Wang Yu, Wang Jian, Kang Song, 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 Jan 4;9(1):0. doi: 10.3390/biomimetics9010033.
Biological fish often swim in a schooling manner, the mechanism of which comes from the fact that these schooling movements can improve the fishes' hydrodynamic efficiency. Inspired by this phenomenon, a target-following control framework for a biomimetic autonomous system is proposed in this paper. Firstly, a following motion model is established based on the mechanism of fish schooling swimming, in which the follower robotic fish keeps a certain distance and orientation from the leader robotic fish. Second, by incorporating a predictive concept into reinforcement learning, a predictive deep deterministic policy gradient-following controller is provided with the normalized state space, action space, reward, and prediction design. It can avoid overshoot to a certain extent. A nonlinear model predictive controller is designed and can be selected for the follower robotic fish, together with the predictive reinforcement learning. Finally, extensive simulations are conducted, including the fix point and dynamic target following for single robotic fish, as well as cooperative following with the leader robotic fish. The obtained results indicate the effectiveness of the proposed methods, providing a valuable sight for the cooperative control of underwater robots to explore the ocean.
生物鱼常常以群体游动的方式游动，其机制源于这些群体运动能够提高鱼的流体动力学效率。受此现象启发，本文提出了一种用于仿生自主系统的目标跟踪控制框架。首先，基于鱼群游动的机制建立了跟随运动模型，其中跟随的机器鱼与领头的机器鱼保持一定的距离和方向。其次，通过将预测概念融入强化学习，为归一化状态空间、动作空间、奖励和预测设计提供了一种预测深度确定性策略梯度跟随控制器。它可以在一定程度上避免超调。设计了一种非线性模型预测控制器，可与预测强化学习一起用于跟随的机器鱼。最后，进行了广泛的仿真，包括单个机器鱼的定点和动态目标跟踪，以及与领头机器鱼的协同跟踪。所获得的结果表明了所提方法的有效性，为水下机器人探索海洋的协同控制提供了有价值的见解。
