Abstract
This paper addresses adaptive, on-line path planning of an autonomous underwater vehicle and presents a GA-based method for it. It is an important module of SAUVIM (Semi-Autonomous Underwater Vehicle for Intervention Missions) which is being developed at the University of Hawaii and will be capable of exploring the ocean at up to 6,000 m depth. In SAUVIM, a genetic algorithm (GA) is employed in order to integrate on-line path planning with off-line planning and make path planning adaptive. We first discuss how sensory information is incorporated into pre-loaded mapping data of the ocean floor. Then, we present a method for updating a path in real time while the vehicle is moving. A prototype of the adaptive, on-line path planning module is also presented.
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