CN112833905A - Distributed multi-AGV collision-free path planning method based on improved A-x algorithm - Google Patents
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Abstract
The invention discloses a distributed AGV collision-free path planning method based on an improved A-algorithm, which comprises the steps of planning paths by using the improved A-algorithm and performing collision processing by using a grid density method, and by establishing a resource scheduling method, a plurality of AGV automatic guided transport vehicles are enabled to aim at the shortest time, so that the high-efficiency operation is realized, the collision and deadlock are solved, the turning times are reduced, and the sorting task is completed cooperatively.
Description
Technical Field
The invention belongs to the technical field of multi-agent path planning, relates to a distributed automatic sorting task path planning technology of a plurality of Automatic Guided Vehicles (AGVs) in a logistics system, and particularly relates to a distributed multi-AGV collision-free path planning method based on an improved A-star algorithm.
Background
The logistics speed, logistics service and logistics cost have great influence in the purchasing stage of network consumers, and the most considered logistics factors are the consumers when finally determining to purchase commodities. The logistics speed has positive influence on the payment intention of the consumer, wherein the distribution speed is more appreciated by the consumer, and the concern of the e-commerce logistics service enterprises on the distribution speed is meaningful. With the continuous development of electronic commerce and the prevalence of technologies such as internet of things and automation, logistics gradually become intelligent and efficient from traditional inefficiency.
Specifically, after a consumer places an order online, an automatic warehouse retrieval system (AR/RS) selects a commodity required by the consumer according to an order, transports the commodity to a packaging area through a conveyor belt to complete packaging, and then performs a sorting operation by an Automatic Guided Vehicle (AGV). The AGV is provided with an automatic guide mechanism which can guide the AGV to run along a given path and can also correct a running route according to a new instruction so as to complete the moving and taking function of materials or goods. The operation quality of the logistics system is related to the economic effect of the storage system, and a cluster type AGV system (AGVs for short) formed by a plurality of AGVs has the absolute advantages of labor saving, high efficiency, safety and good usability. AGVs have important significance for reducing the workload of workers, improving the warehouse efficiency, enhancing the competitiveness of enterprises and the like.
AGVs include a plurality of AGVs, and there is a path network shared by the AGVs, etc., resulting in resource competition between the AGVs, thereby creating a need for resource scheduling. The greater the number of AGVs, the greater the difficulty of resource scheduling. At present, two methods, namely centralized control and distributed control, are used for controlling the AGV. The prior art mostly adopts centralized control, and has the advantages of simple structure, convenient control and the like. However, as the system scale increases, the management and computation pressure faced by the management center in centralized control becomes a bottleneck. The distributed control can avoid the problem that the calculation pressure of a control center is increased suddenly under the condition that the number of the AGVs is increased, and the distributed control has the advantages of good flexibility, good real-time performance, easiness in expansion and the like, and the system has better robustness.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a distributed multi-AGV collision-free path planning method based on an improved A-x algorithm, and aims to establish a resource scheduling strategy in a logistics system and an AGV transport vehicle path planning method, realize collision-free and efficient operation of multiple AGVs, and avoid the phenomena of collision, deadlock and the like.
The method mainly comprises two aspects of path planning and collision processing, and the multiple AGV trolleys are enabled to run efficiently by aiming at the shortest time through establishing a related resource scheduling method, so that the problems of conflict and deadlock are solved reasonably, the turning times are reduced as far as possible, and the sorting task is completed cooperatively.
In order to achieve the purpose, the invention adopts the following technical scheme:
a distributed AGV collision-free path planning method based on an improved A-x algorithm is disclosed. The improved A-x algorithm mainly solves the problem of planning multiple AGV paths, and the grid density method mainly solves the problem of conflict among various AGVs. The method comprises the following steps:
step one, modeling a warehouse field by adopting a grid map method, and dividing an AGV running space into a plurality of grid type areas, wherein the grid type areas comprise an idle area, a loading port, a delivery port, a temporary obstacle area and an area where the AGV is located.
And step two, planning the running track for the AGV according to the improved A path planning algorithm to obtain the AGV planning initial running track.
