Open Access
ARTICLE
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
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. https://doi.org/10.32604/iasc.2020.013921