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为实现外骨骼摆动腿踝关节对人体踝关节运动轨迹的跟踪,建立了外骨骼摆动腿逆运动学模型与卡尔曼滤波预测模型,并在外骨骼摆动腿踝关节处设置人机位姿误差传感器,同时进行了人体步态实验.仿真中以实验测取的人体踝关节相对于其髋关节的运动轨迹作为外骨骼踝关节的跟踪对象,并依此计算外骨骼踝关节处人机位姿误差.采用卡尔曼滤波器对外骨骼摆动腿的髋、膝关节位置进行预测,再利用外骨骼逆运动学模型对预测结果进行校正.结果表明:该方法能良好地实现外骨骼摆动腿踝关节对人体踝关节的运动轨迹跟踪,并且卡尔曼滤波预测能明显改善跟踪的滞后性,同时仅需检测外骨骼踝关节处的人机位姿误差也有助于简化人机之间的传感器布置.
In order to realize the tracking of human ankle joint motion with the exoskeleton swinging ankle joint, an inverse kinematics model and a Kalman filter prediction model of the exoskeleton swing leg are established, and a humanoid attitude error sensor is set up at the ankle joint of the exoskeleton swinging leg, At the same time, the human gait experiment is carried out, and the trajectory of the human ankle relative to the hip joint measured by the experiment is taken as the tracking object of the exoskeleton ankle joint, and the pose error of the humanoid at the ankle joint of the exoskeleton is calculated accordingly. The Kalman filter was used to predict the position of the hip and knee in the exoskeleton swinging leg and the exoskeleton inverse kinematics model was used to correct the prediction results.The results show that this method can well realize the effect of the exoskeleton swinging leg ankle joint on the human body ankle Joint motion trajectory tracking, and Kalman filter prediction can significantly improve tracking hysteresis, and only need to detect the humanoid pose error at the exoskeleton ankle joint also helps to simplify the sensor arrangement between the man-machine.