CN114326708A - AGV (automatic guided vehicle) scheduling method based on space-time network model - Google Patents

Abstract

The invention discloses an AGV (automatic guided vehicle) scheduling method based on a space-time network model, which belongs to the technical field of scheduling operation and specifically comprises the following steps of: initializing data, and constructing a path model through a topological network; the method comprises the steps that an AGV executing a task searches for a shortest path, and information comparison is carried out between the shortest path and the remaining running paths of the AGV which do not finish order tasks before, so that whether conflicts and conflict types exist in the paths or not is judged; if a _ i is equal to b _ j, the AGV vehicles collide, and then the collision type is determined according to the path position and the AGV vehicles are untrusted. The method is based on a space-time network method, and describes possible conflicts in each AGV running path from two dimensions of time and space, constructs an AGV dynamic conflict detection and solving algorithm, dynamically identifies the AGV dynamic conflict detection and solving algorithm in the algorithm solving process, avoids the situation that a plurality of AGVs occupy a certain road section at the same time in the same time period, thereby identifying and detecting the possible path conflict problems of the AGVs, and tries to find a new path optimizing method to optimize the AGV running path.

Description

AGV (automatic guided vehicle) scheduling method based on space-time network model

Technical Field

The invention belongs to the technical field of scheduling operation, and particularly relates to an AGV scheduling method based on a space-time network model.

Background

The increase of the logistics area causes cost increase, becomes an important factor for limiting production capacity expansion and new vehicle type production, and each logistics planning department gradually puts the eye on how to utilize the upper space to realize the aim of intensive storage. Meanwhile, according to the concept of 'carrying is waste' and 'waste elimination', more and more logistics links adopt AGV carrying to replace the original manual carrying. The AGV is intelligent equipment for realizing horizontal operation, the intelligent degree of a scheduling system is high or low, and the operation efficiency of the automatic three-dimensional library is determined to a great extent.

Path planning research is always a hotspot and difficult problem of an AGV system, and scholars at home and abroad make outstanding contributions. The multiple AGV system path planning is more complex than a single-vehicle path planning algorithm, and experts and scholars propose a plurality of effective optimization algorithms for the optimization problems, wherein the most common algorithms comprise: genetic algorithm, particle swarm optimization algorithm, ant colony algorithm and the like.

The AGV path optimization is to determine a specific walking path scheme in a warehouse when the AGV executes, and to solve the path conflict among the AGV paths. The operation interference between AGVs mainly comprises the following three types:

(1) road section interference: in the straight line section, a plurality of AGV need to pass through the same straight line section, and the passing time of each AGV is overlapped;

(2) node interference: at the joint of the road sections, a plurality of AGV need to turn through the same line cross node, and the turning operation time is overlapped;

(3) operation interference: in the straight line section, the AGV rotates, jacks and other operations to the goods stacked beside the road section, so that other AGVs cannot pass through the road section.

Therefore, when the travel path scheme of the AGVs is determined, the interference relationship between the AGVs needs to be identified and solved from two angles of time and space.

(2) Currently existing workflow schemes

Most of the existing AGV conflict detection and dismissal methods are waiting strategies, namely, one AGV stops in advance and gives way in two conflicting AGV, and the AGV continues to operate after waiting for the other AGV to pass through.

The conflict resolution method can cause the reduction of the working efficiency of the AGV, thereby reducing the operation efficiency of the whole automatic warehouse.

Disclosure of Invention

In order to prevent the problems of continuous congestion, even system deadlock and the like of a subsequent AGV caused by overlong waiting time, the invention provides an AGV scheduling method based on a space-time network model; the method is based on a space-time network method, and describes possible conflicts in each AGV running path from two dimensions of time and space, constructs an AGV dynamic conflict detection and solving algorithm, dynamically identifies the AGV dynamic conflict detection and solving algorithm in the algorithm solving process, avoids the situation that a plurality of AGVs occupy a certain road section at the same time in the same time period, thereby identifying and detecting possible path conflict problems of the AGVs in the intelligent storage transportation system, tries to find a new path optimizing method, finds the shortest path in a new topology network, and optimizes the AGV running path.

