Evolutionary computing based mobile robot localization

Authors:
N. M. Kwok;D. K. Liu;G. Dissanayake
Affiliations:
ARC Centre of Excellence in Autonomous Systems (CAS), Faculty of Engineering, University of Technology, Sydney, Broadway, NSW 2007, Australia;ARC Centre of Excellence in Autonomous Systems (CAS), Faculty of Engineering, University of Technology, Sydney, Broadway, NSW 2007, Australia;ARC Centre of Excellence in Autonomous Systems (CAS), Faculty of Engineering, University of Technology, Sydney, Broadway, NSW 2007, Australia
Venue:
Engineering Applications of Artificial Intelligence
Year:
2006

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Abstract

Evolutionary computing techniques, including genetic algorithms (GA), particle swarm optimization (PSO) and ants system (AS) are applied to the localization problem of a mobile robot. Salient features of robot localization are that the system is partially dynamic and information for fitness evaluation is incomplete and corrupted by noise. In this research, variations to the above three evolutionary computing techniques are proposed to tackle the specific dynamic and noisy system. Their performances are compared based on simulation and experiment results and the feasibility of the proposed approach to mobile robot localization is demonstrated.