The initial running track is a path from the current position of the AGV to the task loading position and a path from the task loading position to the task delivery position;
based on a classical path planning algorithm A, a search termination condition and a heuristic function are modified, the path search efficiency is improved, the steering times are reduced, and conflicts are avoided. The method comprises the following operation processes:
1) acquiring grid map information, an AGV starting position and target position information;
2) defining an OPEN list for storing node information to be processed later and a CLOSE list for storing the processed node information, wherein both lists are initialized to be empty lists;
3) searching a path from a starting point, namely adding the starting point into an OPEN list;
4) judging whether the OPEN list is empty, namely judging whether a node to be processed exists; if the node to be processed exists, processing is continued; otherwise, ending the operation, and showing that no path exists;
5) judging whether the node to be processed contains a terminal, namely judging whether the terminal is in an OPEN list; if not, continuing the processing, if yes, indicating that the path searching is successful, and ending the operation;
6) selecting a node U with the minimum cost in the nodes to be processed, and further searching and processing the node U;
7) after the processing is finished, the node U is placed in a CLOSE list;
and obtaining the AGV planning initial running track.
And step three, each AGV runs according to the planning result. If conflict occurs in the operation process, an in-place waiting strategy is preferentially adopted; and if the deadlock is detected, replanning, finding the grid of the next position of the path planning which can not be reached all the time due to the deadlock, virtually setting the grid as a temporary obstacle, and replanning on the basis. Only the AGV with the deadlock detected firstly is planned, so that the time waste and secondary even cyclic deadlock caused by replanning a plurality of trolleys are avoided.
Compared with the prior art, the invention has the beneficial effects that:
compared with the prior art, the method is stable and reliable, can reduce the turning times compared with the original A-star algorithm, and can solve various conflicts in a multi-vehicle scene. The distributed AGV collision-free path planning method based on the improved A-algorithm provides a set of available solutions for all problems of distributed multi-AGV scheduling tasks of an intelligent warehouse.
Drawings
Fig. 1 is a flow chart of a method for obtaining a basic path by using an improved a-algorithm provided by the present invention.
FIG. 2 is a block flow diagram of a distributed multiple AGV collision-free path planning method based on the improved A-algorithm provided by the present invention.
FIG. 3 is a schematic diagram illustrating four types of traffic path conflicts in an embodiment of the present invention;
wherein, (a) is an encounter conflict; (b) is an occupancy conflict; (c) is a conflict in opposite directions; (d) to overcome the conflict.
FIG. 4 is a schematic view of an AGV operation mode in accordance with an embodiment of the present invention; a, B, C, D show the four directions of travel of the AGV.
Fig. 5 is a schematic diagram of a result of modeling a grid map of a warehouse site in an embodiment of the invention.
FIG. 6 is a schematic illustration of a selected warehouse layout in an embodiment of the present invention, illustrating the initial position of each AGV, the loading and delivery locations for a sort task.
FIG. 7 is a diagram illustrating a path search selected according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
The invention provides a distributed multi-AGV collision-free path planning method based on an improved A-x algorithm, which aims at minimizing time by establishing a resource scheduling strategy in a logistics system and an AGV transport vehicle path planning method, realizes collision-free and efficient operation of AGVs, avoids the phenomena of collision, deadlock and the like, reduces the turning times as far as possible, and completes a sorting task cooperatively. Fig. 2 shows a flow of a distributed multiple AGV collision-free path planning method based on the improved a-x algorithm, which includes the following steps:
step one, modeling a warehouse field by adopting a grid map method, and dividing an AGV running space into a plurality of grid type areas, wherein the grid type areas comprise an idle area, a loading port, a delivery port, a temporary obstacle area and an area where the AGV is located.
The warehouse field is the AGV operation space. The AGV running space is divided into a plurality of simple areas (grids) by adopting a grid map method, and each time the AGV moves, one grid is taken as a unit. The size of the grid is set to be the same as the size of the model of the AGV, so that the grid is not excessively divided, the storage capacity of the environmental information is small, and the path planning is not clear; the division is not too small, so that the precision is high, the searching time is too long, and the efficiency is reduced. The types of the grids comprise an idle area, a loading port, a delivery port, a temporary barrier area and an area where an AGV is located, and the specific description is as follows:
1) and (4) idle area: freely available trolley active area.
2) A loading port: all AGVs may select a starting position.
3) A delivery port: and selecting target positions by all the AGVs, starting from the loading port, and starting an intelligent delivery device by any grid near the target delivery port to finish one-time delivery work of the vehicle-mounted cargos. The non-AGV target delivery port is regarded as a barrier in the running process of the AGV.
4) Temporary obstacle area: there is a position of the AGV that needs to be occupied for a long time to complete the task.
5) The area where the AGV is located: the current map is where all AGVs are located.
The grid map information obtained by modeling the warehouse site includes the plurality of grid type regions. Fig. 5 shows the results of a grid map modeling under a small (size: 11 x 11) warehouse.
