CN115016461A - Robot path planning method based on IA-Star algorithm of dynamic end point strategy - Google Patents
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Abstract
The invention relates to a robot path planning method based on an IA-Star algorithm of a dynamic end point strategy, wherein traditional nodes traversed by the A-Star are node clusters concentrated in the direction of a connecting line from a starting point to an end point, the result is better when the optimal path is closer to the connecting line from the starting point to the end point, and the optimal path is often not close to the connecting line from the starting point to the end point in practical application. The invention provides an improved strategy based on a dynamic end point, and the improved A-Star algorithm can dynamically adjust a local temporary end point in the process of traversing nodes, so that the algorithm is prevented from traversing invalid nodes, and the efficiency of the algorithm in completing a path planning task is improved. Simulation shows that the effect of adopting the novel strategy IA-Star algorithm on solving the problem of robot path planning is improved obviously, and the overall performance is superior to that of the basic A-Star algorithm and other improved A-Star algorithms.
Description
Technical Field
The invention relates to the technical field of robot path planning, in particular to a mobile robot path planning method based on an improved A-Star algorithm (improved A-Star, IA-Star).
Background
The path planning technology is an important component in the research field of mobile robots, and the main purpose is to seek an optimal or suboptimal safe collision-free path from a starting position node to a target position node according to certain criteria (such as shortest path, minimum position inflection point, shortest time and the like) in an environment with obstacles.
The development of the path planning technology marks the level of the intelligence of the robot to a certain extent, and the advantages and disadvantages of the path planning method directly influence the path planning effect.
At present, many experts and scholars at home and abroad are dedicated to the research of a path planning algorithm, and the commonly used optimization algorithms mainly comprise an artificial potential field method, an immune algorithm, an ant colony optimization algorithm, a neural network, an A-Star algorithm and the like.
The A-Star algorithm is the most effective direct search method for solving the shortest path in a static environment. The process of the a-Star algorithm to solve path planning can be summarized generally as: the node with the minimum evaluation index F is continuously traversed until the node is traversed to the terminal node, and then the optimal path is obtained by using a method of reversely searching a father node. The evaluation index F of a certain node is equal to the sum of two indexes, namely the actual path length G from the starting point to the node and the estimated path length H from the node to the end point. When solving the path planning under the grid graph model, it is common to define H as the sum of absolute values of differences between coordinates of the current node to the end point. However, this may cause the a-Star algorithm to traverse nodes, which are often a cluster of nodes centered on the direction of the connecting line from the starting point to the end point. Obviously, this can achieve better results when the optimal path is closer to the connection line from the starting point to the end point, but in practical applications, the optimal path is often not closer to the connection line from the starting point to the end point. Based on the existing problems, the invention provides an improved A-Star (IA-Star) algorithm based on a dynamic end point strategy, which is used for solving the problem of robot path planning. In the process of traversing nodes, the IA-Star dynamically adjusts local temporary end points, so that the algorithm is prevented from traversing some invalid nodes, and the efficiency of the algorithm for completing a path planning task is improved. A large number of simulation results show that the effect of the IA-Star algorithm adopting the novel strategy is obviously improved in solving the robot path planning problem, and the overall performance is superior to that of the basic A-Star algorithm and other improved A-Star algorithms.
Disclosure of Invention
The invention aims to provide a robot path planning method based on an IA-Star algorithm of a dynamic end point strategy, which can overcome the defect that the traditional A-Star is easy to fall into the traversal of local invalid nodes when traversing the nodes, reduce the traversal of the invalid nodes and further improve the solving speed of the algorithm.
Firstly, a concept of a dynamic endpoint is proposed, when the robot moves, the robot preferentially moves towards the endpoint in a default condition, if and only when the robot touches an obstacle when moving towards the endpoint, the robot needs to additionally spend a certain path cost to complete obstacle avoidance, so if the robot can predict that a certain obstacle exists on a forward route in advance, the robot has a primary purpose of bypassing the obstacle, at this time, the endpoint is not suitable for providing direction information, and therefore, in this case, the direction information is provided by the dynamic endpoint.
