Zhang Yuepeng, Cao Guangzhong, Wu Jun, Gao Bo, Xia Linzhong, Lu Chen, Wang Hui
School of Sino-German Robotics, Shenzhen Institute of Information Technology, Shenzhen 518172, China.
Inovance Industrial Robot Reliability Technology Research Institute, Shenzhen Institute of Information Technology, Shenzhen 518172, China.
Biomimetics (Basel). 2025 May 12;10(5):311. doi: 10.3390/biomimetics10050311.
To overcome the problem of insufficient adaptability to the motion environment of lower limb exoskeleton robots, this paper introduces computer vision technology into the motion control of lower limb exoskeleton robots and studies an obstacle-crossing-motion method based on detecting obstacle feature information. Considering the feature information of different obstacles and the distance between obstacles and robots, a trajectory planning method based on direct point matching was used to generate offline adjusted gait trajectory libraries and obstacle-crossing gait trajectory libraries. A lower limb exoskeleton robot obstacle-crossing motion decision-making algorithm based on obstacle feature information is proposed by combining gait constraints and motion constraints, enabling it to select appropriate motion trajectories in the trajectory library. The proposed obstacle-crossing-motion method was validated at three distances between the obstacle and the robot and with the feature information of four obstacles. The experimental results show that the proposed method can select appropriate trajectories from the trajectory library based on the detected obstacle feature information and can safely complete obstacle-crossing motions.
为克服下肢外骨骼机器人对运动环境适应性不足的问题，本文将计算机视觉技术引入下肢外骨骼机器人的运动控制中，研究了一种基于检测障碍物特征信息的越障运动方法。考虑不同障碍物的特征信息以及障碍物与机器人之间的距离，采用基于直接点匹配的轨迹规划方法生成离线调整步态轨迹库和越障步态轨迹库。通过结合步态约束和运动约束，提出了一种基于障碍物特征信息的下肢外骨骼机器人越障运动决策算法，使其能够在轨迹库中选择合适的运动轨迹。在障碍物与机器人之间的三个距离以及四种障碍物的特征信息条件下，对所提出的越障运动方法进行了验证。实验结果表明，该方法能够根据检测到的障碍物特征信息从轨迹库中选择合适的轨迹，并能够安全地完成越障运动。
