基于深度强化学习的欠驱动仿生机器鳗鱼控制研究

Research on Control of an Underactuated Bionic Robotic Eel Based on Deep Reinforcement Learning

水下仿生机器人具有高效率、高机动性、低噪声等优点,针对仿生机器鳗鱼存在设计复杂、控制难度大等问题,该文提出了一种新型欠驱动机器鳗鱼的控制方法。首先,基于主动加被动的仿生机构推进原理,设计了两段主动体与两段被动顺从体相结合的机器鳗鱼仿生机构;然后,在仿真环境中进行建模,利用深度强化学习算法进行数据收集和训练,选择表现良好的神经网络在仿真环境中进行控制测试,从而得到机器鳗鱼的控制函数;最后,通过对比实验,验证了该文设计方法的可行性以及控制函数的有效性,实现了对机器鳗鱼的控制。

Underwater bionic robots have distinct advantages such as the high efficiency,high mobility and low noise etc.In this paper,a deep reinforcement learning based method is studied to control the robotic eel.Firstly,based on the propulsion principle of active and passive bionic mechanism,a robotic eel with two active rigid bodies and two compliant bodies is designed.Secondly,the robotic eel is modeled and simulated.The data collecting and training tasks are carried out in the simulation environment using deep reinforcement learning algorithms.The neural network with better performance is selected as the control function for the robotic eel.Finally,feasibility of the design and effectiveness of the control function are verified by a prototype via real experiments.