Extending the Benefits of Parallel Elasticity across Multiple Actuation Tasks: A Geometric and Optimization-Based Approach
<|reference_start|>Extending the Benefits of Parallel Elasticity across Multiple Actuation Tasks: A Geometric and Optimization-Based Approach: A spring in parallel with an effort source (e.g., electric motor or human muscle) can reduce its energy consumption and effort (i.e., torque or force) depending on the spring stiffness, spring preload, and actuation task. However, selecting the spring stiffness and preload that guarantees effort or energy reduction for an arbitrary set of tasks is a design challenge. This work formulates a convex optimization problem to guarantee that a parallel spring reduces the root-mean-square source effort or energy consumption for multiple tasks. Specifically, we guarantee the benefits across multiple tasks by enforcing a set of convex quadratic constraints in our optimization variables -- the parallel spring stiffness and preload. These quadratic constraints are equivalent to ellipses in the stiffness and preload plane, any combination of stiffness and preload inside the ellipse represents a parallel spring that minimizes effort source or energy consumption with respect to an actuator without a spring. This geometric interpretation intuitively guides the stiffness and preload selection process. We analytically and experimentally prove the convex quadratic function of the spring stiffness and preload. As applications, we analyze the stiffness and preload selection of a parallel spring for a knee exoskeleton using human muscle as the effort source and a prosthetic ankle powered by electric motors. To promote adoption, the optimization and geometric methods are available as supplemental open-source software that can be executed in a web browser.<|reference_end|>
@article{yang2024extending,
    title={Extending the Benefits of Parallel Elasticity across Multiple Actuation
  Tasks: A Geometric and Optimization-Based Approach},
    author={Kang Yang, Myia Dickens, James Schmiedeler, and Edgar Bol'ivar-Nieto},
    journal={arXiv preprint arXiv:2409.08889},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.08889},
    primaryClass={cs.RO}
}