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QRDPSO: A New Optimization Method for Swarm Robot Searching and Obstacle Avoidance in Dynamic Environments

Mehiar, D.A.F., Azizul, Z.H.*, Loo, C.K.
Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia
* Corresponding Author: Azizul, Z.H.,

Intelligent Automation & Soft Computing 2020, 26(3), 447-454. https://doi.org/10.32604/iasc.2020.013921

Abstract

In this paper we show how the quantum-based particle swarm optimization (QPSO) method is adopted to derive a new derivation for robotics application in search and rescue simulations. The new derivation, called the Quantum Robot Darwinian PSO (QRDPSO) is inspired from another PSO-based algorithm, the Robot Darwinian PSO (RDPSO). This paper includes comprehensive details on the QRDPSO formulation and parameters control which show how the swarm overcomes communication constraints to avoid obstacles and achieve optimal solution. The results show the QRDPSO is an upgrade over RDPSO in terms of convergence speed, trajectory control, obstacle avoidance and connectivity performance of the swarm.

Keywords

Swarm robotics, particle swarm optimization (PSO), quantum behaving particle, search and rescue simulations.

Cite This Article

M. D.A.F., A. Z.H. and L. C.K., "Qrdpso: a new optimization method for swarm robot searching and obstacle avoidance in dynamic environments," Intelligent Automation & Soft Computing, vol. 26, no.3, pp. 447–454, 2020.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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