And step two, planning the initial running path from the current position of the AGV to the task loading position and the running path from the task loading position to the task delivery position for each AGV according to the improved A path planning algorithm.
FIG. 1 shows a method flow for obtaining a base path using the modified A-algorithm; the steps included in the specific method are described by taking as an example that two AGV carts (AGV-1, AGV-2) in the four-way operation mode cooperatively complete a sorting TASK (TASK-1, TSAK-2) generated at two different times (t is 1, t is 2). The four-way mode of operation is shown in fig. 4. The warehouse layout, the initial position of each AGV, and the loading and delivery locations for the sort jobs are shown in FIG. 6.
1) Acquiring grid map information, an AGV starting point (starting position) and target position information;
2) defining an OPEN list for storing node information to be processed later, a CLOSE list for storing processed node information, and initializing both lists into a null list;
3) starting to search for a path from the starting point (add the starting point to the OPEN list);
4) judging whether a node to be processed exists (namely judging whether the OPEN list is empty) or not, if so, continuing processing, and if not, ending;
5) and judging whether the node to be processed contains the end point (namely judging whether the end point is in the OPEN list), if not, continuing processing, and if so, indicating that the path searching is successful, and ending.
6) And selecting the node U with the minimum cost in the nodes to be processed, and further searching and processing the node U.
7) And after the processing is finished, putting the node U into a CLOSE list.
And obtaining the AGV planning initial running track.
The basic paths of the AGV-1 and the AGV-2 to the corresponding task loading ports are found as follows:
the processing conditions of the OPEN list and the CLOSE list corresponding to the AGV-1 are as follows:
the final path is: 1,4,7, 10, 13, 16, 19
The processing conditions of the OPEN list and the CLOSE list corresponding to the AGV-2 are as follows:
the final path is: 21, 22, 24, 29, 32, 37, 47,6,4,8, 50, 53, 56, 59
The calculation method of the node cost in the step two 6) is specifically that the cost of the node is equal to the cost of the node n from the starting point plus the estimated cost (expressed by manhattan distance) of the node n from the end point plus the steering cost and the grid density cost, and can be expressed by the following formula:
fn＝gn+hn+is_turn*t+density*d
wherein f isnRepresentative node Integrated priority, gnRepresenting the cost of node n from the origin, hnRepresenting the expected cost of node n from the end point. is _ turn t represents the steering penalty and dense d represents the grid density penalty.
In particular, the method comprises the following steps of,
is _ turn represents whether steering is generated, and the generation is set to 1, and corresponding steering cost is added; if not, the value is set to 0, and no steering cost is generated.
t represents a steering parameter (which is set to be 0 and is consistent with the original A-star algorithm), the steering inhibiting effect can be generated when the value is more than or equal to 1, and the inhibiting effect is more obvious when the value is larger. the meaning of t is equivalent to: the path length that is willing to travel more to avoid one turn. So if the parameters are set too large, it will cause the algorithm to waste a lot of time searching for paths with fewer turns, so it is generally reasonable to set them in the interval [1,3 ].
The balance of the path planning between the shortest path and the path with the minimum inflection point can be guided by adjusting the steering parameter t, so that the automatic smoothing of the path can be realized, and finally, the turning times of the path can be reduced.
Density represents the grid at the expected arrival time tarriveThe predicted time-density value represents the probability of a collision that is expected to occur at the planned arrival time of the grid. The larger the estimated value of the density of the grid at the expected arrival time is, the more likely the collision will occur when the point is selected as the planned path and the point is operated to.
The weighted average sum of the number of AGV carts that arrive on the grid at a time near the estimated arrival time may be used, for example, as represented by the following equation:
wherein N istIndicating the number of vehicles expected to reach the grid at time t. All weights should be 1, 0.2,0.7,0.1, which is a set of commonly used weights and can be fine-tuned according to the actual situation, and generally, the weight corresponding to the expected arrival time is the largest.
And guiding the balance of path planning between the shortest path and the minimum conflict path by adjusting the size of the grid density value parameter, so that the conflict relief of the path can be realized.
The specific content of the search processing in the step two 6) comprises: traversing the adjacent (four-way) reachable nodes nb of the node U;
if node nb has been previously processed (i.e., in the CLOSE list), then no processing is required;
if the node nb is a new node (i.e., not in the CLOSE list or in the CLOSE list), calculating the cost of nb according to the specific calculation method of the node cost, then selecting the node nb from the node U to reach (i.e., making the parent node of the node nb be the node U), and adding the node nb into the node to be processed (i.e., adding the OPEN list);
if node nb is waiting to be processed (i.e., in the OPEN list), then the way to reach node nb is updated is the less expensive one of reaching nb from node nb from its original parent and reaching nb from node U;
and planning the AGV path through the steps to obtain the AGV initial running track.
