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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