CAREER: Bridging Self-Organized and Algorithmic Approaches to Multi-Robot Systems

职业：将自组织和算法方法与多机器人系统联系起来

• 批准号: 1453652
• 负责人: Dylan Shell
• 金额: $ 55万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-03-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1453652&HistoricalAwards=false
• 关键词: CAREER; Bridging; Self; Organized; Algorithmic

This project consists of research to address the limitations of traditional ways of programming groups of robots in order to make it easier to program them to solve problems in teams. The work is helping realize a future where robots address important applications such as those with life-saving, ecological, and national strategic elements (e.g., manufacturing, roboticized agriculture, planetary exploration). To do this, the research is establishing new connections between methods developed for thinking about very large data sets, mathematical models invented by physicists for small-scale phenomena, and today's robot swarms. One particular task being explored is the feasibility of managing carbon sequestration with minimal human intervention; if successfully scaled up this could have a huge positive impact ecologically, improving quality-of-life globally.Over the last couple of decades, two disparate perspectives have come to dominate thinking about multi-robot systems, each perspective or paradigm having its own philosophy, tools, models, and even publication venues. The idea being explored by this research is that the existing separation of the paradigms is vestigial, arising out of early AI questions about representation, and that for progress to be made it is essential that methods and tools accommodate systems that mix the characteristics of both paradigms. The work is improving scalability, performance, robustness, and model predictability for multi-robot systems by bridging and consolidating the paradigms along two thrusts. The first introduces and establishes a new position in the space between the paradigms with a new class of distributed algorithms that extend techniques for sublinear time approximation to communication settings with message-passing. The second thrust develops and applies theory that spans the established boundaries by applying renormalization group transformation methods to characterize multi-scale system behavior.

该项目的研究旨在解决传统机器人组编程方法的局限性，以便更轻松地对它们进行编程以解决团队中的问题。 这项工作正在帮助实现机器人解决重要应用的未来，例如那些具有拯救生命、生态和国家战略要素的应用（例如制造、机器人农业、行星探索）。为此，该研究正在为思考非常大的数据集而开发的方法、物理学家为小规模现象发明的数学模型以及当今的机器人群之间建立新的联系。 正在探索的一项具体任务是以最少的人为干预管理碳封存的可行性；如果成功扩大规模，这可能会对生态产生巨大的积极影响，改善全球的生活质量。在过去的几十年里，两种不同的观点已经主导了对多机器人系统的思考，每种观点或范式都有自己的哲学、工具、模型，甚至出版场所。 这项研究探讨的想法是，现有的范式分离是残留的，是由早期人工智能有关表示的问题引起的，为了取得进展，方法和工具必须适应混合两种范式特征的系统。 。 这项工作是通过沿着两个主旨桥接和巩固范例来提高多机器人系统的可扩展性、性能、鲁棒性和模型可预测性。第一个通过一类新型分布式算法在范式之间的空间中引入并建立了一个新的位置，该算法将次线性时间近似技术扩展到具有消息传递的通信设置。 第二个推动力通过应用重正化群变换方法来描述多尺度系统行为，从而发展和应用跨越既定边界的理论。

项目成果

Efficient recursive distributed state estimation of hidden Markov models over unreliable networks

• DOI: 10.1007/s10514-019-09854-3
• 发表时间: 2019-05
• 期刊: Autonomous Robots
• 影响因子: 3.5
• 作者: A. Tamjidi;R. Oftadeh;S. Chakravorty;Dylan A. Shell

Abstractions for computing all robotic sensors that suffice to solve a planning problem

用于计算足以解决规划问题的所有机器人传感器的抽象

• DOI: 10.1109/icra40945.2020.9196812
• 发表时间: 2020
• 期刊: IEEE International Conference on Robotics and Automation (ICRA
• 作者: Zhang, Yulin;Shell, Dylan A.
• 通讯作者: Shell, Dylan A.

Every Action-Based Sensor

每个基于动作的传感器

• DOI: 10.1007/978-3-030-66723-8_11
• 发表时间: 2020
• 期刊: Workshop on the Algorithmic Foundations of Robotics
• 作者: McFassel, Grace;Shell, Dylan A.
• 通讯作者: Shell, Dylan A.

Reality as a simulation of reality: robot illusions, fundamental limits, and a physical demonstration

现实是现实的模拟：机器人的幻想、基本限制和物理演示

• DOI: 10.1109/icra40945.2020.9196761
• 发表时间: 2020
• 期刊: IEEE International Conference on Robotics and Automation (ICRA
• 作者: Shell, Dylan A.;O'Kane, Jason M.
• 通讯作者: O'Kane, Jason M.

Robots in the Huddle: Upfront Computation to Reduce Global Communication at Run Time in Multirobot Task Allocation

拥挤的机器人：预先计算以减少多机器人任务分配中运行时的全局通信

• DOI: 10.1109/tro.2019.2937468
• 发表时间: 2020
• 期刊: IEEE Transactions on Robotics
• 影响因子: 7.8
• 作者: Nam, Changjoo;Shell, Dylan A.
• 通讯作者: Shell, Dylan A.