Yang Renyu, Zheng Jianlin, Song Rong
Key Laboratory of Sensing Technology and Biomedical Instrument of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China.
School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China.
Front Neurorobot. 2022 Dec 22;16:1068706. doi: 10.3389/fnbot.2022.1068706. eCollection 2022.
Continuous mode adaptation is very important and useful to satisfy the different user rehabilitation needs and improve human-robot interaction (HRI) performance for rehabilitation robots. Hence, we propose a reinforcement-learning-based optimal admittance control (RLOAC) strategy for a cable-driven rehabilitation robot (CDRR), which can realize continuous mode adaptation between passive and active working mode. To obviate the requirement of the knowledge of human and robot dynamics model, a reinforcement learning algorithm was employed to obtain the optimal admittance parameters by minimizing a cost function composed of trajectory error and human voluntary force. Secondly, the contribution weights of the cost function were modulated according to the human voluntary force, which enabled the CDRR to achieve continuous mode adaptation between passive and active working mode. Finally, simulation and experiments were conducted with 10 subjects to investigate the feasibility and effectiveness of the RLOAC strategy. The experimental results indicated that the desired performances could be obtained; further, the tracking error and energy per unit distance of the RLOAC strategy were notably lower than those of the traditional admittance control method. The RLOAC strategy is effective in improving the tracking accuracy and robot compliance. Based on its performance, we believe that the proposed RLOAC strategy has potential for use in rehabilitation robots.
连续模式自适应对于满足不同用户的康复需求以及提高康复机器人的人机交互（HRI）性能非常重要且有用。因此，我们针对缆索驱动康复机器人（CDRR）提出了一种基于强化学习的最优导纳控制（RLOAC）策略，该策略可实现被动和主动工作模式之间的连续模式自适应。为了避免对人体和机器人动力学模型知识的需求，采用强化学习算法通过最小化由轨迹误差和人体自愿力组成的代价函数来获得最优导纳参数。其次，根据人体自愿力对代价函数的贡献权重进行调制，这使得CDRR能够在被动和主动工作模式之间实现连续模式自适应。最后，对10名受试者进行了仿真和实验，以研究RLOAC策略的可行性和有效性。实验结果表明可以获得期望的性能；此外，RLOAC策略的跟踪误差和单位距离能量明显低于传统导纳控制方法。RLOAC策略在提高跟踪精度和机器人顺应性方面是有效的。基于其性能，我们认为所提出的RLOAC策略在康复机器人中具有应用潜力。
