机器人Issue(3)：304-309,6.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 of Human Motion Intent
摘要
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.关键词
人机交互/运动意图/外骨骼/卡尔曼滤波/力矩传感器Key words
human-robot interaction/motion intent/exoskeleton/Kalman filter/torque sensor分类
信息技术与安全科学引用本文复制引用
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,(3):304-309,6.基金项目
国家自然科学基金资助项目（61105088）． （）
