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

基本信息

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

项目摘要

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.

群体机器人技术探索机器人群体如何实现单一目标。这一目标通常是通过为每个机器人配备感知能力、基本计算能力和驱动来实现的。传感器检测有关环境的信息，该信息用于做出下一步行动的决定，并执行一些最终的驱动。近年来，群体机器人技术取得了许多进步，但仍处于起步阶段。 PI将采取“以任务为导向”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；作为该过程的一部分，它们还将提供对尚未探索的集体和紧急系统的理解。设想的机器人规模较小，尺寸从毫米到厘米不等，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，表现为宏观可编程活性物质。模拟感兴趣的新兴行为包括聚类（形成一个紧密联系的社区，大部分连接良好）、压缩（保持连接社区的连贯性，同时最小化周长）、聚集（确定商定的方向）和运动（集体行动，同时保持凝聚力）。其中许多都有有趣的相反问题，这些问题也同样值得，例如探索（维持连接的群体，但探索最大面积）和去隔离（防止粒子二元混合物中的分离）。 PI 在跨学科研究方面拥有良好的记录，包括开创跨学科领域，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 还坚定致力于支持少数族裔、女性和本科生研究（例如，通过亚利桑那州立大学的 NSF S-STEM 项目；佐治亚理工学院的 ADVANCE 和 S.U.R.E 项目）。该项目将汇集多个学科的技术，新的研究方法和发现将纳入研究生课程。研究结果（包括开源代码）将在各个学科中发布，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以模型为基础的面向任务的活性物质的理论框架简单的物理系统，可以实现和测试算法。生物物理学家为了解自然而构建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。物理模型将允许在重力和有限能量等额外约束下完善理论。它还允许 PI 测试其算法的稳健性，因为物理系统承认一些错误。将从物理学的角度研究群体机器人的基础知识，将群体视为由微观层面的可编程元素组成的活性物质。因此，将遵循面向（宏观）任务的方法来设计分布式随机算法框架，以在微观层面构建和评估算法，从而产生目标的紧急宏观行为。

项目成果

期刊论文数量（12）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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
影响因子：
0
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
影响因子：
0
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
影响因子：
0
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
影响因子：
0
作者：
Joshua J. Daymude;A. Richa;Jamison Weber
通讯作者：
Joshua J. Daymude;A. Richa;Jamison Weber

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

异质自组织粒子系统分离的局部随机算法