Path Planning for Multipoint Seabed Survey Mission Using Autonomous Underwater Vehicle
Published in IEEE OCEANS, 2017
面向大尺度海底测量任务中 AUV 续航受限的关键挑战，本文提出一种分层式多点任务路径规划方法。首先，采用带动态剪枝的迭代 K-means 聚类，在满足作业约束与覆盖需求的前提下，优化支撑船锚点/驻留点的空间布局；随后，基于改进蚁群算法对每个锚点出发的测量航迹进行优化，并显式考虑潜航时长、采样容量与航行效率等关键约束。在 160×160 海里、100 个目标点的仿真中，所提方法在满足任务限制的同时显著降低总能耗，尤其适用于资源受限的水下测量以及多 AUV 协同部署场景。
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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