CN112525196A

CN112525196A - AGV route planning and scheduling method and system based on multidimensional data

Info

Publication number: CN112525196A
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: 2021-03-19
Anticipated expiration: 2040-11-23
Also published as: CN112525196B

Abstract

The utility model provides an AGV route planning and scheduling method and system based on multidimensional data, which comprises the steps of obtaining the running data and the state data of the AGV and constructing a data model according to the obtained data; calculating the shortest driving route of the AGV according to the current position and the end point position of the AGV; according to the data model and the operation task, acquiring the occupation time and the removal time of each route section to form a time sequence table; the AGV runs according to the shortest running route and the time sequence table, dynamically updates the time sequence table according to the current running state and running time, and evaluates and/or adjusts the route of the AGV according to the updated time sequence table; the method carries out composite processing on the acquired data, and calculates and improves the running route of the vehicle through a data model; and the model data is updated immediately according to the real-time running data of the vehicle, so that the dynamic planning and intelligent avoidance and unlocking of the vehicle are realized, and the accuracy of the 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 technology application fields which are extremely valued at home and abroad at present. Flexible processing, flexible assembly, warehouse logistics automation are important components. In manufacturing production lines, assembly (such as automobiles and parts thereof) production lines and warehouse access conveying lines, AGV equipment is increasingly used for conveying and distributing materials, but with the increase of the AGV equipment, the scheduling and the optimization of the use efficiency of the AGV vehicles become more and more important problems. How to plan and optimize the running route of the AGV and reasonably arranging and scheduling the vehicles become targets continuously pursued by various manufacturers.

The inventors of the present disclosure have discovered that current algorithms for routing AGVs are well developed, such as the commonly used Dijkstra algorithm, Floyd algorithm, a-algorithm, and optimization and variations of these algorithms. The optimal route for the vehicle to travel is conveniently calculated using these algorithms. However, the operation of multiple AGVs is a complex process requiring coordination, and when multiple vehicles share the same route, problems such as conflict of occupied routes exist, which leads to a sharp drop of the operation efficiency of the vehicles. There are many studies, optimizing on existing route planning algorithms, and some algorithms also add time estimation. Such as: a dynamic multi-path planning algorithm based on the A-star algorithm. However, in actual operation, the time plan is determined to be constantly disturbed by service scenes such as vehicle faults, vehicle obstacle avoidance, field loading and unloading and the like, even if dynamic time updating is added, only the notification is obtained, the vehicle operation delay is obtained, and real-time dynamic planning and scheduling of the AGV route cannot be realized in time; moreover, the conventional AGV scheduling usually starts from a route algorithm optimization angle or simple model data analysis, and solves the problem of the operation efficiency of the AGV vehicles, but the solution has great limitation, the field operation is a complex and variable process, and any analysis or algorithm improvement based on a single element cannot simultaneously solve the problem of the vehicle scheduling.

Disclosure of Invention

In order to solve the defects of the prior art, the method and the system for planning and scheduling the AGV route based on the multidimensional data are provided, the acquired data are subjected to composite processing, and on one hand, the running route of a vehicle is calculated and improved through a data model; on the other hand, the model data is updated in real time according to the real-time running data of the vehicle, dynamic planning and intelligent avoidance and unlocking of the vehicle are achieved, and accuracy of AGV route planning and scheduling is improved.

In order to achieve the purpose, the following technical scheme is adopted in the disclosure:

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

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

according to the data model and the operation task, acquiring the occupation time and the removal time of each route section to form a time sequence table;

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

As some possible implementations, 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.

As some possible implementation manners, when an AGV starts a task for the first time, planning the shortest route and the vehicle stop time of each route and point according to the current position and the end position of the AGV;

when a task starts again, a shortest route is planned according to the current position and the end position of the AGV, and whether the shortest route intersects with a previous route or not is checked;

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

As some possible implementation manners, synchronously adjusting the time of all unfinished routes behind the current AGV according to the updated time sequence table;

and the AGV sharing the route with the current task reevaluates the adjusted final arrival time, and triggers the route adjustment if the arrival time exceeds a preset threshold value.

As some possible implementation manners, after the route planning is finished, whether each route segment formed by the route is occupied by other vehicles and the occupied direction is checked, if the route segment is occupied in the same direction, the conflict is avoided, the current vehicle can be continuously occupied, and if the route segment is occupied in the different direction and the occupied time conflicts, the current vehicle is regarded as the occupied conflict;

if an occupation conflict exists, setting the conflict route as unviable so as to plan other new routes, and if a drivable route exists, evaluating the time of the new route;

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

and if the drivable route does not exist, selecting the current planned route as the driving route.

