CN112697161A - AGV path planning method, storage medium and terminal - Google Patents
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Abstract
The invention relates to an AGV path planning method, a storage medium and a terminal, wherein the method comprises the following steps: s1: acquiring a map, and setting vertex angles of obstacles in the map as jumping points; s2: carrying out path searching by utilizing an improved A-x algorithm, and in the path searching process, if an expansion point comprises a jumping point, moving the expansion point by one unit in the oblique direction of the jumping point; s3: fitting the found path to obtain a fitting route; s4: and planning the AGV path by using the fitted route. Compared with the prior art, the method can reduce the calculated amount, improve the operation speed of the algorithm and realize the path planning quickly and effectively.
Description
Technical Field
The invention relates to the field of path planning, in particular to an AGV path planning method, a storage medium and a terminal.
Background
The path planning method is various, various methods are infinite, and various path planning algorithms are applied to the path planning of the AGV. For path planning of a known environment, the existing technologies include path planning of an artificial potential field method, Dijkstra algorithm and bfs (break First search) algorithm. The manual potential field method guides the AGV to reach the terminal point through the resultant force of the gravitational field and the repulsive field. The Dijkstra algorithm searches the periphery continuously by blindly diffusing until the target point is searched. The best-first-search (BFS) algorithm modifies the path by moving towards the target point when an obstacle appears in the direction of travel.
In a traditional algorithm, an artificial potential field method generates oscillation on a path between obstacles, the stability of the path is poor, and under a complex U-shaped scene, a local minimum value may exist, so that search is stopped and even a target point cannot be reached. The Dijkstra algorithm expands outwards in sequence through diffusion, the calculation amount is large, the requirement on hardware configuration is high, and the path searching time is long. The best-first-search (BFS) algorithm is not a blind search, selects the closest point to the target point, and continuously leads to the target point, which is shorter than Dijkstra algorithm in inter-route but has redundant paths.
The A-algorithm is the combination of the Dijkstra algorithm and the BFS algorithm, the defects of the Dijkstra algorithm and the BFS algorithm can be overcome, the A-algorithm continuously moves to a target point by continuously searching the node with the minimum cost value in a path, and the calculated amount is increased when the path is complex.
Disclosure of Invention
The present invention is directed to provide an AGV path planning method, a storage medium and a terminal, which overcome the above-mentioned drawbacks of the prior art.
The purpose of the invention can be realized by the following technical scheme:
an AGV path planning method comprises the following steps:
s1: acquiring a map, and setting vertex angles of obstacles in the map as jumping points;
s2: carrying out path searching by utilizing an improved A-x algorithm, and in the path searching process, if an expansion point comprises a jumping point, moving the expansion point by one unit in the oblique direction of the jumping point;
s3: fitting the found path to obtain a fitting route;
s4: and planning the AGV path by using the fitted route.
S2 is realized by an OPEN linked list and a CLOESD linked list, and S2 comprises:
s21: creating an OPEN linked list and a CLOSED linked list, putting a starting point into the OPEN linked list, and putting a jumping point into the CLOSED linked list;
s22: calculating the total cost of the nodes in the OPEN linked list, taking the node with the minimum total cost as a father node, and searching an expansion point of the node with the minimum total cost;
s23: putting the node with the minimum total cost into the CLOSED linked list, judging whether the expansion point comprises the node in the CLOSED linked list, if so, moving the expansion point to one unit in the oblique direction of the node in the CLOSED linked list, and executing S24, otherwise, executing S24;
s24: and putting the extension points into an OPEN linked list, and executing S22 until a termination condition is met.
The total cost f (n) is expressed as:
f(n)＝g(n)+h(n)
where g (n) is the path cost from the starting point to node n, and h (n) is the estimated cost from node n to the destination point.
The expression of g (n) is as follows:
where g (n-1) is the cost value from the origin to the parent of node n, xcurrentIs the abscissa, x, of node nfatherAbscissa of parent node of node n, ycurrentIs the ordinate, y, of node nfatherIs the ordinate of the parent node of node n.
The expression of h (n) is:
wherein x isgoalIs the abscissa, y, of the target nodegoalIs the ordinate, x, of the target nodecurrentIs the abscissa, y, of the node ncurrentIs the ordinate of node n.
