Adaptive control for autonomous underwater vehicles

Authors:
Conor McGann;Frederic Py;Kanna Rajan;John Ryan;Richard Henthorn
Affiliations:
Monterey Bay Aquarium Research Institute, Moss Landing, California;Monterey Bay Aquarium Research Institute, Moss Landing, California;Monterey Bay Aquarium Research Institute, Moss Landing, California;Monterey Bay Aquarium Research Institute, Moss Landing, California;Monterey Bay Aquarium Research Institute, Moss Landing, California
Venue:
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Year:
2008

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Abstract

We describe a novel integration of Planning with Probabilistic State Estimation and Execution. The resulting system is a unified representational and computational framework based on declarative models and constraint-based temporal plans. The work is motivated by the need to explore the oceans more cost-effectively through the use of Autonomous Underwater Vehicles (AUV), requiring them to be goal-directed, perceptive, adaptive and robust in the context of dynamic and uncertain conditions. The novelty of our approach is in integrating deliberation and reaction over different temporal and functional scopes within a single model, and in breaking new ground in oceanography by allowing for precise sampling within a feature of interest using an autonomous robot. The system is general-purpose and adaptable to other ocean going and terrestrial platforms.