And step three, various conflict problems possibly encountered by the AGV in the operation process are processed according to a waiting and replanning method. The method comprises the following steps:
when the front running position of the AGV is blocked by other AGVs, communicating with the AGV blocking the road in front to judge whether the AGV needs long-term in-situ operation, if the AGV needs the long-term in-situ operation, re-planning a new path from the current position to the terminal by the current AGV, or else, selecting to wait for the front AGV to leave and pass;
if the path is not re-planned, then wait in place is also selected.
Through the process, the collision-free path planning of the distributed AGV based on the improved A-x algorithm is realized.
It is noted that the disclosed embodiments are intended to aid in further understanding of the invention, but those skilled in the art will appreciate that: various substitutions and modifications are possible without departing from the spirit and scope of the invention and appended claims. Therefore, the invention should not be limited to the embodiments disclosed, but the scope of the invention is defined by the appended claims.
Claims (9)
1. A distributed AGV collision-free path planning method based on an improved A-algorithm comprises the steps of planning paths by using the improved A-algorithm and performing collision processing by using a grid density method, and enabling a plurality of automatic guided transport vehicles AGV to achieve efficient operation by taking the shortest time as a target through establishing a resource scheduling method, solving collision and deadlock, reducing the number of turns and cooperatively completing sorting tasks; the method comprises the following steps:
step one, modeling an AGV running space field by adopting a grid map method, and dividing the AGV running space into a plurality of grid type areas, wherein the grid type areas comprise an idle area, a loading port, a delivery port, a temporary barrier area and an area where the AGV is located;
planning an initial running track for the AGV by adopting an improved A path planning algorithm to obtain the planned initial running track of the AGV; the initial running track is a path from the current position of the AGV to the task loading position and a path from the task loading position to the task delivery position;
by modifying the search termination condition and the heuristic function of the A-path planning algorithm, the path search efficiency is improved, the steering times are reduced, and the conflict is avoided; the method comprises the following operation processes:
21) acquiring grid map information, an AGV starting position and target position information;
22) defining an OPEN list for storing node information to be processed later and a CLOSE list for storing the processed node information, wherein both lists are initialized to be empty lists;
23) searching a path from a starting point, namely adding the starting point into an OPEN list;
24) judging whether the OPEN list is empty, namely judging whether a node to be processed exists; if the node to be processed exists, processing is continued; otherwise, ending the operation, and showing that no path exists;
25) judging whether the node to be processed contains a terminal, namely judging whether the terminal is in an OPEN list; if not, continuing the processing, if yes, indicating that the path searching is successful, and ending the operation;
26) selecting a node U with the minimum node cost from the nodes to be processed, and further searching and processing the node U;
27) after the processing is finished, the node U is placed in a CLOSE list;
obtaining an AGV planning initial running track;
step three, each AGV runs according to the planning result, and the conflict problem of the AGV in the running process is processed according to the waiting and re-planning method;
if conflict occurs in the operation process, an in-place waiting strategy is preferentially adopted;
if deadlock is detected in the operation process, replanning, finding out a grid at the next position of the path planning which can not be reached all the time due to deadlock, virtually setting the grid as a temporary obstacle, and replanning; only planning the AGV which detects the deadlock firstly;
through the steps, the collision-free path planning of the distributed AGV based on the improved A-x algorithm is realized.
2. The method according to claim 1, wherein in step one, the AGV moves one grid at a time; the grid is sized to be the same size as the AGV.
3. The method for distributed AGV collision-free path planning based on the modified a-algorithm according to claim 1, wherein in step one,
the idle area is a freely available trolley moving area;
the loading port is an optional initial position of all the AGVs;
the delivery port is the target position selectable by all AGV, and the delivery work of one-time vehicle-mounted goods is completed by starting from the loading port and starting the intelligent delivery device to any grid near the target delivery port; the non-AGV target delivery openings are regarded as obstacles in the running process of the AGV;
the temporary obstacle area is a position where an AGV exists which needs to be occupied for a long time to complete a task;
the area where the AGVs are located is where all of the AGVs are located on the current map.
4. The improved a-algorithm based distributed AGV collision-free path planning method according to claim 1, wherein in step two, the node cost is equal to the sum of the cost of node n from the start point, the estimated cost of node n from the end point, the steering cost, and the grid density cost.
