Inspired by the neuronal principles underlying the tetrapod locomotion,this paper proposed a biomimetic vestibular reflex central pattern generator（ CPG） model to improve motion performance and terrain adaptive abil...Inspired by the neuronal principles underlying the tetrapod locomotion,this paper proposed a biomimetic vestibular reflex central pattern generator（ CPG） model to improve motion performance and terrain adaptive ability of a quadruped robot in complex situations,which is on the basis of central pattern generator（ CPG） model constructed by modified Hopf oscillators. The presented reflex model was modified in the light of the particular joint configuration of the quadruped robot and the trot gait pattern. Focusing on slop locomotion of the quadruped robot with trot gaits,the cosimulations of the ADAMS virtual prototype,CPG mathematical expressions with vestibular reflex and Simulink control model were conducted. The simulation results demonstrated that the presented CPG controller with vestibular reflex was more efficient and stable for the quadruped robot trotting on slopes,c ompared with the different trotting control models.展开更多
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
基金Supported by the Ministerial Level Advanced Research Foundation(40401060305)
文摘Inspired by the neuronal principles underlying the tetrapod locomotion,this paper proposed a biomimetic vestibular reflex central pattern generator（ CPG） model to improve motion performance and terrain adaptive ability of a quadruped robot in complex situations,which is on the basis of central pattern generator（ CPG） model constructed by modified Hopf oscillators. The presented reflex model was modified in the light of the particular joint configuration of the quadruped robot and the trot gait pattern. Focusing on slop locomotion of the quadruped robot with trot gaits,the cosimulations of the ADAMS virtual prototype,CPG mathematical expressions with vestibular reflex and Simulink control model were conducted. The simulation results demonstrated that the presented CPG controller with vestibular reflex was more efficient and stable for the quadruped robot trotting on slopes,c ompared with the different trotting control models.
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.