As some possible implementation modes, after a time sequence table is formed, primary locking is carried out on the route, and only conflict is detected by the primary locking;

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

the vehicle starts to run, the front and rear routes are occupied before running, the vehicle can run after the front and rear occupancy is exclusive locking, and the vehicles in any other direction are forbidden to enter the two routes after locking;

and after the route is driven out, locking the next section, releasing the current section, if the locking fails, stopping to detect whether the deadlock occurs, if the deadlock occurs, executing a deadlock release algorithm, and if the deadlock does not occur, stopping to wait for the route release.

As some possible implementation manners, when multiple AGVs operate, a locking and avoiding manner of overall locking by segments, front-back locking and mutual deadlock preferential release is adopted, which includes:

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

when the locking is unsuccessful, if the occupied vehicle is in an action state, stopping the vehicle for waiting, and continuing to try to lock; if the occupied vehicle is in a stop state, the occupied vehicle and the occupied vehicle are subjected to deadlock, and unlocking is carried out;

during unlocking, each process scans according to a uniform rule, finds out related vehicles influencing occupation, finds out vehicles which are mutually deadlocked and act as the deadlocked vehicles, and the non-deadlocked vehicles are firstly immobile;

and if the parking is performed first, the vehicle is stopped first without an avoidance route, and the vehicle is stopped first and runs first.

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

a data model building module configured to: acquiring running 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 driving route of the AGV according to the current position and the end point position of the AGV;

a temporal sequence table generation module configured to: according to the data model and the operation task, acquiring the occupation time and the removal time of each route section to form a time sequence table;

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

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

a simulation data acquisition module configured to: displaying the running effect of the vehicle in an animation mode according to the route and the running speed of the vehicle, 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: and obtaining a simulation report according to the simulation data, obtaining the efficiency ratio of the current task to the current vehicle to the current 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, on which a program is stored, which when executed by a processor, implements the steps in the method for AGV route planning and scheduling based on multidimensional data according to the first aspect of the present disclosure.

A fifth aspect of the present disclosure provides an electronic device, which includes a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of 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 effect of this disclosure is:

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

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

3. The method, the system, the medium or the electronic equipment disclosed by the disclosure performs composite processing on the acquired data, and calculates and improves the running route of the vehicle through a data model; and the model data is updated immediately according to the real-time running data of the vehicle, so that the dynamic planning and intelligent avoidance and unlocking of the vehicle are realized, and the accuracy of the 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.

Advantages of 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 embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.

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

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

Detailed Description

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

It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the 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 example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.

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

Example 1:

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

step S1: and establishing a data model, wherein model data at least comprises a route length, a vehicle running speed, a reversing speed, a steering speed, the lowest working electric quantity, charging time, loading time, unloading time and electric quantity consumption, and is shown in figure 1.

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

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

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

Step S5: when a route conflict occurs, 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 multiple AGVs operate, the vehicles can be dynamically avoided by adopting a locking and avoiding algorithm of segmented integral locking, front and back locking and mutual deadlock preferential release.

Step S7: the route preview and simulation functions are provided.

In step S1, data model building is performed in an autonomously developed map designer, and a driving route, a passing node, a length of each route, and a direction of the vehicle are drawn (E, W, S, N east west south north); the route comprises straight lines and arc lines, and different types of vehicles can have different driving speeds when passing.

Each route comprises a driving direction, a starting point and an end point; unidirectional or bidirectional driving can be set; establishing a vehicle model, wherein the vehicle model can set vehicle running speed, reversing speed, steering speed, lowest working electric quantity, charging time, loading time, unloading time and electric quantity consumption ratio; establishing a node model, wherein the action and the action time of a vehicle at the node can be defined in the node model; after the model is established, the expected operation result of the vehicle can be estimated according to the model in future route planning and vehicle operation.

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

s1.1: the method comprises a map route model, such as the length of a route, the running speed of a vehicle on the route, the running direction (south-east-west-north) and one-way and two-way (one-way passing, two-way passing) of the route and a control measure of the route.

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

S1.3: the map node and action model are used for generating a map node and an action model, wherein the map node and action model comprise a passing point, a feeding and discharging point, an avoiding point, a decelerating point, an accelerating point and an action interaction point.

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

S1.5: time simulation model, current time, time unit, time acceleration.

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

when a first task starts, a shortest route and the vehicle residence time of each route and point are planned by using a Folyd algorithm; each section of stay time adopts t ═ s/v, the running speeds of different routes and different vehicles can have differences, and the difference is calculated according to the actual vehicle types; and calculating the occupied time (material loading and unloading time and interactive action time) of each point, and establishing a time sequence table.

