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

该项目旨在实现双腿合作机器人，它们相互合作或与人合作，在复杂的环境中完成各种任务。部署下一代泛在协作机器人最具挑战性的问题之一是在复杂环境中的移动性。地球上一半以上的陆地是轮式车辆无法到达的，这促使使用双腿合作机器人进入这些环境，从而将机器人带入现实世界。带有机械手的腿部机器人可以组成合作机器人团队，帮助人类在生活的不同方面提供帮助。虽然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的发展成为可能，包括由协作机械臂、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，即腿部机器人天生不稳定，而腿部协作机器人团队的演化表现为高维和复杂的混合动力系统，这使得分布式控制和协调算法的设计复杂化。对于这些内在不稳定、驱动不足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着根本性的差距。该方案的总体目标是基于混杂系统理论、可扩展优化、健壮性和安全性关键控制建立形式化的基础，为具有机械手的协作腿协作机器人在复杂环境中完成各种任务开发分布式和分级反馈控制算法。拟议的研究将通过在机器人可以帮助人类的场景中部署无处不在的协作腿机器人，从而产生广泛的社会影响，例如灾难应对。综合教育计划将产生广泛的影响，根据结果设计一门新课程，利用机器人为K-12学生、教师和代表性不足的少数民族提供基于STEM的拓展。该项目旨在开发具有弹性和通用性的算法，以安全、稳定和可靠的方式解决腿部协作机器人团队的高维混合模型的协作运动和操作。这些算法将进一步使腿部协作机器人团队在对软件进行最小修改的情况下适应新的任务和环境。它将通过在可伸缩性和可定制化方面的具体目标和关键创新，促进在很大程度上未被探索的腿式协作机器人大规模混合系统模型的分布式控制领域的知识。智能和基于优化的运动规划算法将被创建用于腿部协作机器人的混合模型，以适应各种复杂的环境和新的情况。基于非线性、鲁棒和预测控制器的分布式和递阶控制算法，以及可扩展的凸优化，将被开发用于多智能体腿式机器人系统的协调，以实现灵活的运动模式，同时以灵活的方式操纵对象。最后，基于集不变性和凸优化的安全关键控制方法将与分级和分布式控制器相结合进行避障。为了弥合理论和实施之间的差距，拟议的研究将通过由多个四足机器人和一个人形机器人组成的联合机器人团队协同工作的实验，将理论创新转化为实践。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.