AitF: Collaborative Research: A Distributed and Stochastic Algorithmic Framework for Active Matter

点击翻译按钮获取中文摘要

Swarm robotics explores how groups of robots can work towards a singular goal. Such a goal is typically achieved by equipping each robot with sensory capabilities, basic computing power, and actuation. The sensors detect something about the environment, this information is used to make a decision about the next action, and some resulting actuation is performed. Swarm robotics has made many advances in recent years, but it is still in its infancy. The PIs will take a "task-oriented" approach and start from a desired macroscopic emergent collective behavior to develop the distributed and stochastic algorithmic underpinnings that the robots will run (at the microscopic level) in order to converge to the desired macroscopic behavior; as part of the process, they will also provide the understanding for yet unexplored collective and emergent systems. The robots envisioned are small in scale, ranging in size from millimeters to centimeters, so that when deployed in crowded (i.e., dense) environments, they will behave as active matter, more specifically as macroscopic programmable active matter. The emergent behaviors of interest for simulations include clustering (forming a tight-knit community that is mostly well-connected), compression (maintaining coherence of a connected community while minimizing perimeter), flocking (determining an agreed upon direction of orientation), 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 desegregation (preventing separation in a binary mixture of particles).The PIs have strong records for interdisciplinary research, including initiating interdisciplinary areas, e.g., robo-physics (Goldman), self-organizing particle systems (Richa), and the fusion of statistical physics and randomized algorithms (Randall). The PIs also have a strong commitment toward supporting minorities, women, and undergraduate research (e.g., through NSF S-STEM programs at ASU; ADVANCE and S.U.R.E. programs at Georgia Tech). This project will bring together techniques from multiple disciplines, and new research approaches and findings will be incorporated into graduate courses. Findings (including open source code) will be published in the various disciplines, and will be made available on the web and ArXiv.The specific goals of this project are to work toward developing a theoretical framework for task-oriented active matter, informed by models of simple physical systems, that can realize and test the algorithms. The swarm robotics systems that biophysicists build to understand nature can be modified to perform the tasks these new algorithms require. The physical models will allow refinements to the theories under additional constraints, such as gravity and limited energy. It also will allow the PIs to test their algorithms for robustness, as physical systems admit some error. The fundamentals of swarm robotics will be studied from a physics standpoint, by viewing the ensemble as active matter composed of programmable elements at the micro-level. Thus, a (macro-)task oriented approach will be followed in order to design a distributed, stochastic algorithmic framework to construct and evaluate algorithms at the micro-level that yield the targeted emergent macro-behavior.