Autonomous Mobile Robot Navigation Based on PSO Algorithm with Inertia Weight Variants for Optimal Path Planning

摘要

Abstract Motion planning is an important domain since its performance can significantly affect the utilization of robots. This paper addresses our work to developing a path planner for wheeled a mobile robot using a swarm Intelligence technique for optimal path planning within a short computational time to get better path planning results. Through this technique, we developed particle swarm optimization (PSO) for generating fast and optimal path planning. Inertia weight technique is used for performance comparison of PSO Algorithms to get optimal path planning within a complex environment, PSO with a time-varying mechanism for the inertia weight values (TV-IWPSO), to analyze the performance proposed approach on the of PSO algorithm performance. Finally, the comparison has been done in between TV-IWPSO with both particle swarm optimization with constant inertia weight (B-PSO), and standard particle swarm optimization (S-PSO), in two different maps to performing analysis for algorithms through various environments. The simulation results, which carried out using Matlab 2018a showed that the PSO algorithm with inertia-weight strategy made good results for generating optimal path planning and efficiently than (S-PSO) and (B-PSO) in terms of path distance, execution-time