A parametric method to generate low energy gait for both single and double support phases with zero moment point(ZMP)stability is presented.The ZMP stability condition is expressed as a limit to the actuating torque o...A parametric method to generate low energy gait for both single and double support phases with zero moment point(ZMP)stability is presented.The ZMP stability condition is expressed as a limit to the actuating torque of the support ankle,and the inverse dynamics of both walking phases is investigated.A parametric optimization method is implemented which approximates joint trajectories by cubic spline functions connected at uniformly distributed time knots and makes optimization parameters only involve finite discrete states describing key postures.Thus,the gait optimization is transformed into an ordinary constrained nonlinear programming problem.The effectiveness of the method is verified through numerical simulations conducted on the humanoid robot THBIP-I model.展开更多
Squatting is a basic movement of bipedal robots,which is essential in robotic actions like jumping or picking up objects.Due to the intrinsic complex dynamics of bipedal robots,perfect squatting motion requires high-p...Squatting is a basic movement of bipedal robots,which is essential in robotic actions like jumping or picking up objects.Due to the intrinsic complex dynamics of bipedal robots,perfect squatting motion requires high-performance motion planning and control algorithms.The standard academic solution combines model predictive control(MPC)with whole-body control(WBC),which is usually computationally expensive and difficult to implement on practical robots with limited computing resources.The real-time kinematic prediction(RKP)method is proposed,which considers upcoming reference motion trajectories and combines it with quadratic programming(QP)-based WBC.Since the WBC handles the full robot dynamics and various constraints,the RKP only needs to adopt the linear kinematics in the robot's task space and to softly constrain the desired accelerations.Then,the computational cost of derived closed-form RKP is greatly reduced.The RKP method is verified in simulation on a heavy-loaded bipedal robot.The robot makes rapid and large-amplitude squatting motions,which require close-to-limit torque outputs.Compared with the conventional QP-based WBC method,the proposed method exhibits high adaptability to rough planning,which implies much less user interference in the robot's motion planning.Furthermore,like the MPC,the proposed method can prepare for upcoming motions in advance but requires much less computation time.展开更多
基金the National Natural Science Foundation of China(No.60674017).
文摘A parametric method to generate low energy gait for both single and double support phases with zero moment point(ZMP)stability is presented.The ZMP stability condition is expressed as a limit to the actuating torque of the support ankle,and the inverse dynamics of both walking phases is investigated.A parametric optimization method is implemented which approximates joint trajectories by cubic spline functions connected at uniformly distributed time knots and makes optimization parameters only involve finite discrete states describing key postures.Thus,the gait optimization is transformed into an ordinary constrained nonlinear programming problem.The effectiveness of the method is verified through numerical simulations conducted on the humanoid robot THBIP-I model.
基金Science and Technology Innovation 2030-Key Project,Grant/Award Number:2021ZD0201402。
文摘Squatting is a basic movement of bipedal robots,which is essential in robotic actions like jumping or picking up objects.Due to the intrinsic complex dynamics of bipedal robots,perfect squatting motion requires high-performance motion planning and control algorithms.The standard academic solution combines model predictive control(MPC)with whole-body control(WBC),which is usually computationally expensive and difficult to implement on practical robots with limited computing resources.The real-time kinematic prediction(RKP)method is proposed,which considers upcoming reference motion trajectories and combines it with quadratic programming(QP)-based WBC.Since the WBC handles the full robot dynamics and various constraints,the RKP only needs to adopt the linear kinematics in the robot's task space and to softly constrain the desired accelerations.Then,the computational cost of derived closed-form RKP is greatly reduced.The RKP method is verified in simulation on a heavy-loaded bipedal robot.The robot makes rapid and large-amplitude squatting motions,which require close-to-limit torque outputs.Compared with the conventional QP-based WBC method,the proposed method exhibits high adaptability to rough planning,which implies much less user interference in the robot's motion planning.Furthermore,like the MPC,the proposed method can prepare for upcoming motions in advance but requires much less computation time.
