A hybrid improved PSO-DV algorithm for multi-robot path planning in a clutter environment

Article Type

Research Article

Publication Title

Neurocomputing

Abstract

This paper proposed a novel approach to determine the optimal trajectory of the path for multi-robots in a clutter environment using hybridization of improved particle swarm optimization (IPSO) with differentially perturbed velocity (DV) algorithm. The objective of the algorithm is to minimize the maximum path length that corresponds to minimize the arrival time of all the robots to their respective destination in the environment. The robots on the team make independent decisions, coordinate, and cooperate with each other to determine the next positions from their current position in the world map using proposed hybrid IPSO-DV. The proposed scheme adjusts the velocity of the robots by incorporating a vector differential operator inherited from Differential Evolution (DE) in IPSO. Finally the analytical and experimental results of the multi-robot path planning have been compared to those obtained by IPSO-DV, IPSO, DE in a similar environment. Simulation and khepera environment results are compared with those obtained by IPSO-DV to ensure the integrity of the algorithm. The results obtained from Simulation as well as Khepera environment reveal that, the proposed IPSO-DV performs better than IPSO and DE with respect to optimal trajectory path length and arrival time.

First Page

735

Last Page

753

DOI

10.1016/j.neucom.2016.05.057

Publication Date

9-26-2016

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