Abstract
In the field of robot path planning, aiming at the problems of the standard genetic algorithm, such as premature maturity, low convergence path quality, poor population diversity, and difficulty in breaking the local optimal solution, this paper proposes a multi-population migration genetic algorithm. The multi-population migration genetic algorithm randomly divides a large population into several small with an identical population number. The migration mechanism among the populations is used to replace the screening mechanism of the selection operator. Operations such as the crossover operator and the mutation operator also are improved. Simulation results show that the multi-population migration genetic algorithm (MPMGA) is not only suitable for simulation maps of various scales and various obstacle distributions, but also has superior performance and effectively solves the problems of the standard genetic algorithm.
Highlights
With the development of society, mobile robots are playing an increasingly important role in modern life
This paper proposes a path planning method based on the multi-population migration genetic algorithm (MPMGA)
We propose MPMGA based on the standard genetic algorithm
Summary
With the development of society, mobile robots are playing an increasingly important role in modern life. Mobile robots can autonomously move and operate according to different assigned tasks and have been widely used in the military, medical, manufacturing, entertainment, logistics and other fields [1,2,3]. The path planning problem is a hot topic in the field of robotics research. It requires robots to find an optimal or suboptimal path from the starting position to the target position according to some specific performance index (such as distance, time, etc) in a working environment with obstacles [4,5]. Path planning problems are generally divided into global path planning and local path planning. A path search is carried out in a known environment. Local path planning is relatively complex because the environment may be partially or completely unknown
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