An intelligent multi-robot path planning in a dynamic environment using improved gravitational search algorithm
Pages 1032 - 1044
Abstract
This paper proposes a new methodology to optimize trajectory of the path for multi-robots using improved gravitational search algorithm (IGSA) in clutter environment. Classical GSA has been improved in this paper based on the communication and memory characteristics of particle swarm optimization (PSO). IGSA technique is incorporated into the multi-robot system in a dynamic framework, which will provide robust performance, self-deterministic cooperation, and coping with an inhospitable environment. The robots in the team make independent decisions, coordinate, and cooperate with each other to accomplish a common goal using the developed IGSA. A path planning scheme has been developed using IGSA to optimally obtain the succeeding positions of the robots from the existing position in the proposed environment. Finally, the analytical and experimental results of the multi-robot path planning were compared with those obtained by IGSA, GSA and differential evolution (DE) in a similar environment. The simulation and the Khepera environment result show outperforms of IGSA as compared to GSA and DE with respect to the average total trajectory path deviation, average uncovered trajectory target distance and energy optimization in terms of rotation.
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© Institute of Automation, Chinese Academy of Sciences and Springer-Verlag GmbH Germany 2016.
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Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 01 December 2021
Accepted: 10 December 2015
Received: 03 September 2015
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