CAREER: Utilizing Physical Interactions to Improve Legged Mobility on Challenging Terrains

职业：利用身体互动来提高具有挑战性的地形上的腿部灵活性

• 批准号: 2240075
• 负责人: Feifei Qian
• 金额: $ 60万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2028-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2240075&HistoricalAwards=false
• 关键词: CAREER; Utilizing; Physical; Interactions; Improve

This Faculty Early Career Development (CAREER) award supports research that will create new methods to allow legged robots to utilize physical interactions to achieve high mobility on complex natural terrains. Robots are becoming increasingly better when moving on rigid, flat ground, but still struggle to move through soft sand, sticky mud, and rubble piles, limiting their capabilities in critical missions such as search and rescue, delivery, and explorations. The insights and new methods from this work will enable the development of next generation robots that can actively “harvest” environment interaction forces, or even large disturbances, to generate agile movements. This capability can significantly reduce robot control effort and computational complexity in challenging environments, and therefore empower legged robots for a broader range of applications important for national health, prosperity, and welfare, such as environment monitoring, earthquake rescue, and planetary exploration. Additionally, by translating part of this research into tangible education activities, this project will integrate classroom, laboratory, and real-world application experiences to inspire and engage a diverse group of students from middle school through graduate levels to form the next generation of engineers and scientists.Legged locomotion on natural deformable terrains is fundamentally challenging due to our limited understanding of the complex terrain responses. Upon robot leg contact, sand and soil can deform and flow like a fluid, or jam like a solid, while loosely embedded rocks and boulders can slide or rotate, causing robots to slip, stuck, or even flip over. To overcome this challenge, this research will integrate locomotion experiments, granular physics theory, and dynamical systems methods, to determine how intrinsic terrain properties and leg-terrain contact modes influence terrain responses and the resulting robot locomotion dynamics. This new knowledge will allow robots to incorporate predicted terrain forces as part of their locomotion control, to produce desired dynamics across different environments. Terrain force predictions will be validated by laboratory experiments, and the robot locomotion performance will be evaluated in both laboratory experiments and field testing at White Sands, NM.This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).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.

这个学院早期职业发展（Career）奖支持的研究将创造新的方法，使有腿的机器人利用物理相互作用，在复杂的自然地形上实现高机动性。机器人在坚硬平坦的地面上移动的能力越来越好，但在软沙、粘性泥浆和碎石堆中移动仍然很困难，这限制了它们在搜索、救援、运送和勘探等关键任务中的能力。这项工作的见解和新方法将使下一代机器人的发展能够积极地“收获”环境相互作用的力量，甚至是大的干扰，以产生敏捷的运动。这种能力可以在具有挑战性的环境中显著减少机器人的控制工作量和计算复杂性，从而使有腿机器人能够用于对国家健康、繁荣和福利至关重要的更广泛的应用，例如环境监测、地震救援和行星探索。此外，通过将部分研究转化为有形的教育活动，该项目将整合课堂，实验室和现实世界的应用经验，以激励和吸引从中学到研究生水平的不同群体的学生，形成下一代工程师和科学家。由于我们对复杂地形反应的理解有限，在自然可变形的地形上进行腿部运动从根本上具有挑战性。在机器人腿接触时，沙子和土壤会变形，像液体一样流动，或者像固体一样堵塞，而松散嵌入的岩石和巨石会滑动或旋转，导致机器人滑倒、卡住，甚至翻转。为了克服这一挑战，本研究将整合运动实验、颗粒物理理论和动力系统方法，以确定内在地形特性和腿-地形接触模式如何影响地形响应和由此产生的机器人运动动力学。这一新知识将使机器人能够将预测的地形力作为其运动控制的一部分，从而在不同的环境中产生所需的动力学。地形力预测将通过实验室实验进行验证，机器人的运动性能将在实验室实验和NM White Sands的现场测试中进行评估。该项目由跨部门机器人基础研究项目支持，由工程（ENG）和计算机与信息科学与工程（CISE）联合管理和资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Understanding Human Dynamic Sampling Objectives to Enable Robot-assisted Scientific Decision Making

• DOI: 10.1145/3623383
• 发表时间: 2023-09
• 期刊: ACM Transactions on Human-Robot Interaction
• 影响因子: 5.1
• 作者: Shipeng Liu;Cristina G. Wilson;Bhaskar Krishnamachari;Feifei Qian

Swift progress for robots over complex terrain

机器人在复杂地形上的快速进步

• DOI: 10.1038/d41586-023-00710-0
• 发表时间: 2023
• 期刊: Nature
• 影响因子: 64.8
• 作者: Li, Chen;Qian, Feifei
• 通讯作者: Qian, Feifei

Adaptation of Flipper-Mud Interactions Enables Effective Terrestrial Locomotion on Muddy Substrates

• DOI: 10.1109/lra.2023.3323123
• 发表时间: 2023-12
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Shipeng Liu;Boyuan Huang;Feifei Qian

Modelling Experts' Sampling Strategy to Balance Multiple Objectives During Scientific Explorations

• DOI: 10.1145/3610977.3635112
• 发表时间: 2024-03
• 期刊: Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction
• 作者: Shipeng Liu;Cristina G. Wilson;Zachary I. Lee;Feifei Qian

Downslope Weakening of Soil Revealed by a Rapid Robotic Rheometer

• DOI: 10.1029/2023gl106468
• 发表时间: 2024-01
• 期刊: Geophysical Research Letters
• 影响因子: 5.2
• 作者: John G. Ruck;Cristina G. Wilson;Thomas F. Shipley;Daniel Koditschek;Feifei Qian;D. Jerolmack