When a task starts again, firstly planning a shortest path, checking whether the shortest path and the path have intersection, if so, checking whether the shortest path is in the same-direction driving, if the shortest path and the path have intersection, planning a new path through a scheduling algorithm, evaluating the total operation time consumption of the new path and the operation time consumption of the original path plus the waiting time consumption, and making an optimal decision.

In step S4, the time sequence table in step S3 is updated immediately while the vehicle is running; when a vehicle enters a certain section of route, the actual entering time and the theoretical entering time have deviation, the system updates the starting time, the ending time, the occupation time and the like of all the subsequent incomplete sectional routes of the vehicle according to the actual time, keeps the coexistence of the time sequence table and the actual and theoretical time, and continuously corrects and updates the occupation time of the route in the theoretical and actual operation.

The method specifically comprises the following steps:

the situation that the actual running time is different from the theoretical time due to avoidance, temporary parking, transient abnormity and the like during the actual running of the vehicle can be caused, so that the time of a subsequent route can be updated after each route is finished, and the time of all the incomplete routes in the future can be synchronously adjusted according to the current actual time.

Other vehicles sharing the route with the current mission may reevaluate the adjusted last arrival time, and if the arrival time exceeds a certain threshold, the route adjustment may be triggered, resulting in a change in the travel route of the other vehicle.

In step S5, when a route conflict occurs, 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, in the driving process of multiple AGVs, the routes to be entered are occupied; when the route occupation conflict occurs, a new route can be planned again, and the driving distance of the new route is larger than that of the previous route because the shortest route calculated by a Folyd algorithm is adopted in the previous route. At this point the system will evaluate the expected arrival time of the new route and the wait 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 multiple AGVs are running, a locking and avoidance algorithm that is segmented and integrally locked, front-back locked, and mutual deadlocks are preferentially released is adopted to allow a vehicle to dynamically avoid:

as shown in (1) in fig. 2, when a driving task is started, a- > D, D- > A, B- > C, C- > B, and only 1 intersection is left, deadlock is inevitably caused when the vehicle is started.

The operation and release processes are as follows:

step S6.1: locking to a turnout (one point connecting 2 or more lines, even if the turnout) is performed after the locking is successful, and the failure of the locking needs to check which vehicle the route is locked by.

Step S6.2: if the locking is unsuccessful, if the occupied vehicle is in an action state, the vehicle is parked and waits, and the locking is continuously tried. If the occupied vehicle is in a stop state, deadlock of the occupied vehicle and the occupied vehicle needs to be considered, and unlocking is carried out.

Step S6.3: and when unlocking, scanning by each process according to a uniform rule. And finding out related vehicles influencing occupancy, such as the occupancy relationships A-C, B-C, C-B, D-C in the following graph. Vehicles that are deadlock with each other, such as B-C, C-B, are found to act as deadlock vehicles with each other, not deadlock vehicles first, as shown in (2) of FIG. 2.

Step S6.4: and the parking is mutually deadlock, and the vehicle is parked firstly according to the parking time, and the vehicle is parked firstly as shown in the figure B, and the vehicle B is avoided at the moment. If B has no routes to avoid (assuming no branching nodes exist in the graph), another yields. And (4) assuming that both deadlocks can not yield, the avoidance rule of B ensures that 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 forming D-A, A-D. As shown in (3) of fig. 2, D dodges and then a acts according to the above rule.

Step S6.6: after the central point is released by the A, the B can continue to move upwards, at the moment, the D fails to occupy the central point, the B does not stop and can continue to walk, and the D stops to wait for continuous occupation until the whole process is finished.

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

(1): if the current vehicle is idle and the model of the vehicle is matched with the type of the task to be operated, distributing the task to the vehicle; after the vehicles are distributed, vehicle driving routes are planned according to the starting point and the end point of the current task segment, and the entering and leaving time of each route segment is preliminarily calculated.

(2): after the route planning is finished, checking whether each route segment formed by the route is occupied by other vehicles or not and the occupation direction; if the vehicle is occupied in the same direction, conflict does not exist, the current vehicle can continue to be occupied, and if the vehicle is occupied in a different direction, occupation conflict exists; when the occupation is detected, the direction and the time can be simultaneously detected; if both conflict, the user is considered to be occupied conflict.

(3): if an occupation conflict exists, setting the conflict route as unviable so as to plan other new routes, and if a drivable route exists, evaluating the time of the new route; and if the overall time of the new route is less than that of the current route, adopting the new route. Otherwise, selecting the current route; and similarly, if no drivable route exists, selecting the current planned route as the driving route.

(4): time is calculated for each route segment of the overall route, a time series is formed, and primary locking is performed on the route. The first level lock is a weak lock and only conflicts are detected.

