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

AitF：协作研究：活性物质的分布式随机算法框架

• 批准号: 1637393
• 负责人: Andrea Richa
• 金额: $ 10万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1637393&HistoricalAwards=false
• 关键词: AitF; Collaborative; Research; Distributed; Stochastic

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.

Swarm Robotics探索了机器人群体如何朝着单一目标工作。这样的目标通常通过为每个机器人配备传感能力、基本计算能力和驱动来实现。传感器检测到有关环境的某些信息，这些信息用于决定下一个动作，并执行一些由此产生的驱动。近年来，群体机器人技术取得了许多进展，但仍处于起步阶段。 PI将采取“面向任务”的方法，从期望的宏观紧急集体行为开始，开发机器人将运行的分布式和随机算法基础（在微观层面），以收敛到期望的宏观行为;作为过程的一部分，它们还将提供对尚未探索的集体和紧急系统的理解。设想的机器人规模很小，尺寸从毫米到厘米不等，因此当部署在拥挤的环境中时（即，密度）环境中，它们将表现为活性物质，更具体地说，作为宏观可编程活性物质。 模拟中感兴趣的涌现行为包括集群（形成一个紧密连接的社区），压缩（保持连接社区的一致性，同时最小化周边），群集（确定商定的方向）和运动（集体移动，同时保持凝聚力）。其中许多都有有趣的匡威问题，这些问题也同样值得，例如探索（保持连通的种群，但探索最大区域）和去隔离（防止粒子的二元混合物中的分离）。PI在跨学科研究方面有很好的记录，包括发起跨学科领域，例如，机器人物理学（Goldman）、自组织粒子系统（Richa）以及统计物理和随机算法的融合（Randall）。PI还对支持少数民族，妇女和本科生研究（例如，通过ASU的NSF S-STEM项目; ADVANCE和S.U.R.E.格鲁吉亚理工学院的课程）。 该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（包括开源代码）将在各个学科中发表，并将在Web和ArXiv上提供。该项目的具体目标是致力于开发面向任务的活性物质的理论框架，通过简单物理系统的模型提供信息，可以实现和测试算法。生物制药学家为了解自然而建造的群体机器人系统可以被修改，以执行这些新算法所需的任务。物理模型将允许在额外的约束条件下对理论进行改进，例如重力和有限的能量。它还将允许PI测试他们的算法的鲁棒性，因为物理系统承认一些错误。 群体机器人的基本原理将从物理学的角度进行研究，通过在微观层面上将集合视为由可编程元件组成的活性物质。因此，一个（宏观）面向任务的方法将遵循，以设计一个分布式的，随机的算法框架，以构建和评估算法在微观层面上，产生有针对性的紧急宏观行为。

项目成果

A Stochastic Approach to Shortcut Bridging in Programmable Matter

可编程物质中捷径桥接的随机方法

• DOI: 10.1007/978-3-319-66799-7_9
• 发表时间: 2017
• 期刊: DNA23
• 作者: Andres Arroyo, Marta;Cannon, Sarah;Daymude, Joshua J;Randall, Dana;Richa, Andrea W
• 通讯作者: Richa, Andrea W

Brief Announcement: A Local Stochastic Algorithm for Separation in Heterogeneous Self-Organizing Particle Systems

简短公告：一种用于异质自组织粒子系统分离的局部随机算法

• DOI: 10.1145/3212734.3212792
• 发表时间: 2018
• 期刊: PODC
• 作者: Cannon, Sarah;Daymude, Joshua J;Gokmen, Cem;Randall, Dana;Richa, Andrea W
• 通讯作者: Richa, Andrea W

A Markov Chain Algorithm for Compression in Self-Organizing Particle Systems

自组织粒子系统压缩的马尔可夫链算法

• DOI: 10.1145/2933057.2933107
• 发表时间: 2016
• 期刊: PODC
• 作者: Cannon, Sarah;Daymude, Joshua J.;Randall, Dana;Richa, Andréa W.
• 通讯作者: Richa, Andréa W.

Bio-Inspired Energy Distribution for Programmable Matter

• DOI: 10.1145/3427796.3427835
• 发表时间: 2020-07
• 期刊: Proceedings of the 22nd International Conference on Distributed Computing and Networking
• 作者: Joshua J. Daymude;A. Richa;Jamison Weber

Collaborative Computation in Self-Organizing Particle Systems

• DOI: 10.1007/978-3-319-92435-9_14
• 发表时间: 2017-10
• 作者: Alexandra M. Porter;A. Richa