Mobile robot path planning method based on improved butterfly optimization algorithm

Abstract

The invention discloses a mobile robot path planning method based on an improved butterfly optimization algorithm. The method comprises the following steps: modeling a working space of a mobile robot by using a grid method; setting initial parameters of a butterfly optimization algorithm; setting an initial position and a target position of an algorithm; searching an optimal path, initializing a taboo table and adding the taboo table into a butterfly starting position; calculating the intensity of fragrance generated at the current position of each butterfly, searching a subsequent transfer node by using the transfer probability, and stopping searching until the butterfly reaches the target position; judging whether the number of iterations of the algorithm reaches the maximum value or not, if yes, retaining the shortest path, and if not, continuing to perform path optimization until the optimal path is searched; smoothing the searched path, and outputting a smooth path; and enabling a simulation experiment to verify that the method can solve the problem of path planning of the mobile robot, and is an effective and feasible optimization algorithm.

Description

technical field [0001] The invention relates to the field of path planning of a mobile robot, in particular to a path planning method of a mobile robot based on a butterfly optimization algorithm. Background technique [0002] In recent years, path planning has become more and more important in the field of robotics. It mainly solves the problem of planning a collision-free path from the starting point to the target point for the robot in a known or unknown environment. At present, path planning is mainly studied in depth from two aspects: global path planning and local path planning. The research of global path planning is to achieve the shortest distance, the shortest time and the lowest energy consumption for the robot to reach the target position from the initial position on the basis of no collision. Common global path planning algorithms include A* algorithm, Dijkstra algorithm, particle swarm algorithm and ant colony algorithm. The research of local path planning is...

AI Technical Summary

Problems solved by technology

However, the butterfly optimization algorithm still has some problems in the path optimization process: invalid paths are generated, which affects the optimization efficiency; there is no subsequent expansion node in the optimization process, resulting in a "fake death" state of the robot; the path at the turning point is not smooth enough, which is not conducive to movement robot walking

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