龙亿, 杜志江, 王伟东. 基于人体运动意图卡尔曼预测的外骨骼机器人控制及实验[J]. 机器人, 2015, 37(3): 304-309. DOI: 10.13973/j.cnki.robot.2015.0304
引用本文: 龙亿, 杜志江, 王伟东. 基于人体运动意图卡尔曼预测的外骨骼机器人控制及实验[J]. 机器人, 2015, 37(3): 304-309. DOI: 10.13973/j.cnki.robot.2015.0304
LONG Yi, DU Zhijiang, WANG Weidong. Control and Experiment for Exoskeleton Robot Based on Kalman Prediction ofHuman Motion Intent[J]. ROBOT, 2015, 37(3): 304-309. DOI: 10.13973/j.cnki.robot.2015.0304
Citation: LONG Yi, DU Zhijiang, WANG Weidong. Control and Experiment for Exoskeleton Robot Based on Kalman Prediction ofHuman Motion Intent[J]. ROBOT, 2015, 37(3): 304-309. DOI: 10.13973/j.cnki.robot.2015.0304

基于人体运动意图卡尔曼预测的外骨骼机器人控制及实验

Control and Experiment for Exoskeleton Robot Based on Kalman Prediction ofHuman Motion Intent

  • 摘要: 为在外骨骼控制中准确获取人体运动意图,本文使用力矩传感器测量人机交互信息.基于人体下肢摆动腿的单摆模型获得摆动腿关节的运动轨迹,并使用卡尔曼滤波进行预测,从而弥补意图延时.使用 PD(比例-微分)控制律控制外骨骼跟踪人体摆动腿的关节轨迹,编码器反馈外骨骼关节的实时位置,形成位置闭环控制.进行外骨骼摆动腿实验,结果表明,测得的人机交互信息经过卡尔曼滤波后,可以预测人体摆动腿的运动意图,外骨骼机器人能够实现对人体摆动腿关节轨迹的跟随,所提方法可行.

     

    Abstract: To accurately acquire human motion intent for exoskeleton control, torque sensors are used to measure human-robot interaction information. Based on the single pendulum model of human swing leg, the motion trajectory of lower limb joints is obtained, which is predicted with Kalman filter to compensate the delay of motion intent. The PD (proportional-derivative) control law is used to control exoskeleton to track the joint trajectory of human swing leg, and real-time position of the exoskeleton joint is fed back by the encoder, which forms a closed-loop position control. Experiments on the exoskeleton swing leg are conducted and the results show that motion intent of human swing leg can be predicted with the measured human-robot interaction information using Kalman filter, and the exoskeleton robot can achieve the joint trajectory tracking of human swing leg. Therefore, the proposed method is effective.

     

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