NDLI: Applying Area Extension PSO in Robotic Swarm

Content Provider	Springer Nature Link
Author	Atyabi, Adham Phon Amnuaisuk, Somnuk Ho, Chin Kuan
Copyright Year	2009
Abstract	Particle Swarm optimization (PSO) is a search method inspired from the social behaviors of animals. PSO has been found to outperform other methods in various tasks. Area Extended PSO (AEPSO) is an enhanced version of PSO that achieves better performance by balancing its essential intelligent behaviours more intelligently. AEPSO incorporates knowledge with the aim of choosing proper behaviors in each situation. This study provides a comparison between the variations of Basic PSO and AEPSO aiming to address dynamic and time dependent constraint problems in simulated robotic search. The problem is set up in a multi-robot learning scenario. The scenario is based on the use of a team of simulated robots (hereafter referred to as agents) who participate in survivor rescuing missions. The experiments are classified into three simulations. At first, agents employ variations of basic PSO as their decision maker and movement controllers. The first simulation investigates the impacts of swarm size, parameter adjustment, and population density on agents’ performance. Later, AEPSO is employed to improve the performance of the swarm in the same simulations. The final simulation investigates the feasibility of AEPSO in time-dependent, dynamic and uncertain environments. As shown by the results, AEPSO achieves an appreciable level of performance in dynamic, time-dependence and uncertain simulated environments and outperforms the variations of basic PSO, Linear Search and Random Search used in the simulations.
Starting Page	253
Ending Page	285
Page Count	33
File Format	PDF
ISSN	09210296
Journal	Journal of Intelligent & Robotic Systems
Volume Number	58
Issue Number	3-4
e-ISSN	15730409
Language	English
Publisher	Springer Netherlands
Publisher Date	2009-10-13
Publisher Place	Dordrecht
Access Restriction	Subscribed
Subject Keyword	Area extension PSO Dynamic Particle swarm optimization Robotic swarm Time dependent Uncertainty Mechanical Engineering Artificial Intelligence (incl. Robotics) Electrical Engineering Control , Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Artificial Intelligence Control and Systems Engineering Mechanical Engineering Electrical and Electronic Engineering Software

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