(5): executing secondary locking to the fork; the secondary locking (one point is connected with 2 lines or more, even if the two lines are at a fork) is strong locking, and after the secondary locking is locked, other vehicles are prohibited from entering the collision time window; conflicts include time conflicts and direction conflicts.

(6): the vehicle starts to run, the front and rear routes are occupied before running, and the vehicle can run after the front and rear routes are occupied successfully; the front and back occupancy is exclusive locking, the route is used for guaranteeing safe driving of the AGV, and vehicles in any other direction are prohibited from entering the 2 routes after the locking.

(7): and after the vehicle leaves the route, locking the next section and releasing the current section. If the locking fails, parking and detecting whether the vehicle is deadlock, and if the vehicle is deadlock, executing a deadlock release algorithm; if not, the parking waits for the route to be released.

The method has the advantages that the established data model has the functions of route rehearsal and simulation, after the map and the vehicle are established according to the model, a simulation order can be issued before actual operation is carried out, the vehicle can simulate driving according to set parameters, route occupation, release logic and time sequence updating logic in the process of simulated driving are consistent with those of actual operation, the operation efficiency of the vehicle can be evaluated through simulated operation, and the method is more beneficial to user decision making.

Example 2:

the 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 AGV route planning and scheduling method based on multidimensional data provided in embodiment 1, and is not described here again.

Example 3:

an 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, the running speed of a vehicle, the model of the vehicle, the electric quantity consumption, the charging time and the like; a route model including the type, direction, length of the route, special driving requirements of the route, etc.; loading models including time of loading, time required for unloading, and the like; a charging model including a charging rate, a charging time, and the like; a special task model, time consumption of individual special tasks at a special point, etc.; the model data is the basis for further system operation and analysis.

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

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

(4) And executing motion simulation. And generating the simulation task at regular time according to the type and frequency appointed by the task model. And searching for an idle vehicle to execute the task according to the set vehicle model. And generating a vehicle driving route according to the task, updating the route time, driving the vehicle to travel according to the route occupation and release rules, and presenting the route in an animation mode. During the running process of the vehicle, data information related to the running is recorded, such as the starting time of a task, the planned starting time of a route, the actual starting time, the planned ending time, the actual ending time and the like. Multitasking and multiple vehicles can be performed in parallel.

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

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

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

The efficiency ratio of the current task, the current vehicle and the current route can be analyzed through the obtained simulation report, so that optimization suggestions are given, for example, vehicles are added, the task time is reasonably distributed, and the like.

Example 4:

the embodiment 4 of the present disclosure provides a computer-readable storage medium, on which a program is stored, which when executed by a processor implements the steps in the AGV route planning and scheduling method based on multidimensional data according to the embodiment 1 of the present disclosure.

Example 5:

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

As will be appreciated by one skilled in the art, 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, 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 flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes 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 (RAM), or the like.

The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.

Claims

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

and guiding the AGV to run according to the shortest running route and the time sequence table, dynamically updating the time sequence table according to the current running state and running time of the AGV, and evaluating and/or adjusting the route of the AGV according to the updated time sequence table.

2. The 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 scheduling method according to claim 1, wherein said AGV comprises a first AGV having a first travel path and a second travel path,

when an AGV starts a primary task, planning a shortest route and vehicle stopping 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 or not, if the vehicle runs in the reverse direction, planning a new route, evaluating the total running time consumption of the new route and the running time consumption waiting time consumption of the original route, and taking the route with the shortest time consumption as the running route;

or,

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

4. The AGV routing scheduling method according to claim 1, wherein after routing is completed, checking whether each route segment formed by the route is occupied by other vehicles and the occupied direction, if so, there is no conflict, the current vehicle can continue to be occupied, and if so, there is a conflict between the occupied directions and the occupied time, the current vehicle is regarded as an occupied conflict;

5. The AGV route planning scheduling method according to claim 1, wherein said AGV comprises a first AGV having a first travel path and a second travel path,

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

6. The AGV route planning and scheduling method according to claim 1, wherein when multiple AGVs operate, a locking and avoiding mode of segmented overall locking, front-back locking and mutual deadlock preferential release is adopted, and the method comprises the following steps:

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

a planning scheduling module configured to: and guiding the AGV to run according to the shortest running route and the time sequence table, dynamically updating the time sequence table according to the current running state and running time of the AGV, and evaluating and/or adjusting the route of the AGV according to the updated time sequence table.

8. An AGV route planning and scheduling simulation system based on multidimensional data is characterized by comprising:

9. A computer-readable storage medium, on which a program is stored, which program, when being executed by a processor, carries out the steps of the method for AGV route planning based on multidimensional data according to any one of claims 1 to 7.

10. An electronic device comprising a memory, a processor and a program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of the method according to any one of claims 1-7.