Motion Path Planning of Wearable Lower Limb Exoskeleton Robot Based on Feature Description

Wearable lower extremity exoskeleton robot is a kind of training equipment designed for the disabled or powerless in the lower extremity. In order to improve the environmental adaptability of the robot and better meet the use habits of patients, it is necessary to plan and design the movement path, and a movement path planning model of wearable lower extremity exoskeleton robot based on feature description is proposed, which describes the objects with different wearing frequencies and training intensities. Taking the wearer's natural walking gait as the constraint feature quantity and the control object model, the spatial planning and design of exoskeleton structures such as hip joint, knee joint and ankle joint are adopted, and the traditional single-degree-of-freedom rotating pair is replaced by a four-bar mechanism, which improves the bionic performance of the knee joint. Combining the feature description and the spatial planning algorithm model, an error compensation method based on iterative least square method is adopted to identify geometric parameters. The feature identification model of robot moving path planning is constructed, and the adaptive strong coupling tracking identification and path planning of robot moving path are realized through feature description and spatial distance error identification results. The simulation test results show that the cooperative positioning error is reduced and the torque error is compensated in real time by using this method to plan the movement path of the wearable lower limb exoskeleton robot, which makes the robot obtain better movement planning effect and enhance the stability of the mechanism.

@article{Wang2023,
  title     = {Motion Path Planning of Wearable Lower Limb Exoskeleton Robot Based on Feature Description},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {6},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Ying Wang and Songyu Sui},
  doi       = {10.14569/IJACSA.2023.0140674},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140674}
}