Method for planning shortest path in robot obstacle avoidance

Abstract

The invention relates to a method for planning the shortest path in robot obstacle avoidance. The method comprises steps as follows: a probability grid map is acquired through convolution operation; an original A*algorithm is improved, and a cost function of the improved A*algorithm is obtained; on the basis of the probability grid map, the improved A*algorithm is adopted for shortest path searching from the starting point to the end point. The probability grid map is obtained through improvement of the original grid map, and the shortest path searching is performed in the improved A*algorithm on the basis of the probability grid map. According to a path planned with the method, a robot can avoid an obstacle for a certain distance and can more safely and reliably move.

Description

technical field [0001] The invention belongs to the technical field of robot obstacle avoidance, and in particular relates to a shortest path planning method in robot obstacle avoidance. Background technique [0002] Robot obstacle avoidance path planning refers to selecting a path from the starting point to the target point under the given environmental obstacle conditions, so that the robot can pass through all obstacles safely and without collision. This method of autonomously avoiding obstacles and completing tasks is an important content in the research and application of robots. At present, in order to quickly find an optimal or shortest path from the starting point to the target point, researchers have developed many different algorithms, such as A* algorithm, Dijkstra algorithm, genetic algorithm, particle swarm algorithm and artificial potential field law etc. [0003] Compared with A* algorithm, Dijkstra algorithm, genetic algorithm and particle swarm algorithm a...

AI Technical Summary

Problems solved by technology

The path planned by the artificial potential field method is generally smooth and safe, but this method has a local optimum, that is, the problem of local convergence is prone to occur; and when two obstacles are relatively close, according to the artificial potential field method According to the rules, the passage between them cannot pass through, so at this time, the path planning using the artificial potential field method either fails because the obstacle is too close, or it has to go around the obstacle, resulting in the planned path being too long

In addition, the paths planned by the artificial potential field method are mostly irregular curves, which do not conform to the movement habits of robots.

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