5. The method for distributed AGV collision-free path planning based on the improved a-algorithm according to claim 4, wherein the node cost is calculated by:
fn＝gn+hn+is_turn*t+density*d
wherein f isnRepresentative node Integrated priority, gnRepresenting the cost of node n from the origin, hnRepresenting the expected cost of node n from the end point. is _ turn t represents the steering penalty, dense d represents the grid density penalty; t represents a steering parameter.
6. The improved a algorithm-based distributed AGV collision-free path planning method of claim 5, wherein when a turn is generated, is _ turn is set to 1, plus the corresponding turn cost; otherwise, when the steering is not generated, the is _ turn is set to be 0, and the steering cost is not generated; the value of t is set to [1,3 ].
7. The method of claim 5, wherein the diversity is a weighted average sum of the AGV carts arriving at the grid at a time near the estimated arrival time, and is expressed as follows:
wherein N istIndicating the number of vehicles expected to reach the grid at time t.
8. The method according to claim 1, wherein in step two, the search process specifically includes the following steps:
traversing reachable nodes nb adjacent to the four directions of the node U;
if the node nb is processed before, namely the node nb is in the CLOSE list, the processing is not needed;
if the node nb is a new node, namely the node nb is not in the CLOSE list and is not in the CLOSE list, calculating the cost of nb, then selecting a node U to reach the node nb, namely making the father node of the node nb be the node U, and adding the node nb into the node to be processed, namely adding the node nb into the OPEN list;
if node nb is waiting to be processed, i.e. node nb is in the OPEN list, then the arriving node is updated in such a way that it is less costly to arrive nb from the nb node's original parent and nb from node U.
9. The method of claim 1, wherein the waiting and replanning method in step three comprises:
when the front running position of the AGV is blocked by other AGVs, communicating with the AGV blocking the road in front, and judging whether the AGV blocking the road in front needs long-term in-situ operation or not;
if the AGV blocking the road in front needs long-term in-situ operation, the current AGV replans a new path from the current position to the terminal point; otherwise, selecting to wait for the front AGV to leave for passing again;
if the path is not re-planned, wait in place.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004280213A (en) * | 2003-03-13 | 2004-10-07 | Japan Science & Technology Agency | Distributed path planning device and method, and distributed path planning program |
| WO2017173990A1 (en) * | 2016-04-07 | 2017-10-12 | 北京进化者机器人科技有限公司 | Method for planning shortest path in robot obstacle avoidance |
| CN107990903A (en) * | 2017-12-29 | 2018-05-04 | 东南大学 | A kind of indoor AGV paths planning methods based on improvement A* algorithms |
| CN110530388A (en) * | 2019-09-05 | 2019-12-03 | 苏宁云计算有限公司 | The paths planning method and system of more AGV |
| CN110989570A (en) * | 2019-10-15 | 2020-04-10 | 浙江工业大学 | Multi-AGV anti-collision collaborative path planning method |
| CN111289005A (en) * | 2020-03-16 | 2020-06-16 | 江苏理工学院 | Path finding method based on A star optimization algorithm |
| CN111596658A (en) * | 2020-05-11 | 2020-08-28 | 东莞理工学院 | Multi-AGV collision-free operation path planning method and scheduling system |
| WO2020181729A1 (en) * | 2019-03-08 | 2020-09-17 | 江苏海事职业技术学院 | Path planning method employing distributed parallel computation and system thereof |
| CN111708364A (en) * | 2020-06-19 | 2020-09-25 | 南京理工大学 | Improved AGV path planning method based on A-x algorithm |
| CN112034836A (en) * | 2020-07-16 | 2020-12-04 | 北京信息科技大学 | Mobile robot path planning method for improving A-x algorithm |
-
2021
- 2021-01-08 CN CN202110022264.7A patent/CN112833905B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004280213A (en) * | 2003-03-13 | 2004-10-07 | Japan Science & Technology Agency | Distributed path planning device and method, and distributed path planning program |
| WO2017173990A1 (en) * | 2016-04-07 | 2017-10-12 | 北京进化者机器人科技有限公司 | Method for planning shortest path in robot obstacle avoidance |
| CN107990903A (en) * | 2017-12-29 | 2018-05-04 | 东南大学 | A kind of indoor AGV paths planning methods based on improvement A* algorithms |
| WO2020181729A1 (en) * | 2019-03-08 | 2020-09-17 | 江苏海事职业技术学院 | Path planning method employing distributed parallel computation and system thereof |
| CN110530388A (en) * | 2019-09-05 | 2019-12-03 | 苏宁云计算有限公司 | The paths planning method and system of more AGV |
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