Professor of Mechanical Engineering and Smart Structures, School of Computing Engineering and Mathematics, Western Sydney University, Australia. His research interests cover Industry 4.0, Additive Manufacturing, Advanced Engineering Materials and Structures (Metals and Composites), Multi-scale Modelling of Materials and Structures, Metal Forming and Metal Surface Treatment.
Abstract—This paper discusses a solution to one of the key issues in the swarm robotics field which is the dynamic task allocation problem in which a group of robots needs to be allocated to a set of tasks scattered in the environment in an efficient and decentralized way. The application considered in this context is the foraging application which can be addressed as a searching job followed by a transportation job. The near-optimal allocation scheme is found by using the Particle swarm optimization (PSO) technique to handle the whole task execution in a minimal time. Two case studies have been considered using different swarm sizes and the implemented code has been executed for a distinctive number of iterations. A stability proof for the PSO technique’s parameters choices is presented. Simulation results were verified by comparing the proposed algorithm with the simulated annealing optimization technique in terms of computational time, number of iterations needed and quality of solution to demonstrate the robustness and efficiency of the algorithm.
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