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

该项目旨在实现有腿的协作机器人，它们可以在复杂的环境中相互协作或与人合作，完成各种任务。在部署下一代无处不在的协同机器人时，最具挑战性的问题之一是在复杂环境中的移动性。地球上超过一半的陆地是轮式车辆无法进入的，这促使利用有腿的协作机器人进入这些环境，从而将机器人带入现实世界。带有操纵器的有腿机器人可以组成协作机器人团队，在人类生活的不同方面提供帮助。尽管重要的理论和技术进步使复杂机器人系统的分布式控制器得以发展，包括由协作机器人手臂、多指机器人手、飞行器和地面车辆组成的多智能体系统，但理解如何控制协作腿智能体是一个悬而未决的问题。在这一领域实现协调的挑战源于这样一个事实，即有腿机器人本身是不稳定的，并且有腿的协同机器人团队的发展是由高维和复杂的混合动力系统来代表的，这使得用于控制和协调的分布式控制算法的设计复杂化。对于这些本质上不稳定、欠驱动和复杂的混合动力系统的安全关键控制，分布式控制算法的知识存在根本差距。本提案的总体目标是建立一个正式的基础，基于混合系统理论，可扩展优化，鲁棒和安全关键控制，开发分布式和分层反馈控制算法，用于具有机械手的协作式腿式协同机器人在复杂环境中完成各种任务。拟议的研究将通过正式的原则和安全关键部署无处不在的协作式腿机器人，在机器人可以帮助人类的场景中，例如，灾难响应，产生广泛的社会影响。综合教育计划将根据结果设计新课程，利用机器人为K-12学生、教师和弱势群体提供基于stem的服务，从而产生广泛的影响。该项目旨在开发弹性和通用算法，以安全、稳定和可靠的方式解决有腿协作机器人团队的高维混合模型的协同运动和操纵问题。这些算法将进一步使有腿的协作机器人团队能够在对软件进行最小修改的情况下适应新的任务和环境。它将通过在可扩展性和可定制性方面的具体目标和关键创新，推进在大量未开发的有腿协作机器人的大规模混合系统模型的分布式控制领域的知识。将为腿式协作机器人的混合模型创建智能和基于优化的运动规划算法，以适应各种复杂环境和新情况。基于非线性、鲁棒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，将用于多智能体腿机器人系统的协调，以实现灵活的运动模式，同时以灵巧的方式操纵物体。最后，将基于集合不变性和凸优化的安全关键控制方法与分层和分布式避障控制器相结合。为了弥合理论与实现之间的差距，拟议的研究将通过一个由多个四足机器人和一个人形机器人组成的协作机器人团队的实验，将理论创新转化为实践。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。

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.