基于多信息融合的下肢外骨骼机器人感知系统研究

人体运动感知系统是外骨骼机器人柔顺控制和人机耦合的关键。通过分析外骨骼机器人的下肢动态模型，提出了一种支撑相选择位置控制、摆动相选择交互力导纳控制的人机协同控制方法。根据该方法开发了一种基于多信息融合的下肢外骨骼机器人感知系统，以人体下肢关节角度、人机交互力和足底压力为运动状态感知量，利用变增益卡尔曼滤波算法和Savitzky-Golay滤波算法分别对IMU传感器数据进行姿态角度解算和对FSR压力数据进行预处理，获得步态特征并通过试验来验证该方法的可靠性。试验结果表明：角度解算算法具有高精度、高稳定性的特点，交互力感知方法和FSR压力数据处理算法具有可行性，验证了所开发的感知系统能够可靠地获取关节角度、人机交互力和足底压力并融合处理，以及准确地识别穿戴者的步态特征。研究结果为外骨骼机器人感知系统的优化提供一定参考，促进了外骨骼机器人控制系统及策略的发展。

Research on the sensing system of lower limb exoskeleton robot based on multi-information fusion

The sensing system of body movement is the key to realize compliant control and human-robot coupling for the exoskeleton robot. The dynamic model of the lower limb was analyzed and the cooperative control method was brought forward. Position control method was applied in the support phase, and the interactive-force based admittance control method was applied in the swing phase. According to the method, the sensing system of lower limb exoskeleton robot based on multi-information fusion was developed, which employed joints angle of body, human-machine interaction force and plantar pressure as perceptual parameters. Using the variable-gain Kalman fliter algorithm to process the angle measured by IMU sensor and the Savitzky-Golay filter algorithm to process the pressure measured by FSR, the gait characteristic was acquired and the test was carried out to verify the reliability of the method. The experimental results showed that the attitude angle calculating algorithm of IMU data had the characteristics of high precision and good stability, and the human-robot interactive method and the FSR pressure data processing algorithm were feasible, which meant that the sensing system had the reliable ability to acquire and fuse attitude angle, interaction force and plantar pressure and identify the wearer's gait accurately. The results can provide a reference for optimizing the sensing system of exoskeleton robot and promote the development of the control systems and strategies of exoskeleton robot.