将并联弹性的益处扩展至多种驱动任务：一种基于几何与优化的方法
Extending the Benefits of Parallel Elasticity across Multiple Actuation Tasks: A Geometric and Optimization-Based Approach
AI总结：
本研究构建了一个凸优化问题，通过在弹簧刚度和预载上施加凸二次约束，保证并联弹簧在多种任务中降低动力源能耗或出力，并利用几何解释直观指导参数选择，已在膝关节外骨骼和假肢踝关节中验证。
摘要：
与动力源（如电机或人体肌肉）并联的弹簧可以降低其能耗和出力（即扭矩或力），具体效果取决于弹簧刚度、弹簧预载以及驱动任务。然而，选择能够保证在任意任务集中均实现出力或能耗降低的弹簧刚度和预载是一项设计挑战。本研究构建了一个凸优化问题，以保证并联弹簧能够降低多种任务的均方根动力源出力或能耗。具体而言，我们通过在优化变量（即并联弹簧刚度和预载）上施加一组凸二次约束，来确保其在多种任务中的益处。这些二次约束等价于刚度与预载平面中的椭圆；椭圆内部的任意刚度与预载组合，均代表一个相对于无弹簧驱动器能最小化动力源出力或能耗的并联弹簧。这种几何解释直观地指导了刚度与预载的选择过程。我们从解析和实验两方面证明了弹簧刚度与预载的凸二次函数性质。作为应用，我们分别以人体肌肉为动力源，分析了膝关节外骨骼并联弹簧的刚度与预载选择；以及以电机为动力，分析了电动假肢踝关节的并联弹簧刚度与预载选择。与我们框架相关的源代码已作为补充的开源软件提供。
英文摘要：
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. The source code associated with our framework is available as supplemental open-source software.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
