Xing Tianyu, Wang Xiaohao, Ding Kaiyang, Ni Kai, Zhou Qian
Division of Advanced Manufacturing, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.
Sensors (Basel). 2023 Jul 26;23(15):6680. doi: 10.3390/s23156680.
With the development of ocean exploration technology, the exploration of the ocean has become a hot research field involving the use of autonomous underwater vehicles (AUVs). In complex underwater environments, the fast, safe, and smooth arrival of target points is key for AUVs to conduct underwater exploration missions. Most path-planning algorithms combine deep reinforcement learning (DRL) and path-planning algorithms to achieve obstacle avoidance and path shortening. In this paper, we propose a method to improve the local minimum in the artificial potential field (APF) to make AUVs out of the local minimum by constructing a traction force. The improved artificial potential field (IAPF) method is combined with DRL for path planning while optimizing the reward function in the DRL algorithm and using the generated path to optimize the future path. By comparing our results with the experimental data of various algorithms, we found that the proposed method has positive effects and advantages in path planning. It is an efficient and safe path-planning method with obvious potential in underwater navigation devices.
随着海洋探测技术的发展，海洋探索已成为一个涉及使用自主水下航行器（AUV）的热门研究领域。在复杂的水下环境中，快速、安全且平稳地到达目标点是AUV执行水下探测任务的关键。大多数路径规划算法将深度强化学习（DRL）与路径规划算法相结合，以实现避障和路径缩短。在本文中，我们提出了一种改进人工势场（APF）中局部最小值的方法，通过构建牵引力使AUV摆脱局部最小值。改进的人工势场（IAPF）方法与DRL相结合进行路径规划，同时优化DRL算法中的奖励函数，并使用生成的路径来优化未来路径。通过将我们的结果与各种算法的实验数据进行比较，我们发现所提出的方法在路径规划中具有积极的效果和优势。它是一种高效且安全的路径规划方法，在水下导航设备中具有明显的潜力。
