CAREER: Bridging Self-Organized and Algorithmic Approaches to Multi-Robot Systems

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This project consists of research to address the limitations of traditional ways of programming groups of robots in order to make it easier to program them to solve problems in teams. The work is helping realize a future where robots address important applications such as those with life-saving, ecological, and national strategic elements (e.g., manufacturing, roboticized agriculture, planetary exploration). To do this, the research is establishing new connections between methods developed for thinking about very large data sets, mathematical models invented by physicists for small-scale phenomena, and today's robot swarms. One particular task being explored is the feasibility of managing carbon sequestration with minimal human intervention; if successfully scaled up this could have a huge positive impact ecologically, improving quality-of-life globally.Over the last couple of decades, two disparate perspectives have come to dominate thinking about multi-robot systems, each perspective or paradigm having its own philosophy, tools, models, and even publication venues. The idea being explored by this research is that the existing separation of the paradigms is vestigial, arising out of early AI questions about representation, and that for progress to be made it is essential that methods and tools accommodate systems that mix the characteristics of both paradigms. The work is improving scalability, performance, robustness, and model predictability for multi-robot systems by bridging and consolidating the paradigms along two thrusts. The first introduces and establishes a new position in the space between the paradigms with a new class of distributed algorithms that extend techniques for sublinear time approximation to communication settings with message-passing. The second thrust develops and applies theory that spans the established boundaries by applying renormalization group transformation methods to characterize multi-scale system behavior.