The biomechanical energy harvester is expected to harvest the electric energies from human motions. A tradeoff between harvesting energy and keeping the user's natural movements should be balanced via optimization techniques. In previous studies, the hardware itself has been specialized in advance for a single task like walking with constant speed on a flat. A key ingredient is Continuous Variable Transmission (CVT) to extend it applicable for multiple tasks. CVT could continuously adjust its gear ratio to balance the tradeoff for each task; however, such gear-ratio optimization problem remains open yet since its optimal solution may depend on the user, motion, and environment. Therefore, this paper focuses on a framework for data-driven optimization of a gear ratio in a CVT-equipped biomechanical energy harvester. Since the data collection requires a heavy burden on the user, we have to optimize the gear ratio for each task in the shortest possible time. To this end, our framework is designed sample-efficiently based on the fact that the user encounters multiple tasks, which are with similarities with each other. Specifically, our framework employs multi-task Bayesian optimization to reuse the optimization results of the similar tasks previously optimized by finding their similarities. Through experiments, we confirmed that, for each task, the proposed framework could achieve the optimal gear ratio of around 50~\% faster than one by random search, and that takes only around 20~minutes. Experimental results also suggested that the optimization can be accelerated by actively exploiting similarities with previously optimized tasks.
翻译:生物机械能源采集器预计将从人类运动中获取电能。 收获能源与保持用户自然运动之间的平衡应当通过优化技术实现平衡。 在以前的研究中, 硬件本身已经预先专门用于单项任务, 如在一个固定的固定速度行走。 关键成分是连续变量传输( CVT), 以将其扩展为适用于多项任务。 CVT 可以不断调整其齿轮比率, 以平衡每个任务之间的权衡; 然而, 这种齿轮优化问题仍然开放, 因为它的最佳解决方案可能取决于用户、 运动和环境。 因此, 本文侧重于一个以数据驱动为驱动的框架, 以加速优化CVT装备的生物机械能源采集器中的齿轮比率。 由于数据收集工作需要用户承担沉重的负担, 我们必须在尽可能短的时间内优化每项任务的齿轮比。 为此, 我们的框架的设计具有样本效率, 其基础是用户只能遇到多重任务, 而这些任务彼此相似。 具体地说, 我们的框架使用多塔克巴耶斯优化至再利用。 围绕50种最优化框架, 最优化后, 最优化的每个任务都要通过最优化的类似的任务, 找到最优化的任务, 最优化的比最优化的任务, 最优化的比最优化的任务 最优化的任务 最优化的任务 最优化的任务 最优化的任务 最优化的任务, 最优化的任务 最优化的任务 最优化的任务 最优化的任务 最优化的任务 最优化的任务 最优化的任务 最优化的任务 最优化的任务 。