RI: Small: Agile Legged Robots: Composing Periodic Gaits for Aperiodic Locomotion

这个项目的总体目标是创建腿部敏捷性的计算算法，它被定义为机器人改变其速度、方向和/或步态(例如，从步行到跑步)的能力。从生物学上看，腿提供了一种高度灵活的运动模式，但没有任何一种腿部机器人能够达到与高性能人类或动物相提并论的性能。这种需求的持续存在是一个重要的问题，因为它限制了腿式机器人的用途；如果腿式机器人要在崎岖的地形中取代轮式机器人，那么启动、停止、改变方向、快速加/减速的需求都是必不可少的。这种敏捷机器人的一些应用是作为第一反应人员、搜索和救援人员以及产业工人。此外，这项研究将为多名本科生提供培训机会，并与在校儿童进行接触，包括传统上在科学、技术、工程和数学(STEM)中代表性不足的目标人群。实现敏捷性的关键思想是结合稳态(周期)步态来实现非稳态步态。其基本原理是，在计算方面，稳定状态的步态比非稳定步态的计算成本至少低一个数量级。这项研究的具体目标是创建工具，用于(1)使用稳态步态控制器和相关的稳定控制器填充整个状态空间，(2)在稳态步态之间转换以创建灵活步态，以及(3)该方法的实验验证。一种新的Lyapunov函数公式允许创建稳定控制器和估计吸引区域(ROA)。然后，这些ROA将根据它们的重叠区域按顺序组成，以创建用于敏捷移动的过渡控制器。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The overall objective of this project is to create computational algorithms for legged agility, which is defined as the robots ability to change its speed, direction, and/or gait (e.g., walk to run). As evident from biology, legs provide a highly agile mode of locomotion, yet no legged robot has been able to achieve performance comparable to highly performing humans or animals. The continued existence of this need is an important problem because it limits the usefulness of legged robots; the need to start, stop, change directions, accelerate/decelerate quickly are all essential if legged robots are to replace wheeled robots in rough terrain. Some applications of such agile robots are, as first responders, as search and rescue personnel, and as industrial workers. In addition, this research will provide training opportunities for multiple undergraduate students and outreach to school children including target populations that are traditionally under-represented in Science, Technology, Engineering, and Mathematics (STEM). The key idea to achieve agility is to combine steady-state (periodic) gaits to achieve non-steady gaits. The rationale is that computation-wise, steady-state gaits are at least an order of magnitude cheaper to compute than non-steady gaits. The specific aims of this research are to create tools for (1) filling the entire state space with steady-state gait controllers and associated stabilizing controllers, (2) transitioning between steady-state gaits to create agile gaits and (3) experimental validation of the approach. A novel formulation of the Lyapunov function allows the creation of stabilizing controllers and estimating the regions of attraction (ROA). Then these ROAs will be composed sequentially based on their overlapping regions to create transition controllers for agile locomotion.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.