Title: Autonomous PSO-DVSF2: an optimised force field mobile robot motion planning approach for unknown dynamic environments

Authors: Safa Ziadi; Mohamed Njah; Mohamed Chtourou

Addresses: Control and Energy Management Laboratory (CEM-Lab), Electrical Engineering Department, National Engineering School of Sfax, University of Sfax, Tunisia; Digital Research Center of Sfax, Technopole of Sfax, PO Box 275, Sakiet Ezzit, 3021 Sfax, Tunisia ' Control and Energy Management Laboratory (CEM-Lab), Electrical Engineering Department, National Engineering School of Sfax, University of Sfax, Tunisia; Digital Research Center of Sfax, Technopole of Sfax, PO Box 275, Sakiet Ezzit, 3021 Sfax, Tunisia ' Control and Energy Management Laboratory (CEM-Lab), Electrical Engineering Department, National Engineering School of Sfax, University of Sfax, Tunisia

Abstract: PSO-DVSF2 (Ziadi et al., 2016) is a PSO optimised mobile robot motion planning strategy based on the force field approach. PSO-DVSF2 has been previously developed to optimally guide the mobile robot in known dynamic environments. Autonomous PSO-DVSF2, the subject of this paper, is an improvement of PSO-DVSF2 to deal with unknown dynamic environments. In this new real-time PSO optimised mobile robot motion planning approach, the robot has to update F2 parameters all along the trajectory and not once in the beginning of the navigation as has been the case with the previous version. A comparison with the autonomous PSO-CF2 has been applied in various unknown environments (static and dynamic) using the 3D virtual Webots simulator. The robot localisation based on sensor readings with a local motion planning, and the variation of angular and linear speeds ensure the robot collision-free motion. Simulation results prove the efficiency of the autonomous PSO-DVSF2 to guide the robot along the shortest and safest path in complex unknown dynamic environments.

Keywords: mobile robot motion planning; unknown dynamic environment; particle swarm optimisation; PSO; F2; Webots simulator.

DOI: 10.1504/IJAAC.2021.114922

International Journal of Automation and Control, 2021 Vol.15 No.3, pp.318 - 339

Received: 20 Sep 2018
Accepted: 02 May 2019

Published online: 12 May 2021 *

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