Path Planning for Multipoint Seabed Survey Mission Using Autonomous Underwater Vehicle
Published in IEEE OCEANS, 2017
Addressing the challenge of limited AUV endurance in large-scale seabed surveys, this work develops a hierarchical path planning methodology for multi-point missions. The approach first employs iterative K-means clustering with dynamic pruning to strategically position support vessel anchor points while ensuring target coverage under operational constraints. Subsequently, it applies a modified ant colony algorithm to optimize survey paths from each anchor point, incorporating critical constraints including dive duration, sampling capacity, and travel efficiency. Simulation results across a 160×160 nautical mile area with 100 target points demonstrate the framework’s effectiveness in minimizing total energy consumption while satisfying operational limitations. The solution provides significant efficiency gains for resource-constrained underwater survey operations, particularly in multi-AUV deployments.
Recommended citation: Yang An, Gaofei Xu, Chunhui Xu, Hongyu Zhao, and Jian Liu*. (2017). "Path Planning for Multipoint Seabed Survey Mission Using Autonomous Underwater Vehicle." IEEE OCEANS Conference.
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