The invention is realized by the following technical scheme:

an AGV scheduling method based on a space-time network model specifically comprises the following steps:

the method comprises the following steps: initializing data, and constructing a path model through a topological network;

step two: the AGV which is executing the task searches a shortest path (a _1, a _2, a _3, … a _ n) by utilizing Dijkstra algorithm, and compares the shortest path with the residual running paths (b _ i, b _ (i +1), b _ (i +2), … b _ n) of the AGV which does not finish the order task before, thereby judging whether conflicts and conflict types exist in the paths; the method comprises the steps that a is a path sequence of an AGV executing a task, b is a path sequence of an AGV not completing an order task before, n is a time sequence, n is an integer larger than 1, and i is smaller than n;

step three: if a _ i is equal to b _ j, the AGV vehicles collide, and then the collision type is determined according to the path position and the AGV vehicles are untrusted.

Further, the data initialization in the first step includes initialization of the AGV position, initialization of recovery road network information and initialization of an AGV task list.

Further, whether a conflict exists in the path in the step two is specifically determined as follows: firstly, whether a space conflict exists between the AGV executing the task and the AGV remaining running path which does not finish the order task before is judged, and if the space conflict exists, whether the occupied time of the nodes of the space conflict exists in other conflict types is further judged.

Further, the conflict types in the second step include road section interference, node interference and operation interference.

Further, the third step is as follows:

if conflict interference exists in the space, the AGV executing the task normally carries out, the AGV not completing the order task selects other paths to carry out detour evasion before, and selects the secondary short circuit again;

if conflict interference exists in time, the AGV which does not finish the order task before waits, the AGV which is executing the task normally runs, and the AGV which does not finish the order task before continues to run the residual running path after the AGV which is executing the task passes through the conflict node.

Compared with the prior art, the invention has the following advantages:

1. the method reduces the waiting time of the AGV, and avoids the conflict of the AGV paths through dynamic conflict detection;

2. the running efficiency of the automatic three-dimensional library is improved, and the running conflict of the AGV is avoided through an algorithm, so that the running efficiency of the automatic three-dimensional library is improved;

3. the method can adapt to the dynamic operation environment of the automatic three-dimensional warehouse, pre-judge the time of operation conflict generation and avoid the collision risk.

Drawings

In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.

FIG. 1 is a flowchart of an AGV scheduling method based on a spatiotemporal network model according to the present invention.

FIG. 2 is a schematic diagram showing the operation paths and time sequences of an AGV that is executing a task and an AGV that has not previously completed an order task according to embodiment 1 of the present invention;

fig. 3 is a schematic diagram showing the operation paths and time sequences of AGVs executing a task and AGVs not completing an order task before the conflict is resolved according to embodiment 1 of the present invention.

Detailed Description

For clearly and completely describing the technical scheme and the specific working process thereof, the specific implementation mode of the invention is as follows by combining the attached drawings of the specification:

in the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; they may be directly connected or indirectly connected through intervening media, or they may be connected internally or in any other suitable relationship, unless expressly stated otherwise. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.

In the present invention, unless otherwise expressly stated or limited, the first feature "on" or "under" the second feature may be directly contacting the first and second features or indirectly contacting the first and second features through an intermediate. Also, a first feature "on," "over," and "above" a second feature may be directly or diagonally above the second feature, or may simply indicate that the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature may be directly under or obliquely under the first feature, or may simply mean that the first feature is at a lesser elevation than the second feature.

In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.

The following embodiments are only used for illustrating the technical solutions of the present invention more clearly, and therefore, the following embodiments are only used as examples, and the protection scope of the present invention is not limited thereby.

It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which the invention pertains.

Example 1

On the basis of discretization of time and space, the invention uses a position-time space-time network and carries out quantitative description on the optimization problem of multiple AGV paths. The spatio-temporal network model has the advantages that the interference relation among the AGV traveling paths can be determined through pre-combing the interference arc sets, so that conflict and confusion are described in the model. According to the method, extra cost is added to the paths which conflict with each other in the topological network at fixed cost to form a new topological network, and the paths are re-planned in the established topological network aiming at the AGV vehicles of the current task to find the shortest path of the current network.

As shown in fig. 1, the present embodiment provides an AGV scheduling method based on a spatio-temporal network model, which specifically includes the following steps:

the method comprises the following steps: initializing data, and constructing a path model through a topological network;

the data initialization in the first step comprises the initialization of an AGV position, the initialization of the recovery road network information and the initialization of an AGV task list;

step two: the AGV which is executing the task searches a shortest path (a _1, a _2, a _3, … a _ n) by utilizing Dijkstra algorithm, and compares the shortest path with the residual running paths (b _ i, b _ (i +1), b _ (i +2), … b _ n) of the AGV which does not finish the order task before, thereby judging whether conflicts and conflict types exist in the paths; the method comprises the steps that a is a path sequence of an AGV executing a task, b is a path sequence of an AGV not completing an order task before, n is a time sequence, n is an integer larger than 1, and i is smaller than n;

and step two, whether the paths have conflicts or not is specifically judged as follows: firstly, judging whether a space conflict exists between the AGV executing a task and the residual running path of the AGV not completing an order task before, and if the space conflict exists, further judging whether the occupied time of the nodes of the space conflict exists in other conflict types;

step three: if a _ i is equal to b _ j, the AGV vehicles collide, and then the collision type is determined according to the path position and the AGV vehicles are untrusted.

