Design and Evaluation of a Compact 3D End-effector Assistive Robot for Adaptive Arm Support
<|reference_start|>Design and Evaluation of a Compact 3D End-effector Assistive Robot for Adaptive Arm Support: We developed a 3D end-effector type of upper limb assistive robot, named as Assistive Robotic Arm Extender (ARAE), that provides transparency movement and adaptive arm support control to achieve home-based therapy and training in the real environment. The proposed system composes five degrees of freedom, including three active motors and two passive joints at the end-effector module. The core structure of the system is based on a parallel mechanism. The kinematic and dynamic modeling are illustrated in detail. The proposed adaptive arm support control framework calculates the compensated force based on the estimated human arm posture in 3D space. It firstly estimates human arm joint angles using two proposed methods: fixed torso and sagittal plane models without using external sensors such as IMUs, magnetic sensors, or depth cameras. The experiments were carried out to evaluate the performance of the two proposed angle estimation methods. Then, the estimated human joint angles were input into the human upper limb dynamics model to derive the required support force generated by the robot. The muscular activities were measured to evaluate the effects of the proposed framework. The obvious reduction of muscular activities was exhibited when participants were tested with the ARAE under an adaptive arm gravity compensation control framework. The overall results suggest that the ARAE system, when combined with the proposed control framework, has the potential to offer adaptive arm support. This integration could enable effective training with Activities of Daily Living (ADLs) and interaction with real environments.<|reference_end|>
@article{yang2024design,
    title={Design and Evaluation of a Compact 3D End-effector Assistive Robot for
  Adaptive Arm Support},
    author={Sibo Yang, Lincong Luo, Wei Chuan Law, Youlong Wang, Lei Li and Wei
  Tech Ang},
    journal={arXiv preprint arXiv:2404.03149},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.03149},
    primaryClass={cs.RO}
}