Optimal Multi-robot Path Planning Using Particle Swarm Optimization Algorithm Improved by Sine and Cosine Algorithms

Abstract

This paper highlights a new approach to generate an optimal collision-free trajectory path for each robot in a cluttered and unknown workspace using enhanced particle swarm optimization (IPSO) with sine and cosine algorithms (SCAs). In the current work, PSO has enhanced with the notion of democratic rule in human society and greedy strategy for selecting the optimal position in the successive iteration using sine and cosine algorithms. The projected algorithm mainly emphasizes to produce a deadlock-free successive location of every robot from their current location, preserve a good equilibrium between diversification and intensification, and minimize the path distance for each robot. Results achieved from IPSO–SCA have equated with those developed by IPSO and DE in the same workspace to authenticate the efficiency and robustness of the suggested approach. The outcomes of the simulation and real platform result reveal that IPSO–SCA is superior to IPSO and DE in the form of producing an optimal collision-free path, arrival time, and energy utilization during travel.