Deterministic mobile robot path planning method

Abstract

The invention relates to the technical field of robot path planning methods, and specifically relates to a deterministic mobile robot path planning method. The method comprises four steps. Through improving a path fitness function, and increasing path and angle comprehensive penalty factors, the problem of local continuous steering in the path is effectively avoided. Through performing forward construction and reconstruction mutation operation on the antibody, empirical parameters such as evolution algebra, antibody quantity and mutation probability are prevented from being set. Deterministicsolution of iterative search is realized, redundant calculation is reduced, antibody reconstruction improvement rate is introduced, antibody adaptation values are estimated and screened, correlation between algorithm search space and antibody quantity is improved, and contradiction between algorithm determinacy and the search space is solved.

Description

technical field [0001] The invention relates to the technical field of robot path planning methods, in particular to a deterministic mobile robot path planning method. Background technique [0002] Path planning is an important direction of mobile robot research, which aims to plan a collision-free path from the starting position to the ending position in the working environment with obstacles through the path planning algorithm. In this regard, scholars at home and abroad have proposed many path planning algorithms, specifically, there are one-time search algorithms such as A* algorithm, Dijkstra algorithm, and artificial potential field algorithm, and iterative algorithms such as genetic algorithm, ant colony algorithm, particle swarm algorithm, and rapidly expanding random tree algorithm. search algorithm, but the A* algorithm uses global path planning. In the actual execution process, the local path is adjusted through the artificial potential field algorithm, which impr...

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Problems solved by technology

[0003] However, the optimization and improvement of the above algorithms are all based on the shortcomings of the algorithm itself. The operator improvement and algorithm fusion are carried out to obtain better solution results, and it does not fundamentally solve the contradiction between the search space and the search cost of the primary search algorithm. The iterative search algorithm The problems of premature and unstable search results still exist, therefore, those skilled in the art provide a deterministic mobile robot path planning method to solve the problems raised in the above background technology

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