Terrain-adaptive optimal guidance for near-bottom survey by an autonomous underwater vehicle

An optimal guidance strategy for an autonomous underwater vehicle dive specified to a near-bottom survey over an uneven seafloor topography is presented. Major objective of the optimal guidance is letting the longitudinal vehicle attitude follow the along-track gradient of the seafloor throughout the near-bottom survey dive. The proposed method derives depth sequence corresponding to waypoints predefined in a horizontal plane, combined set of which defines the 3-dimentional trackline for the optimal near-bottom survey. A supervised learning approach based on gradient descent search algorithm is employed in deriving the optimal depth sequence. Iterative simulations of vehicle´s dives driven by the learning algorithm subject to vehicle dynamics and interacting with given seabed topography derive the optimal depth sequence. The bottom gradient-following is an important issue in achieving higher quality sonograph and precise echo-sounding, as well as keeping the reliability of altitudes obtained by an acoustic altimeter. Proposed strategy is applied to near-bottom survey dives performed with a cruising AUV Aqua-Explorer 2000a. Based on the body structure of Aqua-Explorer 2000 originally developed by KDDI for inspecting and monitoring the undersea cables, Aqua-Explorer 2000a was born via full renovation conducted by Institute of Industrial Science, The University of Tokyo.