The total cost is calculated based on euclidean distance.
In S3, when fitting the found path, it is checked whether there is an obstacle between the head and the tail of any adjacent three nodes, and if not, the intermediate node is removed.
In S3, a bezier curve is used for fitting.
A computer-readable storage medium, in which a computer program is stored, which, when loaded and executed by a processor, implements the AGV path planning method.
A terminal, comprising: a processor and a memory; wherein,
the memory is used for storing a computer program;
the processor is used for loading and executing the computer program so as to enable the terminal to execute the AGV path planning method.
Compared with the prior art, the invention has the following advantages:
(1) the vertex angle of the obstacle is taken as the jumping point of the path planning, the jumping point can provide space for the path fitting, the collision between the AGV trolley and the obstacle can be avoided, meanwhile, a large amount of unnecessary OPEN linked list and CLOSED linked list in the A-star algorithm are reduced, the operation speed of the algorithm is improved, the target point can be found quickly and effectively, and therefore the path planning is achieved.
(2) When the found path is fitted, whether an obstacle exists between the head node and the tail node in any three adjacent nodes is checked, if not, the middle node is removed, and the length of the running path is favorably reduced.
(3) The Bezier curve is adopted for fitting, and the path is smoothed, so that the time required by static steering can be reduced, and the trolley can run more stably.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic diagram of the path planning of the present invention;
FIG. 3 is a schematic diagram of the skip point identification of the present invention;
FIG. 4 is a schematic diagram of a fitting route according to the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Examples
The embodiment provides an AGV path planning method, as shown in fig. 1, the method includes the following steps:
s1: acquiring a map, and setting vertex angles of obstacles in the map as jumping points;
s2: carrying out path searching by utilizing an improved A-x algorithm, and in the path searching process, if an expansion point comprises a jumping point, moving the expansion point by one unit in the oblique direction of the jumping point;
s3: fitting the found path to obtain a fitting route;
s4: and planning the AGV path by using the fitted route.
Specifically, the method comprises the following steps:
in S1, the map is processed first to find a qualified skip point around the obstacle.
S2 is realized by an OPEN linked list and a CLOESD linked list, and S2 comprises:
s21: creating an OPEN linked list and a CLOSED linked list, initializing a starting point and a target point, putting the starting point into the OPEN linked list, and putting a jump point into the CLOSED linked list; the path cost g (n) of the starting point is 0.
S22: calculating the total cost of the nodes in the OPEN linked list, taking the node with the minimum total cost as a father node, and searching an expansion point of the node with the minimum total cost;
s23: putting the node with the minimum total cost into the CLOSED linked list, judging whether the expansion point comprises the node in the CLOSED linked list, if so, moving the expansion point to one unit in the oblique direction of the node in the CLOSED linked list, and executing S24, otherwise, executing S24;
s24: and (4) putting the extension points into an OPEN linked list, and executing S22 until a termination condition is met, wherein the termination condition can be as follows: and the node with the minimum total cost is the target point. After the target point is reached, backtracking finds all father nodes, and finally all the father nodes are connected, namely the shortest path.
The total cost f (n) is expressed as:
f(n)＝g(n)+h(n)
where g (n) is the path cost from the starting point to node n, and h (n) is the estimated cost from node n to the destination point.
The expression of g (n) is as follows:
where g (n-1) is the cost value from the origin to the parent of node n, xcurrentIs the abscissa, x, of node nfatherAbscissa of parent node of node n, ycurrentIs the ordinate, y, of node nfatherIs the ordinate of the parent node of node n.
The expression of h (n) is:
wherein x isgoalIs the abscissa, y, of the target nodegoalIs the ordinate, x, of the target nodecurrentIs the abscissa, y, of the node ncurrentIs the ordinate of node n.
The total cost is calculated based on euclidean distance.
In S3, when the found path is fitted, whether an obstacle exists between the head node and the tail node of any three adjacent nodes is checked, if not, the middle node is removed, multiple rounds of checking are carried out until no redundant node exists, and finally the remaining nodes are fitted by adopting a Bezier curve.
