated, and an optimal decision strategy is adopted.

Step S6: when the multi-AGV operates, a locking and avoiding algorithm of sectional integral locking, front-back locking and mutual deadlock preferential release is adopted, so that the vehicle can avoid dynamically.

Step S7: the system has the functions of route previewing and simulation.

In the step S1, a data model is built in an autonomously developed map designer, and a driving route, a passing stop node and the length and the direction (E, W, S, N, east, west, south and north) of each section of route of a vehicle are drawn; routes include straight lines and arcs, and different types of vehicles may have different traveling speeds when passing by.

Each section of route comprises a running direction, a starting point and a finishing point; one-way or two-way running can be set; establishing a vehicle model, wherein the vehicle model can set the running speed, the reversing speed, the steering speed, the minimum working electric quantity, the charging time, the loading time, the unloading time and the electric quantity consumption ratio of the vehicle; establishing a node model, wherein the node model can define the action and action time of the vehicle at the node; after the model is established, the expected running result of the vehicle can be calculated according to the model in future route planning and vehicle running.

More specifically, the data model contains basic information of the map:

s1.1: including map route models such as the length of the route, the speed of travel of the route vehicle, the direction of travel of the route (southwest, northwest) and one-way (one-way, two-way), and the measure of regulation of the route.

S1.2: the vehicle model comprises a map vehicle model, and can set the running speed, the reversing speed, the steering speed, the minimum working electric quantity, the charging time, the loading time, the unloading time and the electric quantity consumption ratio of the vehicle.

S1.3: the method comprises map nodes and action models, namely passing points, feeding and discharging points, avoiding points, decelerating points, accelerating points and action interaction points.

S1.4: the method comprises a task model, a task type, a distribution starting point, a task frequency and a vehicle requirement.

S1.5: and (5) a time simulation model, namely current time, time unit and time acceleration.

In step S3, according to the planned driving route, the vehicle running speed, and the loading time, the estimated starting occupation time, the release time, the duration, the occupied vehicle, and the occupied direction (starting point-ending point or ending point-starting point) of each section of route can be calculated, and an occupied time sequence table is formed:

when the primary task starts, the Folyd algorithm rule is adopted to draw the shortest route and the vehicle residence time of each route and point; each section of stay time adopts t=s/v, the running speeds of different routes and different vehicles can be different, and the stay time is calculated according to the actual vehicle type; the occupation time (loading and unloading time and interaction time) of each point is calculated, and a time sequence table is established.

When a task starts again, firstly planning a shortest path, checking whether the shortest path has an intersection with the path, if so, checking whether the shortest path runs in the same direction, if so, reversely running, planning a new route through a scheduling algorithm, evaluating the total running time of the new route and the running time of the original route, and making an optimal decision.

In step S4, in the running process of the vehicle, the time sequence table in the above step S3 is updated in real time; when a vehicle enters a certain route, the actual entering time deviates from the theoretical entering time, the system updates the starting time, the ending time, the occupied time and the like of all the segmented routes which are not completed in the follow-up process of the vehicle according to the actual time, the time sequence table is kept to coexist with the actual and theoretical time, and the occupied time of the route is corrected and updated continuously in the theoretical and actual running process.

The method specifically comprises the following steps:

when the vehicle actually runs, the actual running time and the theoretical time are different due to avoidance, temporary stopping, short abnormality and the like, so that the time of a subsequent route can be updated after each route is completed, and the time of all the follow-up routes is synchronously adjusted according to the current actual time.

The other vehicles share the route with the current task and re-evaluate the adjusted last arrival time, and if the arrival time exceeds a certain threshold, the route adjustment is triggered, thereby causing the travel route of the other vehicles to change.

In step S5, when a route conflict is encountered, a new route is dynamically planned, the expected time and the current waiting time of the new route are evaluated, and an optimal decision strategy is adopted.

Specifically, the multiple AGVs occupy the upcoming route during the travel; when the collision of the occupied routes occurs, a new route is re-planned, and the travel distance of the new route is larger than that of the previous route because the previous route adopts the shortest route calculated by the Folyd algorithm. The system will then evaluate the expected arrival time of the new route and the waiting and arrival times of the old route; when the new route time is better than the old route, a route switch is made.

In step S6, when the multi-AGV operates, a locking and avoiding algorithm of sectional integral locking, front-back locking and mutual deadlock preferential release is adopted, so that the vehicle can dynamically avoid:

as shown in fig. 2 (1), if a traveling task is a- > D, D- > A, B- > C, C- > B, and the vehicle starts at the same time, there are only 1 intersections, and a deadlock is inevitably caused when the vehicle starts.

The operation and release process is as follows:

step S6.1: locking to an intersection (a point connecting 2 or more lines, even if the intersection is a road), is performed after the locking is successful, and it is necessary to check which vehicle the route is locked by other vehicles if the locking is unsuccessful.

Step S6.2: if the locking is unsuccessful, if the occupied vehicle is in a mobile state, the vehicle is stopped and waits, and the locking is continuously attempted. If the occupied vehicle is in a stop state, the two dead locks need to be considered for unlocking.