As shown in fig. 1, when the robot is ready to move from the starting point S to the end point E, it detects the existence of the obstacle B (mesh grid), and therefore, the robot needs to consider how to bypass the obstacle B first, and obviously, no matter how to bypass, the robot must pass through the boundary node B1 or B2 of the obstacle B, and therefore, the feasible adjacent nodes E2 and E3 of the boundary nodes B1 and B2 of the obstacle B are selected as dynamic end points to provide the robot with temporary guidance information. When the robot reaches a certain dynamic terminal, the dynamic terminal takes over the function of a new starting point, and then the operation is repeated until the robot reaches the real terminal.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a robot path planning method based on an IA-Star algorithm of a dynamic end point strategy comprises the following steps:
s1, initializing information required by the IA-Star algorithm, including but not limited to map data m, a starting point S and an end point E;
s2, creating a data table (DataFram, df) storing path data information, each piece of information having a data structure of a row vector with a size of 1 × 7, and labels of each column being a temporary start point, a dynamic end point, an end point, G, H, P, F, wherein G is a shortest path length from the temporary start point to the start point, H is an estimated path length from the temporary start point to the end point, which is a sum of absolute values of coordinate differences between the temporary start point and the end point, and P is a total estimated path length, which is equal to an unobstructed path length from the start point to the end point via the dynamic end point, noting that P is valid only when the dynamic end point exists; f is an evaluation index and is calculated from G + H + P;
initializing a path information in an S3 and Df data table, wherein a temporary starting point is S, a dynamic end point is None, and an end point is E, and the rest of data are calculated according to definitions;
s4, finding the path information with the minimum F value in the df data table, and if a plurality of pieces of information F are minimum and equal, taking the path information with the maximum G, marking as i, and executing S5;
s5, whether the temporary starting point in the path information i is equal to the end point or not is judged, if yes, S9 is executed; otherwise, go to S6;
s6, judging whether a dynamic terminal point exists in the information i, if so, executing S6.1; otherwise, go to S7;
s6.1, calling A-Star to calculate the path length L between the temporary starting point and the dynamic end point, updating the information according to the result, namely assigning the dynamic end point to the temporary starting point, resetting the dynamic end point, G being equal to L, recalculating H, P being equal to 0, recalculating F, and executing S4;
s7, judging whether a dynamic end point exists between the temporary starting point and the end point, if so, executing S7.1; otherwise, go to S8;
s7.1, adding two pieces of path information into the df data table, wherein the temporary starting point, the temporary end point and the data in the G and information i are the dynamic end points found in the S7, H is set to be 0, P and F are calculated according to the definition, the path information i is deleted, and S4 is executed;
s8, calling A-Star to calculate the distance l from the temporary starting point to the end point, and updating the data of the path information i: assigning the end point to a temporary starting point, enabling G < + > (l), H < + > (F) > to be 0, recalculating F, and executing S4;
and S9, outputting shortest path information and exiting the program.
The method has the advantages that by using the dynamic end point strategy, the probability of traversing invalid nodes is reduced when the IA-Star algorithm traverses the nodes, and therefore the solving speed of the algorithm is increased.
The invention is further illustrated with reference to the following figures and examples:
drawings
FIG. 1 is a schematic diagram of a dynamic endpoint in the present invention;
FIG. 2 is a flow chart of the IA-Star algorithm of the present invention;
FIG. 3 shows the results of the conventional A-Star algorithm;
FIG. 4 shows the result of the IA-Star algorithm of the present invention;
FIG. 5 shows the IA-Star algorithm operating results of the present invention;
the document of figure 6 improves the results of the running of the a-Star algorithm.