In fig. 2, the extension points are searched in four directions, i.e., up, down, left and right, from the starting point, when the jumping point is reached, the search is stopped, then the grid is moved in an oblique direction, and then the extension points are continuously searched in eight directions, i.e., horizontally and vertically.
In fig. 3, b2 is a simulated obstacle, and the squares where a1, a2 and b1 are located are freely movable square points. Considering the volume of the AGV car and the safety distance, a1 is set as a trip point, leaving a certain safety distance.
In fig. 4, the nodes are processed by the bezier curve, so that the path is smoother, the trolley runs more stably in actual running, and the time required for stationary steering is reduced.
The AGV path planning method of this embodiment has the following priority: and taking the volume of the AGV trolley in the actual situation into consideration, and taking the top angle of the obstacle as a jumping point of the path planning. The collision between the AGV trolley and the obstacle can be avoided, a space can be provided for the optimization of the Bezier path curve, and the obstacle in the path is avoided.
The present embodiment provides a computer-readable storage medium, in which a computer program is stored, and when the computer program is loaded and executed by a processor, the AGV path planning method is implemented.
The present embodiment further provides a terminal, including: a processor and a memory; wherein,
the memory is used for storing a computer program;
the processor is used for loading and executing the computer program so as to enable the terminal to execute the AGV path planning method.
Claims (10)
1. An AGV path planning method is characterized by comprising the following steps:
s1: acquiring a map, and setting vertex angles of obstacles in the map as jumping points;
s2: carrying out path searching by utilizing an improved A-x algorithm, and in the path searching process, if an expansion point comprises a jumping point, moving the expansion point by one unit in the oblique direction of the jumping point;
s3: fitting the found path to obtain a fitting route;
s4: and planning the AGV path by using the fitted route.
2. The AGV path planning method of claim 1, wherein S2 is implemented by an OPEN linked list and a CLOESD linked list, and S2 comprises:
s21: creating an OPEN linked list and a CLOSED linked list, putting a starting point into the OPEN linked list, and putting a jumping point into the CLOSED linked list;
s22: calculating the total cost of the nodes in the OPEN linked list, taking the node with the minimum total cost as a father node, and searching an expansion point of the node with the minimum total cost;
s23: putting the node with the minimum total cost into the CLOSED linked list, judging whether the expansion point comprises the node in the CLOSED linked list, if so, moving the expansion point to one unit in the oblique direction of the node in the CLOSED linked list, and executing S24, otherwise, executing S24;
s24: and putting the extension points into an OPEN linked list, and executing S22 until a termination condition is met.
3. The AGV path planning method according to claim 2, wherein the total cost f (n) is expressed as:
f(n)＝g(n)+h(n)
where g (n) is the path cost from the starting point to node n, and h (n) is the estimated cost from node n to the destination point.
4. The AGV path planning method according to claim 3, wherein the expression of g (n) is:
where g (n-1) is the cost value from the origin to the parent of node n, xcurrentIs the abscissa, x, of node nfatherAbscissa of parent node of node n, ycurrentIs the ordinate, y, of node nfatherIs the ordinate of the parent node of node n.
5. The AGV path planning method according to claim 3, wherein the expression of h (n) is:
wherein x isgoalIs the abscissa, y, of the target nodegoalIs a target ofOrdinate, x, of the nodecurrentIs the abscissa, y, of the node ncurrentIs the ordinate of node n.
6. The AGV path planning method according to claim 2, wherein the total cost is calculated based on euclidean distance.
7. The AGV path planning method according to claim 1, wherein in step S3, when matching the found path, checking whether there is an obstacle between the first and the last nodes of any adjacent three nodes, and if not, removing the middle node.
8. The AGV path planning method of claim 1, wherein in step S3, a Bezier curve is used for fitting.
9. A computer-readable storage medium, in which a computer program is stored which, when loaded and executed by a processor, carries out a method for AGV path planning according to any one of claims 1-8.
10. A terminal, comprising: a processor and a memory; wherein,
the memory is used for storing a computer program;
the processor is configured to load and execute the computer program to make the terminal execute the AGV path planning method according to any one of claims 1-8.
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