赵佳伟, 朱立忠, 陈万鑫, 张弼, 赵新刚. 基于动态运动基元的6自由度下肢外骨骼步态轨迹规划与控制策略[J]. 信息与控制, 2024, 53(1): 33-46. DOI: 10.13976/j.cnki.xk.2023.2522
引用本文: 赵佳伟, 朱立忠, 陈万鑫, 张弼, 赵新刚. 基于动态运动基元的6自由度下肢外骨骼步态轨迹规划与控制策略[J]. 信息与控制, 2024, 53(1): 33-46. DOI: 10.13976/j.cnki.xk.2023.2522
ZHAO Jiawei, ZHU Lizhong, CHEN Wanxin, ZHANG Bi, ZHAO Xingang. Gait Trajectory Planning and Control Strategy of 6-DOF Lower Limb Exoskeleton Based on Dynamic Movement Primitives[J]. INFORMATION AND CONTROL, 2024, 53(1): 33-46. DOI: 10.13976/j.cnki.xk.2023.2522
Citation: ZHAO Jiawei, ZHU Lizhong, CHEN Wanxin, ZHANG Bi, ZHAO Xingang. Gait Trajectory Planning and Control Strategy of 6-DOF Lower Limb Exoskeleton Based on Dynamic Movement Primitives[J]. INFORMATION AND CONTROL, 2024, 53(1): 33-46. DOI: 10.13976/j.cnki.xk.2023.2522

基于动态运动基元的6自由度下肢外骨骼步态轨迹规划与控制策略

Gait Trajectory Planning and Control Strategy of 6-DOF Lower Limb Exoskeleton Based on Dynamic Movement Primitives

  • 摘要: 针对下肢刚性运行过程中的步态轨迹规划问题,提出了一种基于动态运动基元的步态轨迹在线规划方法和控制策略。首先,使用足底压力进行步态相位分割;其次,使用线性倒立摆模型确定落地点位置,再运用逆运动学规划确定各关节角度,使用动态运动基元算法对以前的各个步态相位的运动轨迹进行在线学习;再次,将前面得到的关节角度作为目标终点进行实时步态轨迹规划;最后,根据生成的曲线按照控制策略对外骨骼控制。使用6自由度外骨骼对算法进行了验证,结果表明,使用所提算法可以有效根据步态相位的变换对步态轨迹进行实时在线学习和生成,同时可以保证一定程度的运动稳定性。

     

    Abstract: To solve the problem of gait trajectory planning during the rigid operation of lower limbs, we propose an online gait trajectory planning method and control strategy based on dynamic movement primitives. The technique uses plantar pressure to segment the gait phase, a linear inverted pendulum model to determine the location of the landing site, inverse kinematic programming to determine the angle of each joint, and a dynamic movement primitives algorithm to learn online the trajectory of each previous gait phase. We apply the previously obtained joint angle as the target endpoint for real-time gait trajectory planning. Finally, we control the exoskeleton according to the control strategy from the generated curves. The algorithm is verified using a 6-DOF exoskeleton, and the results show that the proposed algorithm can effectively learn and generate the gait trajectory online according to the transformation of the gait phase, ensuring a certain degree of motion stability.

     

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