NRI: FND: COLLAB: Hierarchical Safe, and Distributed Feedback Control of Multiagent Legged Robots for Cooperative Locomotion and Manipulation

NRI：FND：COLLAB：用于协作运动和操纵的多智能腿机器人的分层安全分布式反馈控制

• 批准号: 1924526
• 负责人: Aaron Ames
• 金额: $ 37.5万
• 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: 2019-09-15 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1924526&HistoricalAwards=false
• 关键词: NRI; FND; COLLAB; Hierarchical; Safe

The project aims to realize legged co-robots that cooperatively work with each other or people to achieve a variety of tasks in complex environments. One of the most challenging problems in deploying the next generation of ubiquitous co-robots is mobility in complex environments. More than half of the Earth's landmass is inaccessible to wheeled vehicles this motivates utilizing legged co-robots to access these environments and thus bring robots into the real world. Legged robots that are augmented with manipulators can form co-robot teams that assist humans in different aspects of their life. Although important theoretical and technological advances have enabled the development of distributed controllers for complex robot systems, including multiagent systems composed of collaborative robotic arms, multifingered robot hands, aerial vehicles, and ground vehicles, understanding how to control cooperative legged agents is an open problem. The challenges in achieving coordination in this domain stems from the fact that legged robots are inherently unstable, and the evolution of legged co-robot teams is represented by high-dimensional and complex hybrid dynamical systems which complicate the design of distributed control algorithms for control and coordination. There is a fundamental gap in knowledge of distributed control algorithms for safety-critical control of these inherently unstable, underactuated, and complex hybrid dynamical systems. The overarching goal of this proposal is to create a formal foundation, based on hybrid systems theory, scalable optimization, and robust and safety-critical control, to develop distributed and hierarchical feedback control algorithms for cooperative legged co-robots with manipulators to achieve a variety of tasks in complex environments. The proposed research will have broad societal impact through the formally principled and safety-critical deployment of ubiquitous collaborative legged robots in scenarios where robots can assist humans, e.g., disaster response. The integrated educational plan will have broad impact by designing a new course based upon the results, utilizing robots for STEM-based outreach for K-12 students, teachers, and under-represented minorities.The project aims to develop resilient and versatile algorithms that address cooperative locomotion and manipulation of high-dimensional hybrid models of legged co-robot teams in a safe, stable, and reliable manner. These algorithms will further enable legged co-robot teams to adapt to new tasks and environments with minimal modification to software. It will advance knowledge in the largely unexplored field of distributed control of large-scale hybrid system models of legged co-robots through specific objectives and key innovations in Scalability and Customizability. Intelligent and optimization-based motion planning algorithms will be created for hybrid models of legged co-robots to adapt to a wide variety of complex environments and new situations. Distributed and hierarchical control algorithms, based on nonlinear, robust and predictive controllers, together with scalable convex optimization, will be developed for coordination of multiagent legged robotic systems to enable agile locomotion patterns while manipulating objects in a dexterous manner. Finally, safety-critical control methods, based on set invariance and convex optimization, will be integrated with the hierarchical and distributed controllers for obstacle avoidance. To bridge the gap between theory and implementation, the proposed research will transfer the theoretical innovations into practice through experiments with a co-robot team consisting of multiple quadruped robots and one humanoid robot working collaboratively.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.

该项目旨在实现互相合作或人合作的腿部联合机器人，以在复杂的环境中完成各种任务。在复杂环境中，部署下一代无处不在的共同机器人最具挑战性的问题之一是移动性。超过一半的地球道路车辆无法进入车辆，这激发了利用腿部的共同机器人进入这些环境，从而将机器人带入现实世界。用操纵者增强的腿部机器人可以组成共同机器人团队，以帮助人类生活的各个方面。尽管重要的理论和技术进步使得为复杂机器人系统的分布式控制器开发，包括由协作机器人臂组成的多种系统系统，多边形机器人手，航空车辆和地面车辆，了解如何控制合作的腿部机构是一个开放的问题。 在该领域实现协调的挑战源于腿部机器人本质上不稳定的事实，而腿部共同机器人团队的演变则由高维且复杂的混合动力学系统表示，这使得分布式控制算法的设计复杂化，以控制和协调。在分布式控制算法的知识中，对于这些固有的不稳定，不稳定且复杂的混合动力学系统的安全性控制算法存在根本差距。 该提案的总体目标是基于混合系统理论，可扩展优化以及可靠和关键安全控制的正式基础，以开发与操作机构合作腿部共同运行的分布式和分层反馈控制算法，以在复杂环境中实现各种任务。拟议的研究将通过在机器人可以协助人类（例如灾难响应）的情况下，正式的原则性和安全关键的协作机器人的正式和安全关键部署产生广泛的社会影响。 The integrated educational plan will have broad impact by designing a new course based upon the results, utilizing robots for STEM-based outreach for K-12 students, teachers, and under-represented minorities.The project aims to develop resilient and versatile algorithms that address cooperative locomotion and manipulation of high-dimensional hybrid models of legged co-robot teams in a safe, stable, and reliable manner.这些算法将进一步使腿部的共同机器人团队能够适应新任务和环境，以最小的修改对软件进行修改。它将通过特定的目标和可伸缩性和可定制性的关键创新，在很大程度上促进在很大程度上未开发的大规模混合系统模型的分布式控制领域的知识。将为腿部共同机器人的混合模型创建基于智能和优化的运动计划算法，以适应各种复杂的环境和新情况。将开发基于非线性，稳健和预测控制器的分布式和分层控制算法，以及可扩展的凸优化，以协调多型腿部机器人系统，以启用敏捷的运动模式，同时以敏捷的方式操纵对象。最后，基于集合不变性和凸优化的安全 - 关键控制方法将与分层和分布式控制器集成以避免障碍物。 To bridge the gap between theory and implementation, the proposed research will tr​​ansfer the theoretical innovations into practice through experiments with a co-robot team consisting of multiple quadruped robots and one humanoid robot working collaboratively.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.

项目成果

Coupled Control Lyapunov Functions for Interconnected Systems, With Application to Quadrupedal Locomotion

互连系统的耦合控制李亚普诺夫函数及其在四足运动中的应用

• DOI: 10.1109/lra.2021.3065174
• 发表时间: 2021
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Ma, Wen-Loong;Csomay-Shanklin, Noel;Kolathaya, Shishir;Hamed, Kaveh Akbari;Ames, Aaron D.
• 通讯作者: Ames, Aaron D.

Online Learning of Unknown Dynamics for Model-Based Controllers in Legged Locomotion

• DOI: 10.1109/lra.2021.3108510
• 发表时间: 2021-10
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Yu Sun;Wyatt Ubellacker;Wen-Loong Ma;Xiang Zhang;Changhao Wang;Noel Csomay-Shanklin;M. Tomizuka;K. Sreenath;A. Ames

Data-driven Characterization of Human Interaction for Model-based Control of Powered Prostheses

人类交互的数据驱动表征，用于基于模型的动力假肢控制

• DOI: 10.1109/iros45743.2020.9341388
• 发表时间: 2020
• 期刊: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS
• 作者: Gehlhar, Rachel;Chen, Yuxiao;Ames, Aaron D.
• 通讯作者: Ames, Aaron D.

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)
• 作者: I. D. Rodriguez;Ugo Rosolia;A. Ames;Yisong Yue

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