A Deterministic Path Planning Method for Mobile Robots

Abstract

The present invention relates to the technical field of robot path planning methods, specifically a path planning method for a deterministic mobile robot, including four steps. The present invention improves the path fitness value function and increases the path and angle comprehensive penalty factor, effectively avoiding the For the problem of local continuous turning in the path, through the forward construction and reconstruction mutation operation of the antibody, it avoids setting the evolution algebra, the number of antibodies, the mutation probability and the number of clones, and realizes the deterministic solution of iterative search, reducing redundant calculations , using the introduction of the antibody reconstruction improvement rate, the antibody fitness value is estimated and screened, and then the correlation between the algorithm search space and the number of antibodies is improved, and the contradiction between the algorithm certainty and the search space is resolved.

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. It 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 one-time search algorithms such as A* algorithm, Dijkstra algorithm, artificial potential field algorithm, and iterative algorithms such as genetic algorithm, ant colony algorithm, particle swarm algorithm and fast expanding random tree algorithm. search algorithm, but the A* algorithm adopts global path planning. In the actual execution process, the artificial potential field algorithm is used to adjust the local path, which improves the practical app...

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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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