The third step is as follows:

if conflict interference exists in the space, the AGV executing the task normally carries out, the AGV not completing the order task selects other paths to carry out detour evasion before, and selects the secondary short circuit again;

if conflict interference exists in time, the AGV which does not finish the order task before waits, the AGV which is executing the task normally runs, and the AGV which does not finish the order task before continues to run the residual running path after the AGV which is executing the task passes through the conflict node.

Example 2

As shown in fig. 2, by simply exemplifying the operation scheduling of two AGVs, the shortest path of the AGVs executing the task is (2_3, 3_4, 1_5, 4_6, 7_7, 6_8, 5_9), the remaining operation paths of the AGVs not completing the order task are (11_3, 10_4, 9_5, 8_6, 7_7, 6_8, 5_9), the two paths are compared, it can be determined from fig. 2 that the AGV executing the task and the AGVs not completing the order task all plan to access the path sequence 7-6-5 in the time sequences 7,8,9, and at this time, if the AGVs execute according to the planned AGV operation sequence, there is a problem of job interference between the two AGVs;

at this time, as shown in fig. 3, the job task of the AGV that is executing the task is preferentially executed, the waiting policy is executed for the AGVs that have not completed the order task before, one time unit is waited for, and after the AGV that is executing the task passes through the conflict time node, the AGVs that have not completed the order task before restart the job.

The preferred embodiments of the present invention have been described in detail with reference to the accompanying drawings, however, the present invention is not limited to the specific details of the above embodiments, and various simple modifications can be made to the technical solution of the present invention within the technical idea of the present invention, and these simple modifications are within the protective scope of the present invention.

It should be noted that the various technical features described in the above embodiments can be combined in any suitable manner without contradiction, and the invention is not described in any way for the possible combinations in order to avoid unnecessary repetition.

In addition, any combination of the various embodiments of the present invention is also possible, and the same should be considered as the disclosure of the present invention as long as it does not depart from the spirit of the present invention.

Claims (5)

1. An AGV scheduling method based on a space-time network model is characterized by comprising the following steps:

the method comprises the following steps: initializing data, and constructing a path model through a topological network;

step two: the AGV which is executing the task searches a shortest path (a _1, a _2, a _3, … a _ n) by utilizing Dijkstra algorithm, and compares the shortest path with the residual running paths (b _ i, b _ (i +1), b _ (i +2), … b _ n) of the AGV which does not finish the order task before, thereby judging whether conflicts and conflict types exist in the paths; the method comprises the steps that a is a path sequence of an AGV executing a task, b is a path sequence of an AGV not completing an order task before, n is a time sequence, n is an integer larger than 1, and i is smaller than n;

step three: if a _ i is equal to b _ j, the AGV vehicles collide, and then the collision type is determined according to the path position and the AGV vehicles are untrusted.

2. The AGV scheduling method according to claim 1, wherein the initialization of the data in step one includes initialization of AGV position, initialization of recovery road network information and initialization of AGV task list.

3. The AGV scheduling method according to claim 1, wherein in step two, whether there is a collision in the path is determined as follows: firstly, whether a space conflict exists between the AGV executing the task and the AGV remaining running path which does not finish the order task before is judged, and if the space conflict exists, whether the occupied time of the nodes of the space conflict exists in other conflict types is further judged.

4. The AGV scheduling method according to claim 1, wherein the conflict types in step two include section interference, node interference and operation interference.

5. The AGV scheduling method based on the spatiotemporal network model according to claim 1, wherein the third specific steps are as follows:

if conflict interference exists in the space, the AGV executing the task normally carries out, the AGV not completing the order task selects other paths to carry out detour evasion before, and selects the secondary short circuit again;

if conflict interference exists in time, the AGV which does not finish the order task before waits, the AGV which is executing the task normally runs, and the AGV which does not finish the order task before continues to run the residual running path after the AGV which is executing the task passes through the conflict node.

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