NRI: FND: Robust and Scalable Planning for Agile and Collaborative Robot Teammates in Complex Environments

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Collaborative robot teams may one day become ubiquitous in our lives, accompany humans in challenging environments, perform a wide range of task, and even surpsass human capabilities where needed. This potential offers a significant opportunity for co-robot teams to collaborate effectively and reliably with humans for daily life and rescue tasks. However, it remains challenging to design robotic decision-making and planning algorithms that are scalable to complex real world scenarios and that provide formal safety assurances. Formal decision-making methods are critical in reasoning about how robot teammates collaborate in unstructured environments, e.g., how a ground robot coordinates with aerial robots for search and rescue tasks, or how a humanoid robot cooperates with humans to deliver supplies in confined space. This challenge will be accentuated when robots are required to perform tasks at or beyond human-level dexterity and proficiency. The overarching vision of this interdisciplinary project is to achieve robust and scalable algorithms for ubiquitous co-robots maneuvering in complex environments while effectively collaborating with humans for various tasks. A wide range of applications will be targeted, including home care applications and search and rescue tasks. Although this project focuses on legged and aerial robots as design examples, the approaches are targeting a broader range of robotic systems including wheeled robots, manipulators, and fielding environments such as networked control systems and transportation systems. This project also features an integrated education plan involving the creation of a new graduate course on formal control methods for robotic systems as well as STEM-based outreach initiative for women and underrepresented minority students.This project aims at advancing planning and decision-making algorithms of heterogeneous and collaborative robotic systems to achieve ubiquitous tasks in unstructured, human-in-the-scene, and dynamically changing environments. This project will target the following three objectives: devise robust, non-periodic motion planners based on kinodynamic planning and control barrier certificates for versatile terrestrial and aerial maneuvering; synthesize game-theoretic, reactive, and robust task planners in response to diverse environmental events; and propose a novel multi-agent decision-making approach decomposing the whole robot team into multiple sub-teams. This integrated planning framework will adopt algorithmic methods at the interaction of state-of-the-art formal methods, multi-agent systems, robust control, and machine learning. The proposed planning theory will focus on robustness and scalability reasoning which are of high importance, as they open up the opportunity for achieving complex multi-robot maneuverability and cooperation tasks while reasoning about formal guarantees including provable correctness and assured safety. Under the hypothesis of sufficient computational resources, the proposed framework will make task decisions and generate motion plans in real-time satisfying the required specifications at both mission and task planning levels. The deliverables from this project include decision-making and planning theories envisioned for unified, heterogeneous legged and aerial robots with open-sourced algorithms and experiment implementations.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.