Sensor-Driven Online Coverage Planning for Autonomous Underwater Vehicles

Abstract

At present, autonomous underwater vehicle (AUV) mine countermeasure (MCM) surveys are normally preplanned by operators using ladder or zig-zag paths. Such surveys are conducted with side-looking sonar sensors whose performance is dependent on environmental, target, sensor, and AUV platform parameters. It is difficult to obtain precise knowledge of all of these parameters to be able to design optimal mission plans offline. This research represents the first known sensor driven online approach to seabed coverage for MCM. A method is presented where paths are planned using a multiobjective optimization. Information theory is combined with a new concept coined branch entropy based on a hexagonal cell decomposition. The result is a planning algorithm that not only produces shorter paths than conventional means, but is also capable of accounting for environmental factors detected in situ. Hardware-in-the-loop simulations and in water trials conducted on the IVER2 AUV show the effectiveness of the proposed method.