Reinforcement learning-based shared control for walking-aid robot and its experimental verification |
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| Authors: | Wenxia Xu Yongji Wang Chunjing Tao Lei Cheng |
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| Affiliation: | 1. Key Laboratory of Ministry of Education for Image Processing and Intelligent Control, School of Automation, Huazhong University of Science and Technology, Wuhan, China.;2. National Rehabilitation Center, Beijing, China.;3. School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan, China. |
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| Abstract: | A walking-aid robot is an assistive device for enabling safe, stable and efficient locomotion in elderly or disabled individuals. In this paper, we propose a reinforcement learning-based shared control (RLSC) algorithm for intelligent walking-aid robot to address existing control problems in cooperative walking-aid robot system. Firstly, the intelligent walking-aid robot and the human walking intention estimation algorithm are introduced. Due to the limited physical and cognitive capabilities of elderly and disabled people, robot control input assistance is provided to maintain tactile comfort and a sense of stability. Then, considering the robot’s ability to autonomously adapt to different user operation habits and motor abilities, the RLSC algorithm is proposed. By dynamically adjusting user control weight according to different user control efficiencies and walking environments, the robot can improve the user’s degree of comfort when using the device and automatically adapting to user’s behaviour. Finally, the effectiveness of our algorithm is verified by experiments in a specified environment. |
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| Keywords: | walking-aid robot shared control reinforcement learning motion control motion estimation |
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