AiTF: Collaborative Research: Distributed and Stochastic Algorithms for Active Matter: Theory and Practice

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Swarm robotics explores how groups of robots can work towards a singular goal, which is typically achieved by equipping each robot with sensory capabilities, basic computing power, and movement. The sensors detect and use information about the environment to decide on the next action. Swarm robotics has made many advances in recent years, but is still in its infancy. This project proposes to explore swarm robotics systems in a non-standard way as physical systems. The PIs take a "task-oriented" approach to develop the distributed algorithmic rules that the robots will run (at the microscopic level) in order to converge to the desired collective behavior (at the macroscopic level). This will provide understanding of the minimal requirements for individuals to accomplish the desired behavior, for both algorithmic and physical realizations, and will provide a more principled approach for studying swarm robotics. The robots envisioned are small in scale, ranging in size from millimeters to centimeters, so that when deployed in dense environments, they will behave as programmable active matter.The PIs have strong records for interdisciplinary research, including initiating interdisciplinary areas (e.g., robo-physics, self-organizing particle systems, and the fusion of statistical physics and randomized algorithms). They have a strong commitment toward supporting minorities, women, and undergrad research (e.g., through NSF REUs, including through this project, NSF S-STEM programs at ASU; ADVANCE and S.U.R.E. programs at Georgia Tech). Any breakthrough in this combination of swarm and active matter systems will require employing analyses and techniques from stochastic systems, condensed matter physics, swarm systems, robotics, and distributed algorithms to understand and achieve the desired group dynamics, and hence will bring together and educate researchers from different disciplines and specialties. New research approaches and findings will be incorporated into multiple graduate courses and workshops will provide tutorials for bridging multiple disciplines, making material accessible to young researchers and helping to widely disseminate results. Findings (including open source code) will be published in the various disciplines, and will be be made available on our web pages and ArXiv. The project explores the fundamentals of swarm robotics from a physics standpoint, by viewing the ensemble as active matter composed of programmable elements at the micro-level. The project will follow a (macro-)task oriented approach, and design a distributed stochastic algorithmic framework to design and evaluate algorithms at the micro-level that will yield the targeted emergent macroscopic behavior. The emergent behaviors it addresses include compression (maintaining coherence of a connected community while minimizing perimeter), bridging (connecting two or more locations in the most efficient manner), alignment (determining an agreed upon direction of orientation), jamming (obstruction of movement by increased collective flow), and locomotion (collectively moving while maintaining cohesiveness). Many of these have interesting converse problems which are also equally worthwhile, such as exploration (maintaining a connected population, but exploring maximal area) and non-alignment (representing a disordered ensemble). In some cases the collective behavior acts like a physical system changing between a liquid (disordered) and a solid (ordered) state, as determined by phase transitions in the systems. The project will explore stochastic and distributed algorithms for rigorously achieving these goals.