A Method of Joint Angle Estimation Using Only Relative Changes in Muscle Lengths for Tendon-driven Humanoids with Complex Musculoskeletal Structures
<|reference_start|>A Method of Joint Angle Estimation Using Only Relative Changes in Muscle Lengths for Tendon-driven Humanoids with Complex Musculoskeletal Structures: Tendon-driven musculoskeletal humanoids typically have complex structures similar to those of human beings, such as ball joints and the scapula, in which encoders cannot be installed. Therefore, joint angles cannot be directly obtained and need to be estimated using the changes in muscle lengths. In previous studies, methods using table-search and extended kalman filter have been developed. These methods express the joint-muscle mapping, which is the nonlinear relationship between joint angles and muscle lengths, by using a data table, polynomials, or a neural network. However, due to computational complexity, these methods cannot consider the effects of polyarticular muscles. In this study, considering the limitation of the computational cost, we reduce unnecessary degrees of freedom, divide joints and muscles into several groups, and formulate a joint angle estimation method that takes into account polyarticular muscles. Also, we extend the estimation method to propose a joint angle estimation method using only the relative changes in muscle lengths. By this extension, which does not use absolute muscle lengths, we do not need to execute a difficult calibration of muscle lengths for tendon-driven musculoskeletal humanoids. Finally, we conduct experiments in simulation and actual environments, and verify the effectiveness of this study.<|reference_end|>
@article{kawaharazuka2024a,
    title={A Method of Joint Angle Estimation Using Only Relative Changes in Muscle
  Lengths for Tendon-driven Humanoids with Complex Musculoskeletal Structures},
    author={Kento Kawaharazuka, Shogo Makino, Masaya Kawamura, Yuki Asano, Kei
  Okada, Masayuki Inaba},
    journal={arXiv preprint arXiv:2404.14100},
    year={2024},
    doi={10.1109/HUMANOIDS.2018.8625002},
    archivePrefix={arXiv},
    eprint={2404.14100},
    primaryClass={cs.RO}
}