Numerical Simulation of Time-Optimal Path Planning for Autonomous Underwater Vehicles Using a Markov Decision Process Method
Numerical Simulation of Time-Optimal Path Planning for Autonomous Underwater Vehicles Using a Markov Decision Process Method
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DOI：
10.3390/app12063064
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发表时间：
2022-03
期刊：
影响因子：
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通讯作者：
Mingrui Shu;Xiuyu Zheng;F. Li;Kai Wang;Qiang Li
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文献类型：
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作者：
Mingrui Shu;Xiuyu Zheng;F. Li;Kai Wang;Qiang Li
许多为陆地或空中自主车辆开发的路径规划算法不再适用于水下。针对海洋环境，提出了一种基于马尔可夫决策过程（MDP）算法的自主水下机器人（AUV）时间最优路径规划方法。它的性能检查不同的海洋条件下，包括复杂的沿海水深和随时间变化的洋流，揭示了优势相比，A* 算法，传统的路径规划方法。洋流预测使用区域海洋模型，然后提供给MDP算法作为先验。通过一系列的灵敏度实验确定了计算效率和特征分辨的空间分辨率。仿真结果表明，在真实的海洋中，将海流纳入AUV路径规划的重要性。MDP算法即使在海流复杂的情况下也保持鲁棒性。
Many path planning algorithms developed for land or air based autonomous vehicles no longer apply under the water. A time-optimal path planning method for autonomous underwater vehicles (AUVs), based on a Markov decision process (MDP) algorithm, is proposed for the marine environment. Its performance is examined for different oceanic conditions, including complex coastal bathymetry and time-varying ocean currents, revealing advantages compared to the A* algorithm, a traditional path planning method. The ocean current is predicted using a regional ocean model and then provided to the MDP algorithm as a priori. A computation-efficient and feature-resolved spatial resolution are determined through a series of sensitivity experiments. The simulations demonstrate the importance to incorporate ocean currents in the path planning of AUVs in the real ocean. The MDP algorithm remains robust even if the ocean current is complex.