一种空间仿生柔性机器人设计与智能规划仿真方法

Design and Intelligent Planning Simulation Method of a Space Bionic Soft Robot

针对传统空间刚体机器人存在的自由度有限和环境适应性差等缺陷,基于生物体结构提出了一种受“尺蠖”与“蛇”启发的适用于空间在轨服务的柔性机器人。首先,搭建了柔性机器人原型样机,研究了镍钛形状记忆合金(SMA)驱动器的驱动特性,设计了可视化控制界面并通过实物实验验证了机器人原型样机的可操控性。然后,设计了一种基于所提柔性机器人结构的Q学习算法和相应的奖励函数,搭建了柔性机器人仿真模型并在仿真环境中完成了基于Q学习的机器臂自主学习规划仿真实验。实验结果显示机器臂能够在较短时间内收敛到稳定状态并自主完成规划任务,表明所提出算法具有有效性和可行性,强化学习方法在柔性机器人的智能规划与控制中具有良好的应用前景。

Conventional space rigid-body robots are subject to defects such as limited degree of freedom and low environmental adaptability. For those problems, a soft robot based on the organism structure is designed inspired by inchworm and snake, which is applicable to space in-orbit service. Firstly, the prototype of the soft robot is built and the drive characteristics of the Ni-Ti shape memory alloy(SMA) as the actuators are studied. A visual control interface is designed and the manoeuvrability of the soft robot prototype is verified by physical experiments. Besides, the Q-learning algorithm and the corresponding reward function based on the designed soft robot structure are designed. A simulation model of the soft robot is built, and autonomous learning and planning experiment of the robot arm model based on Q-learning is completed in the simulation environment. The experiment results show that the robot arm can converge to a stable state in a short time and complete planning tasks independently. Therefore, the proposed algorithm is effective and feasible, and the reinforcement learning method has a good application prospect in the intelligent planning and control of soft robots.