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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Sugihara, K. (1998). GA-based on-line path planning for SAUVIM. In: Pasqual del Pobil, A., Mira, J., Ali, M. (eds) Tasks and Methods in Applied Artificial Intelligence. IEA/AIE 1998. Lecture Notes in Computer Science, vol 1416. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64574-8_419
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DOI: https://doi.org/10.1007/3-540-64574-8_419
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