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AUV Obstacle Avoidance Planning Based on Deep Reinforcement Learning
Journal of Marine Science and Engineering ( IF 2.9 ) Pub Date : 2021-10-23 , DOI: 10.3390/jmse9111166
Jianya Yuan , Hongjian Wang , Honghan Zhang , Changjian Lin , Dan Yu , Chengfeng Li

In a complex underwater environment, finding a viable, collision-free path for an autonomous underwater vehicle (AUV) is a challenging task. The purpose of this paper is to establish a safe, real-time, and robust method of collision avoidance that improves the autonomy of AUVs. We propose a method based on active sonar, which utilizes a deep reinforcement learning algorithm to learn the processed sonar information to navigate the AUV in an uncertain environment. We compare the performance of double deep Q-network algorithms with that of a genetic algorithm and deep learning. We propose a line-of-sight guidance method to mitigate abrupt changes in the yaw direction and smooth the heading changes when the AUV switches trajectory. The different experimental results show that the double deep Q-network algorithms ensure excellent collision avoidance performance. The effectiveness of the algorithm proposed in this paper was verified in three environments: random static, mixed static, and complex dynamic. The results show that the proposed algorithm has significant advantages over other algorithms in terms of success rate, collision avoidance performance, and generalization ability. The double deep Q-network algorithm proposed in this paper is superior to the genetic algorithm and deep learning in terms of the running time, total path, performance in avoiding collisions with moving obstacles, and planning time for each step. After the algorithm is trained in a simulated environment, it can still perform online learning according to the information of the environment after deployment and adjust the weight of the network in real-time. These results demonstrate that the proposed approach has significant potential for practical applications.

中文翻译:

基于深度强化学习的AUV避障规划

在复杂的水下环境中,为自主水下航行器 (AUV) 寻找可行的、无碰撞的路径是一项具有挑战性的任务。本文的目的是建立一种安全、实时和稳健的避碰方法,以提高 AUV 的自主性。我们提出了一种基于主动声纳的方法,该方法利用深度强化学习算法来学习处理后的声纳信息,从而在不确定的环境中导航 AUV。我们将双深度 Q 网络算法的性能与遗传算法和深度学习的性能进行了比较。我们提出了一种视线引导方法,以减轻偏航方向的突然变化并平滑 AUV 切换轨迹时的航向变化。不同的实验结果表明,双深度Q网络算法保证了优异的防撞性能。在随机静态、混合静态和复杂动态三种环境中验证了本文提出的算法的有效性。结果表明,该算法在成功率、防撞性能、泛化能力等方面均优于其他算法。本文提出的双深度Q网络算法在运行时间、总路径、避免与移动障碍物碰撞的性能以及每一步的规划时间等方面均优于遗传算法和深度学习。在模拟环境中训练算法后,部署后仍可根据环境信息进行在线学习,实时调整网络权重。这些结果表明,所提出的方法具有实际应用的巨大潜力。
更新日期:2021-10-24
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