迈向高效且稳健的双足行走优化
Toward Efficient and Robust Biped Walking Optimization
AI总结：
本文研究双足机器人行走的高效与稳健优化，探讨步态的能耗与鲁棒性量化，以及运动轨迹与机器人参数的联合优化，通过五连杆平面行走模型验证效率与稳健性的平衡。
摘要：
实用的双足机器人运动需要同时具备能量效率和对变化与不确定性的鲁棒性。本文基于最近的轨迹优化研究，有两个主要目标：首先，展示考虑并量化步态的能耗与鲁棒性的重要性，以及优化名义运动轨迹和机器人设计参数及反馈控制策略的重要性；其次，通过五连杆平面行走模型的优化研究，提供关于提高效率与鲁棒性之间权衡和一般趋势的初步数据。在解决重要的开放性挑战时，特别讨论了在选择始终是近似优化的指标以及在结构和调节反馈控制时的选择影响。
英文摘要：
Practical bipedal robot locomotion needs to be both energy efficient and robust to variability and uncertainty. In this paper, we build upon recent works in trajectory optimization for robot locomotion with two primary goals. First, we wish to demonstrate the importance of (a) considering and quantifying not only energy efficiency but also robustness of gaits, and (b) optimization not only of nominal motion trajectories but also of robot design parameters and feedback control policies. As a second, complementary focus, we present results from optimization studies on a 5-link planar walking model, to provide preliminary data on particular trade-offs and general trends in improving efficiency versus robustness. In addressing important, open challenges, we focus in particular on discussions of the effects of choices made (a) in formulating what is always, necessarily only an approximate optimization, in choosing metrics for performance, and (b) in structuring and tuning feedback control.
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