Collaborative Research: Intelligent and Agile Robotic Legged Locomotion in Complex Environments: From Planning to Safety and Robust Control

协作研究：复杂环境下智能敏捷的机器人腿式运动：从规划到安全和鲁棒控制

基本信息

批准号：
1923239
负责人：
Aaron Ames
金额：
$ 34.19万
依托单位：
California Institute of Technology
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1923239&HistoricalAwards=false
关键词：
Collaborative Research Intelligent Agile Robotic

项目摘要

This project will advance the progress of science, national prosperity and welfare, by enhancing walking capabilities of robotic systems. While legged robots are becoming more mechanically sophisticated, their agility, dexterity, and dynamic stability are still far from capabilities of their biological counterparts (such as animals and humans) in real world situations. The overarching goal of this project is to create a formal foundation to enable intelligent and fast motion planning, safety, and robust control for a multitude of agile behaviors of autonomous legged machines in real-world situations. This research will have broad societal impact through seamless integration of control theory, optimization, and intelligence to enable ubiquitous use of increasingly sophisticated robots. Such robots will be able to effectively assist, or serve as remote extensions of, humans in situations of extreme danger, such as industrial accidents and natural disasters. The integrated educational plan will have broad impact by designing new courses. STEM-based outreach initiative for K-12 students, teachers, and under-represented minorities, and engagement of undergraduate students in research.The project will advance knowledge in the largely unexplored field of intelligent and robust legged locomotion through key innovations in controls and analysis. Intelligent and fast motion planning algorithms, based on hybrid systems theory, optimization, and perception, will be created for dynamical models of legged locomotion in complex environments. The project will address the synthesis of robust-optimal controllers, based on nonlinear control and matrix inequalities, for agile and dexterous locomotion. It will also create safety-critical control algorithms, based on set invariance and quadratic programming, for real-world implementation of robust controllers that guarantee obstacle avoidance in unknown environments. To bridge the gap between theory and implementation, this research will transfer the theoretical innovations into practice through experiments with two advanced quadrupedal and bipedal robots in researchers' laboratories at Virginia Tech and Caltech sites.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.

该项目将通过增强机器人系统的步行能力来推动科学，国家繁荣和福利的进步。尽管腿部机器人在机械上变得越来越复杂，但它们的敏捷性，敏捷性和动态稳定性仍然远非其生物学对应物（例如动物和人类）在现实世界中的能力。该项目的总体目标是为在现实世界中的自主腿机器的多种敏捷行为促进智能和快速的计划，安全性和强大的敏捷行为。这项研究将通过无缝整合控制理论，优化和智力来产生广泛的社会影响，从而无处不在使用日益复杂的机器人。这样的机器人将能够有效地协助或充当极端危险的人类的远程扩展，例如工业事故和自然灾害。综合教育计划将通过设计新课程产生广泛的影响。基于STEM的K-12学生，教师和代表性不足的少数群体以及本科生参与研究的计划。该项目将通过控制和分析中的关键创新来推进在很大程度上未开发的智能和强大的腿部运动领域的知识。基于混合系统理论，优化和感知的智能运动计划算法将用于复杂环境中的腿部运动的动态模型。该项目将针对基于非线性控制和基质不等式的鲁棒最佳控制器的合成，以解决敏捷和灵活的运动。它还将基于设置不变性和二次编程创建安全 - 关键控制算法，以实现强大的控制器的实际实现，以确保在未知环境中避免障碍物。为了弥合理论与实施之间的差距，这项研究将通过在弗吉尼亚理工大学和加州理工学院的研究人员实验室中对两个高级四足动物和两足动物机器人进行实验，将理论创新转移到实践中。该奖项反映了NSF的立法任务，并被认为是通过基金会的智力效果和广泛的评估来进行评估，并获得了评估。

项目成果

期刊论文数量（39）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Distributed Data-Driven Predictive Control for Multi-Agent Collaborative Legged Locomotion

DOI：
10.1109/icra48891.2023.10160914
发表时间：
2022-11
期刊：
2023 IEEE International Conference on Robotics and Automation (ICRA)
影响因子：
0
作者：
Randall T. Fawcett;Leila Amanzadeh;Jeeseop Kim;A. Ames;K. Hamed
通讯作者：
Randall T. Fawcett;Leila Amanzadeh;Jeeseop Kim;A. Ames;K. Hamed

Safety-Critical Coordination for Cooperative Legged Locomotion via Control Barrier Functions

通过控制屏障功能实现协作腿式运动的安全关键协调

DOI：
10.1109/iros55552.2023.10341987
发表时间：
2023
期刊：
IEEE
影响因子：
0
作者：
Kim, Jeeseop;Lee, Jaemin;Ames, Aaron D.
通讯作者：
Ames, Aaron D.

Multi-Layered Safety for Legged Robots via Control Barrier Functions and Model Predictive Control

DOI：
10.1109/icra48506.2021.9561510
发表时间：
2020-10
期刊：
2021 IEEE International Conference on Robotics and Automation (ICRA)
影响因子：
0
作者：
R. Grandia;Andrew J. Taylor;A. Ames;Marco Hutter
通讯作者：
R. Grandia;Andrew J. Taylor;A. Ames;Marco Hutter

SLIP Walking Over Rough Terrain via H-LIP Stepping and Backstepping-Barrier Function Inspired Quadratic Program

SLIP 通过 H-LIP 步进和反步障碍函数启发二次规划在崎岖地形上行走

DOI：
10.1109/lra.2021.3061385
发表时间：
2021
期刊：
IEEE Robotics and Automation Letters
影响因子：
5.2
作者：
Xiong, Xiaobin;Ames, Aaron
通讯作者：
Ames, Aaron

Learning to Control an Unstable System with One Minute of Data: Leveraging Gaussian Process Differentiation in Predictive Control

DOI：
10.1109/iros51168.2021.9636786
发表时间：
2021-03
期刊：
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
影响因子：
0
作者：
I. D. Rodriguez;Ugo Rosolia;A. Ames;Yisong Yue
通讯作者：
I. D. Rodriguez;Ugo Rosolia;A. Ames;Yisong Yue