Detailed Description
The invention aims to provide a robot path planning method based on an IA-Star algorithm of a dynamic end point strategy, which can overcome the defect that the traditional A-Star is easy to fall into the traversal of local invalid nodes when traversing the nodes, reduce the traversal of the invalid nodes and further improve the solving speed of the algorithm.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a robot path planning method based on an IA-Star algorithm of a dynamic end point strategy comprises the following steps:
s1, initializing information required by the IA-Star algorithm, including but not limited to map data m, a starting point S and an end point E;
s2, creating a data table (DataFrame, df) storing path data information, each of which has a data structure of a row vector with a size of 1 × 7 and labels of each column as a temporary start point, a dynamic end point, an end point, G, H, P, F, wherein G is a shortest path length from the temporary start point to the start point, H is an estimated path length from the temporary start point to the end point, which is a sum of absolute values of coordinate differences between the temporary start point and the end point, and P is a total estimated path length, which is equal to an unobstructed path length from the start point to the end point via the dynamic end point, noting that P is valid only when the dynamic end point exists; f is an evaluation index and is calculated from G + H + P;
initializing a path information in the data table of S3 and Df, wherein the temporary starting point is S, the dynamic end point is None, and the end point is E, and the rest data are calculated according to the definition;
s4, finding the path information with the minimum F value in the df data table, if a plurality of pieces of information F are minimum and equal, taking the path information with the maximum G, marking as i, and executing S5;
s5, whether the temporary starting point in the path information i is equal to the end point or not is judged, if yes, S9 is executed; otherwise, go to S6;
s6, judging whether a dynamic terminal point exists in the information i, if so, executing S6.1; otherwise, go to S7;
s6.1, calling A-Star to calculate the path length L between the temporary starting point and the dynamic end point, updating the information according to the result, namely assigning the dynamic end point to the temporary starting point, resetting the dynamic end point, G being equal to L, recalculating H, P being equal to 0, recalculating F, and executing S4;
s7, judging whether a dynamic end point exists between the temporary start point and the end point, if so, executing S7.1; otherwise, go to S8;
s7.1, adding two pieces of path information into the df data table, wherein the temporary starting point, the temporary end point and the data in the G and G information i, the dynamic end point is the dynamic end point found in the S7, H is set to be 0, P and F are calculated according to the definition, the path information i is deleted, and S4 is executed;
s8, calling A-Star to calculate the distance l from the temporary starting point to the end point, and updating the data of the path information i: assigning the end point to a temporary start point, and recalculating F by making G + ═ l, H ═ F ═ 0, and executing S4;
and S9, outputting shortest path information and exiting the program.
The algorithm flow chart of the present invention is shown in fig. 2.
The method has the advantages that by using the dynamic end point strategy, the probability of traversing invalid nodes is reduced when the IA-Star algorithm traverses the nodes, and therefore the solving speed of the algorithm is increased.
The effect of the invention can be further illustrated by the following simulation experiments:
in order to verify the correctness and the rationality of the method, the algorithm is simulated under 4 20 × 20 grid environment models (the degree of deviation of the optimal solution path from the connecting line between the starting point and the ending point of M1-M4 is gradually increased) by using python language programming, and the algorithm is compared with the traditional A-Star algorithm. The simulation result is shown in fig. 3, fig. 4 and table 1 (where the red grids in fig. 3 and fig. 4 are traversed nodes, and the blue is an optimal path node obtained by the algorithm), with the upper-left corner node as a starting point and the lower-right corner node as an end point.
TABLE 1 Algorithm Experimental data comparison
As can be seen from the data in table 1, when the optimal path is close to the connection line between the start point and the end point, there is no significant difference between the traversal efficiency (the number of nodes included in the optimal path/the number of all traversal nodes) and the operation speed of the IA-Star of the present invention and the conventional a-Star, but as the optimal path deviates from the connection line between the start point and the end point to a greater extent, the IA-Star of the present invention has significant improvement effect regardless of the traversal efficiency or the operation speed.
In order to further verify the effectiveness of the improved algorithm proposed by the present invention, the present invention is compared with another improved a-Star algorithm, which is an improved genetic algorithm described in journal "computer integrated manufacturing system" in 2022, 1-17 pages "optimize a" and dynamic window method mobile robot path planning ", the simulation is performed by using the method of the present invention under the grid environment condition of 50 × 50 in the article, and the experimental results are compared, as shown in fig. 5, fig. 6 and table 2.
TABLE 2 comparison of simulation results
As can be seen from the data in Table 2, the optimal path result obtained by the IA-Star algorithm of the present invention is 75.3259, which is better than the result 76.1543 obtained by the improved document algorithm, and the number of traversal nodes of the algorithm of the present invention is 225, which is significantly less than the number of traversal 262 of the improved document algorithm. Therefore, compared with the literature algorithm, the IA-Star algorithm has advantages in the aspects of solution finding effect and traversal efficiency.
