CAREER: Control and Fractal-based Stability of Dynamic Vision-Based Aperiodic Legged Locomotion

这个教师早期职业发展（CAREER）补助金的目标是使有腿机器人在崎岖的地形上像雪豹，山羊和人类跑酷专家一样稳定和敏捷。该项目考虑了实现这一目标的两个关键问题。首先是机器人如何使用摄像头来决定下一步在哪里以及如何进行。第二个是如何确保小的绊倒和滑倒引起的不安随着时间的推移逐渐消失，而不是越来越大，最终导致跌倒。第一个问题将通过观察人类的视觉和运动是如何联系起来的，并将这种关系转化为腿式机器人来部分回答。第二个问题需要扩展当前关于跑步和步行的数学思想。例如，一个有腿机器人可能会沿着一条干燥的小路跑，慢慢地走过一片泥泞，然后小心地跳上踏脚石穿过一条小溪。目前的方法假装每一个基本步骤都是永远重复的。相比之下，这个项目允许以任何顺序组合不同的基本步骤模式。如果没有能力在现实地形中导航并对意外障碍物做出反应，腿式机器人将被限制在高度受控的环境中。该项目将帮助这些机器人进入现实世界，用于涉及探索和安全的任务，在工厂和仓库中移动，并协助人类完成日常任务。该项目的教育部分将通过在增强创造力的限制下进行的竞争性学生活动来教授动力学和控制方面的专业技能。该项目将为高动态机器人创建控制技术，这些机器人可以在极端地形上快速运行，并在步行，跑步，跳跃，跳跃和着陆之间进行过渡。这些运动需要基于感知的世界信息在动态行为之间快速切换。扩展目前的理论需要分析所产生的非周期运动。 该项目调用基于分形的理论来定义和证明形式的稳定性保证。该项目还结合了深度学习和形式控制理论，对人类视觉-运动整合进行建模和仿真，并将其转化为腿式机器人。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

The goal of this Faculty Early Career Development (CAREER) grant is to enable legged robots that are as sure-footed and agile on rough terrain as snow leopards, mountain goats, and human parkour experts. The project considers two critical questions towards achieving this goal. The first is how the robot can use cameras to decide where and how to take its next few steps. The second is how to make sure that upsets caused by small stumbles and slips fade away over time, instead of growing bigger and eventually causing a fall. The first question will be partially answered by observing how vision and locomotion are connected in humans, and translating that relationship to legged robots. The second question requires extending current mathematical ideas about running and walking. For example, a legged robot might run along a dry path, walk slowly to cross a muddy patch, and then carefully hop on stepping-stones to cross a stream. Current approaches pretend that each basic step is repeated forever. In contrast, this project allows different basic patterns of steps to be combined in any order. Without the ability to navigate realistic terrain and respond to unplanned obstacles, legged robots will be restricted to highly controlled settings. This project will help bring these robots into real-world usage, for tasks involving exploration and security, moving in factories and warehouses, and assisting humans in everyday tasks. The education portion of this project will teach expert skills in dynamics and control through competitive student activities under creativity-enhancing constraints.This project will create control techniques for highly dynamic robots that can run rapidly on extreme terrain, and transition between walking, running, vaulting, jumping, and landing on discrete footholds. These movements require rapid switching between dynamical behaviors based on perceived information of the world. An extension of current theory is needed to analyze the resulting aperiodic locomotion. This project invokes fractal-based theory to define and prove formal stability guarantees. The project also unites deep learning and formal control theory to model and emulate human visual-motor integration for translation to legged robots.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.