CAREER: Formalizing the Concept of Teamwork in Heterogeneous Multi-Robot Systems

Temple University, Philadelphia PA

Investigators

Abstract

A new field of study, multi-robot analytics, provides an unconventional approach to developing effective multi-robots teams. Multi-robot analytics greatly extends the concepts of sport analytics, a data-driven analytical decision-making methodology for improving performance in team sports that has transformed the study and business of athletics, to study how robots within a team coordinate, how to develop these multi-robot teams, and how to field a team most efficiently. This matters greatly in search-and-rescue emergencies, e.g., a building collapse or a natural disaster, where fielding the most effective available team possible would make a significant difference in the outcome and overall safety. This Faculty Early Career Development (CAREER) project creates the technical foundations for this new field of research and its application, promotes the participation in the STEM disciplines underpinning robotics by high-school students near Temple University, and works with local businesses and technology companies in the Philadelphia region to build a strong local workforce in the rapidly growing field of advanced robotics. This project develops the cyber-physical foundations and the computational framework for multi-robot analytics, which will lead to the development of coordination and control strategies for heterogeneous multi-robot systems that effectively use the relative strengths of each individual robot and that generalize to new team compositions and situations. The result of this project includes a collection of standardized test scenarios, an extensible simulation environment, new metrics to assess the performance of multi-robot systems, and a multi-agent reinforcement learning framework that will maximize overall performance across a range of scenarios. These tools are provided in an open-source software development toolkit that will be shared with the larger research community to advance the study of multi-robot systems and their effective use. Finally, this project will devise methods to predict the expected performance of multi-robot systems in challenging new scenarios and will develop novel metrics for multi-agent reinforcement learning that properly attribute each agent’s contribution to the team’s performance. Collectively, these efforts allow for a nuanced and analytical understanding of how to compose heterogeneous multi-robot systems and design effective team coordination and control strategies. This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE). 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.