A gait control method for a biped robot based on the deep Q-network (DQN) algorithm is proposed to enhance the stability of walking on uneven ground. This control strategy is an intelligent learning method of posture ...A gait control method for a biped robot based on the deep Q-network (DQN) algorithm is proposed to enhance the stability of walking on uneven ground. This control strategy is an intelligent learning method of posture adjustment. A robot is taken as an agent and trained to walk steadily on an uneven surface with obstacles, using a simple reward function based on forward progress. The reward-punishment (RP) mechanism of the DQN algorithm is established after obtaining the offline gait which was generated in advance foot trajectory planning. Instead of implementing a complex dynamic model, the proposed method enables the biped robot to learn to adjust its posture on the uneven ground and ensures walking stability. The performance and effectiveness of the proposed algorithm was validated in the V-REP simulation environment. The results demonstrate that the biped robot's lateral tile angle is less than 3° after implementing the proposed method and the walking stability is obviously improved.展开更多
Background Restoration of both normal movement of the pelvis and centre of mass is a primary goal of walking rehabilitation in post-stroke patients because these movements are essential components of effective gait. T...Background Restoration of both normal movement of the pelvis and centre of mass is a primary goal of walking rehabilitation in post-stroke patients because these movements are essential components of effective gait. The aim of this study is to quantitatively analyze the effect of ankle-foot orthosis on walking ability, and to investigate the correlation between improvements in trunk motion and walking capacity.展开更多
基金Supported by the National Ministries and Research Funds(3020020221111)
文摘A gait control method for a biped robot based on the deep Q-network (DQN) algorithm is proposed to enhance the stability of walking on uneven ground. This control strategy is an intelligent learning method of posture adjustment. A robot is taken as an agent and trained to walk steadily on an uneven surface with obstacles, using a simple reward function based on forward progress. The reward-punishment (RP) mechanism of the DQN algorithm is established after obtaining the offline gait which was generated in advance foot trajectory planning. Instead of implementing a complex dynamic model, the proposed method enables the biped robot to learn to adjust its posture on the uneven ground and ensures walking stability. The performance and effectiveness of the proposed algorithm was validated in the V-REP simulation environment. The results demonstrate that the biped robot's lateral tile angle is less than 3° after implementing the proposed method and the walking stability is obviously improved.
文摘Background Restoration of both normal movement of the pelvis and centre of mass is a primary goal of walking rehabilitation in post-stroke patients because these movements are essential components of effective gait. The aim of this study is to quantitatively analyze the effect of ankle-foot orthosis on walking ability, and to investigate the correlation between improvements in trunk motion and walking capacity.