Wang Yu, Wang Jian, Yu Lianyi, Kong Shihan, Yu Junzhi
Department of Automation, Tsinghua University, Beijing 100084, China.
The Laboratory of Cognitive and Decision Intelligence for Complex System, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Biomimetics (Basel). 2024 Feb 21;9(3):126. doi: 10.3390/biomimetics9030126.
Safe, underwater exploration in the ocean is a challenging task due to the complex environment, which often contains areas with dense coral reefs, uneven terrain, or many obstacles. To address this issue, an intelligent underwater exploration framework of a biomimetic robot is proposed in this paper, including an obstacle avoidance model, motion planner, and yaw controller. Firstly, with the aid of the onboard distance sensors in robotic fish, the obstacle detection model is established. On this basis, two types of obstacles, i.e., rectangular and circular, are considered, followed by the obstacle collision model's construction. Secondly, a deep reinforcement learning method is adopted to plan the plane motion, and the performances of different training setups are investigated. Thirdly, a backstepping method is applied to derive the yaw control law, in which a sigmoid function-based transition method is employed to smooth the planning output. Finally, a series of simulations are carried out to verify the effectiveness of the proposed method. The obtained results indicate that the biomimetic robot can not only achieve intelligent motion planning but also accomplish yaw control with obstacle avoidance, offering a valuable solution for underwater operation in the ocean.
由于海洋环境复杂，其中常常包含珊瑚礁密集、地形不平或障碍物众多的区域，因此在海洋中进行安全的水下探测是一项具有挑战性的任务。针对这一问题，本文提出了一种仿生机器人智能水下探测框架，包括避障模型、运动规划器和偏航控制器。首先，借助机器鱼上的车载距离传感器，建立障碍物检测模型。在此基础上，考虑矩形和圆形两种类型的障碍物，随后构建障碍物碰撞模型。其次，采用深度强化学习方法规划平面运动，并研究不同训练设置的性能。第三，应用反步法推导偏航控制律，其中采用基于 sigmoid 函数的过渡方法来平滑规划输出。最后，进行了一系列仿真以验证所提方法的有效性。所得结果表明，该仿生机器人不仅能够实现智能运动规划，还能完成具有避障功能的偏航控制，为海洋水下作业提供了一种有价值的解决方案。
