Path Planning for Robot Using the Particle Swarm Optimization with Self-adaptive Inertia Weight
Jian Liu, Weisheng Wu
Journal of Information Security Research, Vol. 9, No. 2, 2018.
DOI: https://doi.org/10.6025/jisr/2018/9/2/41-49
Link: http://www.dline.info/jisr/fulltext/v9n2/jisrv9n2_1.pdf
Abstract:
This works aims at the problems of poor local searching capability and slow convergence speed existing in the current robot path planning method. Based on particle swarm optimization (PSO) algorithm, this paper presents a novel
approach using self- adaptive inertia weight particle swarm optimization. This study utilizes the grid method to establish
space model and is based on the particle swarm optimization algorithm. Furthermore, self-adaptive adjustment inertia
weight and a novel method of fitness function are introduced to improve particle swarm optimization algorithm to get the
globally optimal path of the robot. The experiments indicate that the algorithm can find a simple and secure optimal path
between the starting point and the end points. Compared with other classical methods, this algorithm has faster search speed
and better convergence.