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

群体机器人研究了一组机器人如何朝着一个单一的目标工作。这样的目标通常是通过为每个机器人配备感官能力、基本计算能力和驱动能力来实现的。传感器检测到环境中的某些东西，这些信息被用来决定下一步的行动，并执行一些由此产生的激励。近年来，群体机器人技术取得了许多进展，但仍处于初级阶段。PI将采用“面向任务”的方法，从期望的宏观涌现集体行为开始，开发(在微观层面)机器人将运行的分布式和随机算法基础，以便收敛到期望的宏观行为；作为该过程的一部分，它们还将为尚未探索的集体和涌现系统提供理解。设想的机器人规模很小，从毫米到厘米不等，因此当部署在拥挤(即密集)环境中时，它们将表现为活性物质，更具体地说，是宏观可编程活性物质。对模拟感兴趣的紧急行为包括聚集(形成一个紧密连接的社区，该社区大多连接良好)、压缩(在保持连接社区的连贯性的同时最小化周长)、聚集(确定商定的定向方向)和运动(集体移动，同时保持凝聚力)。其中许多具有有趣的逆向问题，同样也是值得的，例如探索(保持连接的人口，但探索最大区域)和取消种族隔离(防止在粒子的二元混合物中分离)。PI在跨学科研究方面有着良好的记录，包括启动跨学科领域，例如，机器人物理(Goldman)，自组织粒子系统(RICHA)，以及统计物理和随机算法的融合(Randall)。私人投资机构也坚定地致力于支持少数民族、女性和本科生的研究(例如，通过亚利桑那州立大学的国家科学基金会S-STEM项目；佐治亚理工学院的高级和S.U.R.E.项目)。这个项目将汇集来自多个学科的技术，新的研究方法和发现将被纳入研究生课程。研究结果(包括开放源代码)将在各个学科中发表，并将在网络和Arxiv上提供。该项目的具体目标是致力于开发一个面向任务的活性物质的理论框架，该框架由简单物理系统的模型提供信息，可以实现和测试算法。生物物理学家为理解自然而建立的群体机器人系统可以进行修改，以执行这些新算法所需的任务。物理模型将允许在附加约束下对理论进行完善，例如重力和有限的能量。它还将允许PI测试其算法的健壮性，因为物理系统承认了一些错误。将从物理学的角度研究群体机器人的基本原理，将群体视为由微观层面上的可编程元素组成的活动物质。因此，将遵循(宏观)面向任务的方法，以设计一个分布式、随机的算法框架，以在微观层面构建和评估产生目标紧急宏观行为的算法。

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.