基于深度强化学习的空间机械臂柔顺捕获控制方法研究

On Compliant Capture Control Method by Space Manipulator Based on Deep Reinforcement Learning

针对空间机械臂在轨捕获问题,提出了一种基于深度强化学习原理的柔顺捕获控制方法,采用深度确定性策略梯度算法设计了控制器.在仿真环境中使用6自由度机械臂对特定质量、初始速度的目标进行了大量抓捕训练,使得控制器能够根据机械臂状态输出合适的力矩,促使目标运动速度最终趋近于0并能够有效降低交互过程中的冲击力.同时,对于不同质量和初始速度的目标,该控制器同样具备良好的适应性并可实现柔顺捕获.与传统基于阻抗控制原理的柔顺控制方法相比,该方法能够减小碰撞过程的最大冲击力,实现不依赖模型的柔顺控制,经工程化改进后有望应用于空间智能捕获任务中.

A compliant capture control method based on the principle of deep reinforcement learning is proposed to solve the in-orbit capture problem by a space manipulator. In the simulation environment the controller is trained to use a 6-DOF manipulator to capture the target, which has specified mass and initial velocity. The controller learns to output appropriate control forces according to the states of the manipulator and it can make the target speed eventually approach to 0 and effectively reduce the impact force. The controller also shows good compliance interaction performance for targets with different mass and initial velocity. Compared with the traditional compliant control method based on the principle of impedance control, this method can reduce the max impact force effectively and realize the model-independent control. After improving for space application, it is expected to be used in an intelligence capture task.