The comparison simulation experiment can be used for drawing the conclusion that: the path planning efficiency of the IA-Star algorithm is obviously superior to that of the traditional A-Star algorithm. Compared with the traditional A-Star algorithm and an A-Star algorithm improved by others, the IA-Star algorithm provided by the invention has good effect and high speed, which shows that the IA-Star algorithm provided by the invention has certain feasibility and practicability in the aspect of path optimization.
The foregoing is merely a preferred embodiment of the invention and is not intended to limit the invention in any manner; those skilled in the art can make numerous possible variations and modifications to the present teachings, or modify equivalent embodiments to equivalent variations, without departing from the scope of the present teachings, using the methods and techniques disclosed above. Therefore, any simple modification, equivalent replacement, equivalent change and modification made to the above embodiments according to the technical essence of the present invention are still within the scope of the protection of the technical solution of the present invention.
Claims (1)
1. A robot path planning method based on an IA-Star algorithm of a dynamic end point strategy comprises the following steps:
s1, initializing information required by the IA-Star algorithm, including but not limited to map data m, a starting point S and an end point E;
s2, creating a data table (DataFram, df) storing path data information, each piece of information having a data structure of a row vector with a size of 1 × 7, and labels of each column being a temporary start point, a dynamic end point, an end point, G, H, P, F, wherein G is a shortest path length from the temporary start point to the start point, H is an estimated path length from the temporary start point to the end point, which is a sum of absolute values of coordinate differences between the temporary start point and the end point, and P is a total estimated path length, which is equal to an unobstructed path length from the start point to the end point via the dynamic end point, noting that P is valid only when the dynamic end point exists; f is an evaluation index and is calculated by G + H + P;
initializing a path information in an S3 and Df data table, wherein a temporary starting point is S, a dynamic end point is None, and an end point is E, and the rest of data are calculated according to definitions;
s4, finding the path information with the minimum F value in the df data table, if a plurality of pieces of information F are minimum and equal, taking the path information with the maximum G, marking as i, and executing S5;
s5, whether the temporary starting point in the path information i is equal to the end point or not is judged, if yes, S9 is executed; otherwise, go to S6;
s6, judging whether a dynamic terminal point exists in the information i, if so, executing S6.1; otherwise, go to S7;
s6.1, calling A-Star to calculate the path length L between the temporary starting point and the dynamic end point, updating the information according to the result, namely assigning the dynamic end point to the temporary starting point, resetting the dynamic end point, G being equal to L, recalculating H, P being equal to 0, recalculating F, and executing S4;
s7, judging whether a dynamic end point exists between the temporary starting point and the end point, if so, executing S7.1; otherwise, go to S8;
s7.1, adding two pieces of path information into the df data table, wherein the temporary starting point, the temporary end point and the data in the G and information i are the dynamic end points found in the S7, H is set to be 0, P and F are calculated according to the definition, the path information i is deleted, and S4 is executed;
s8, calling A-Star to calculate the distance l from the temporary starting point to the end point, and updating the data of the path information i: assigning the end point to a temporary starting point, enabling G < + > (l), H < + > (F) > to be 0, recalculating F, and executing S4;
and S9, outputting shortest path information and exiting the program.
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| WO2017041730A1 (en) * | 2015-09-09 | 2017-03-16 | 北京进化者机器人科技有限公司 | Method and system for navigating mobile robot to bypass obstacle |
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| WO2022007350A1 (en) * | 2020-07-08 | 2022-01-13 | 格力电器（武汉）有限公司 | Global path planning method and apparatus, terminal, and readable storage medium |
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| WO2017041730A1 (en) * | 2015-09-09 | 2017-03-16 | 北京进化者机器人科技有限公司 | Method and system for navigating mobile robot to bypass obstacle |
| WO2022007350A1 (en) * | 2020-07-08 | 2022-01-13 | 格力电器（武汉）有限公司 | Global path planning method and apparatus, terminal, and readable storage medium |
| CN113341975A (en) * | 2021-06-08 | 2021-09-03 | 长沙理工大学 | Robot moving method based on variable objective function firefly optimized path |
Non-Patent Citations (2)
| Title |
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