两栖六足仿生机器人的水陆运动控制研究

两栖六足机器人不仅需应对崎岖地形对陆地爬行提出的挑战,还要解决机器人在水下灵活运动的控制问题。 因此,本 文首先提出了基于深度强化学习的崎岖地形运动控制方法。 通过 MuJoCo 为机器人执行爬行任务构建交互环境,并采用近端策 略优化(PPO)算法训练智能体使其获取适应于不同崎岖程度地形的控制策略。 仿真数据表明,陆地控制策略可使机器人在平 坦、轻度崎岖、重度崎岖 3 类地形上快速、稳定地完成前进任务。 针对水下运动控制问题,本文通过分析机器人动力学模型将其 分解为:采用视线法与 PID 控制器解决平面轨迹跟踪和深度控制问题。 水下实验表明,机器人可在平面快速跟踪 Sigmoid 曲线 且轨迹偏差不超过 0. 11 m。 深度控制实验中,机器人可平稳到达指定深度且控制精度在 0. 02 m 以内。

Study on the terrestrial and underwater motion control of the amphibious hexapod bionic robot

The amphibious hexapod robot is characterized by its flexible legs, which aims to address the terrestrial and underwater motion control in complex environment. In this article, a deep reinforcement learning based terrestrial motion control is firstly proposed for movements on rugged lands. By building agent interaction scenarios using the MuJoCo physical engine, the proximal policy optimization algorithm is employed to obtain the optimal motion policy applied in rugged lands of different conditions. Simulation results show that the robot is capable of climbing fast and stable on rugged terrains when controlled by the generated policies. Concerning the underwater motion control issue, the hydrodynamic model is derived. Based on its analysis, the three-dimensional underwater motions can be decoupled into the planar trajectory tracking and the depth control. Especially, the LOS and PID control are used. Experimental results prove that the robot can track the Sigmoid curve with path error less than 0. 11 m. In addition, the robot is qualified to realize the PID based depth control with the accuracy of 0. 02 m.