Step S6.3: when unlocking, each process scans according to a unified rule. Associated vehicles that affect occupancy are found, such as the following map occupancy relationships A-C, B-C, C-B, D-C. Vehicles with deadlock are found out, such as B-C, C-B, the vehicles with deadlock act, and the vehicles without deadlock do not move first, as shown in (2) in figure 2.

Step S6.4: the two are mutually deadlocked, and according to the parking time, the avoidance of the vehicle is firstly parked, and the avoidance of the vehicle is firstly parked according to the figure B, and then the avoidance of the vehicle is carried out. If B has no route to avoid (assuming no branching nodes in the graph exist), another yield. Assuming that both deadlocks cannot yield, the avoidance rule of B, the avoidance route does not coincide with the route of C.

Step S6.5: after the center point is released, A, D walks next, regardless of who is acting, resulting in a deadlock, resulting in D-A, A-D. At this time, D dodges and then a acts according to the above rule, as shown in (3) of fig. 2.

Step S6.6: after the center point is released by the A, the B can continue to upwards, at the moment, the D occupies the center and fails, the B does not stop, the B can continue to walk, and the D stops and waits for continuing to occupy, so that the process is finished.

More specifically, as shown in fig. 3, by adopting the above scheme, the embodiment provides a planning and scheduling method for an AGV, which includes the following steps:

(1): if the current vehicle is idle and the vehicle model is matched with the type of task to be operated, assigning a task to the vehicle; after the vehicle is distributed, the running route of the vehicle is planned according to the starting point and the ending point of the current task section, and the entering time and the leaving time of each section of route are calculated preliminarily.

(2): after the route planning is finished, checking whether each route section formed by the route is occupied by other vehicles or not and the occupied direction; if the vehicle is occupied in the same direction, no conflict exists, the current vehicle can continue to occupy, and if the vehicle is occupied in different directions, the occupancy conflict exists; when the detection is occupied, the direction and the time are detected simultaneously; both are considered to be occupancy conflicts if they conflict.

(3): if the occupation conflict exists, setting the conflict route as non-passable, thereby planning other new routes, and if the passable route exists, evaluating the time of the new route; if the whole time of the new route is smaller than the current route, the new route is adopted. Otherwise, selecting a current route; and similarly, if no drivable route exists, selecting the current planned route as the driving route.

(4): the time is calculated for each route segment of the overall route, a time series is formed, and the route is locked at one level. The primary lock is a weak lock and only collisions are detected.

(5): performing a secondary lock, locking to the intersection; the secondary locking (of 2 or more lines connected at one point, namely the turnout) is strong locking, and after the secondary locking, other vehicles are forbidden to enter in a conflict time window; conflicts include time conflicts and direction conflicts.

(6): the vehicle starts to run, and before the vehicle runs, the front section and the rear section of routes are occupied, and after the vehicles are successfully occupied, the vehicle can run; the front and back occupation is exclusive locking, and the route is used for guaranteeing safe running of the AGV, and vehicles in any other directions are forbidden to enter the 2 routes after the locking.

(7): after the route is driven out, the next section is locked, and the current section is released. If the locking fails, parking detection is carried out to determine whether the locking is dead, and if the locking is dead, a dead lock relieving algorithm is executed; if not, the park waiting route is released.

It can be understood that the constructed data model has route previewing and simulation functions, after a map and a vehicle are built according to the model, a simulation order can be issued before actual running, the vehicle can simulate running according to set parameters, route occupation, release logic and time sequence update logic in the simulated running process are consistent with the actual running, the running efficiency of the vehicle can be estimated through the simulation running, and the decision making of a user is facilitated.

Example 2:

embodiment 2 of the present disclosure provides an AGV route planning and scheduling system based on multidimensional data, including:

The working method of the system is the same as the multi-dimensional data-based AGV route planning and scheduling method provided in embodiment 1, and will not be described here again.

Example 3:

embodiment 3 of the present disclosure provides an AGV route planning and scheduling simulation system based on multidimensional data, as shown in fig. 4, including:

Specifically, the method comprises the following steps:

(1) And establishing a model of basic data of system operation. The method comprises a vehicle information model, a vehicle running speed, a vehicle model, electric quantity consumption, charging time and the like; a route model comprising the type, direction, length, special running requirements of the route, etc.; loading a model, including loading time, unloading time, etc.; a charging model including a charging rate, a charging time, etc.; a special task model, time consumption of individual special tasks at special points, etc.; the model data is the basis for the next system operation and analysis.

(2) And establishing a task model. The method comprises the steps of starting points, distribution rules, stop stations, starting points, end points and the like corresponding to different kinds of tasks, and actions after the operation is finished; establishing task rules, such as elements contained in the task, contained actions, time consumption and the like; the frequency model of the task, the trigger frequency of the delivery task of different kinds.

(3) And (5) building a simulation environment. And drawing a vehicle running route map of the site by using a map designer according to the actual topography of the site. The route map includes information on each model such as a route and a vehicle.

(4) And executing motion simulation. And generating simulation tasks regularly according to the types and the frequencies agreed by the task model. And searching for idle vehicles to execute tasks according to the set vehicle model. Generating a vehicle driving route according to the task, updating route time, driving the vehicle to walk according to the route occupation and release rules, and presenting the vehicle driving route in an animation mode. During the running of the vehicle, data information related to the running, such as the start time of a task, the planned start time of a route, the actual start time, the planned end time, the actual end time and the like, are recorded. Multitasking, multiple vehicles may be performed in parallel.

(5) And checking the simulation report. The data generated during the operation of the vehicle is recorded by the system. The system collects and analyzes the original data to obtain a whole simulation report, and the whole simulation report is named as SystemRunDataInfo class; including task lists, order lists, route locking information lists, node locking information lists, loading information lists, vehicle running records, waiting information lists, etc.

In this embodiment, a time model may be further built, and a time acceleration multiple may be set to double the simulation time; through the established vehicle operation model, all instructions, states and other information related to the normal operation of the vehicle can be simulated.

When the system operates, the running effect of the vehicle is shown in an animation mode according to the route and the running speed of the vehicle, and meanwhile, model data are updated at any time to obtain simulation data of the vehicle.

The efficiency ratio of the current task to the current vehicle and the route can be analyzed through the obtained simulation report, so that optimization suggestions are given, such as adding the vehicle, reasonably distributing the task time and the like.

Example 4:

embodiment 4 of the present disclosure provides a computer readable storage medium having stored thereon a program which when executed by a processor performs the steps in the AGV route planning and scheduling method based on multidimensional data as described in embodiment 1 of the present disclosure.

Example 5:

embodiment 5 of the present disclosure provides an electronic device including a memory, a processor, and a program stored on the memory and executable on the processor, where the processor implements steps in the AGV route planning and scheduling method based on multidimensional data according to embodiment 1 of the present disclosure when executing the program.

It will be apparent to those skilled in the art that embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.

The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random access Memory (Random AccessMemory, RAM), or the like.

The foregoing description of the preferred embodiments of the present disclosure is provided only and not intended to limit the disclosure so that various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.

Claims

1. The AGV route planning and scheduling method based on the multidimensional data is characterized by comprising the following steps of:

guiding the AGV to operate according to the shortest driving route and the time sequence table, dynamically updating the time sequence table according to the current operation state and the operation time of the AGV, and carrying out route evaluation and/or adjustment of the AGV according to the updated time sequence table;

when a plurality of AGVs run, a locking and avoiding mode of sectional integral locking, front-back locking and mutual deadlock preferential release is adopted, and the method comprises the following steps:

after a time sequence table is formed, primary locking is carried out on the route, and the primary locking only detects conflict;

after the vehicle exits the route, locking the next section, releasing the current section, and if the locking is unsuccessful, stopping the vehicle and waiting if the occupied vehicle is in a running state, and continuously attempting to lock; if the occupied vehicle is in a stop state, considering the two to be deadlocked, and unlocking; if the vehicle is not deadlocked, the parking waiting route is released;

2. The multi-dimensional data based AGV route planning and scheduling method of claim 1, wherein the data model includes a map route sub-model, a map vehicle sub-model, a map node and action sub-model, a task sub-model, and a simulation sub-model.

3. The AGV route planning and scheduling method based on multi-dimensional data according to claim 1 wherein,

when an AGV starts a primary task, planning a shortest route and the vehicle residence time of each route and point according to the current position and the end position of the AGV;

if the intersection exists, checking whether the vehicle runs in the same direction, if the vehicle runs in the opposite direction, planning a new route, evaluating the total running time of the new route and the running time waiting time of the original route, and taking the route with the shortest time as the running route;

or alternatively, the process may be performed,

according to the updated time sequence table, synchronously adjusting the time of all the unfinished routes behind the current AGV;

4. The multi-dimensional data based AGV route planning and scheduling method according to claim 1, wherein after the route planning is completed, checking whether each route segment formed by the route is occupied by other vehicles and occupied direction, if the route segment is occupied in the same direction, the current vehicle can continue to occupy, and if the current vehicle is occupied in different directions and occupied time is in conflict, the current vehicle is regarded as occupied conflict;

5. An AGV route planning and scheduling system based on multidimensional data, comprising:

a planning scheduling module configured to: guiding the AGV to operate according to the shortest driving route and the time sequence table, dynamically updating the time sequence table according to the current operation state and the operation time of the AGV, and carrying out route evaluation and/or adjustment of the AGV according to the updated time sequence table;

6. An AGV route planning and scheduling simulation system based on multidimensional data, comprising:

7. A computer readable storage medium having stored thereon a program which when executed by a processor performs the steps of the multi-dimensional data based AGV route planning and scheduling method of any of claims 1-4.

8. An electronic device comprising a memory, a processor and a program stored on the memory and executable on the processor, wherein the processor performs the steps in the multi-dimensional data based AGV route planning and scheduling method of any of claims 1-4 when the program is executed.