CAREER: Robots that Plan Interactions, Come and Go, and Build Trust
CAREER: Robots that Plan Interactions, Come and Go, and Build Trust
批准号:
2046770
负责人:
Ryan Williams
金额:
$56.99万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2021
资助国家:
美国
项目状态:
未结题
起止时间:
2021-04-01 至 2026-03-31
中文摘要
点击翻译按钮获取中文摘要
英文摘要
After decades of fundamental advances, we are beginning to see meaningful influence of autonomous robots on crucial social and economic problems. While tremendously exciting, these advances are primarily seen in single robot systems, a natural progression given the highly complex nature of multi-robot systems. In order to scale to real-world problems, this Faculty Early Career Development (CAREER) project advances multi-robot system theory to allow robots to plan their interactions intelligently, gracefully enter and exit systems, and participate in trustful decision-making processes with other robots and human teammates. In addition, the theoretical work in this project applies to multi-robot multi-human search and rescue. Indeed, when robots and humans search for a lost person in large wilderness, the theoretical advancements listed above will prove invaluable. Finally, this project includes a comprehensive education and outreach plan consisting of: curriculum focused on autonomy that crosses departmental boundaries; pedagogical programs with an emphasis on persons with a disability; K-12 academic experiences for underrepresented students in engineering; and channels for national and international education and outreach.This project focuses on developing new theory and technologies, including: a framework based on independence systems for modeling robot interaction structures over time with sampling and gradient-based methods for efficiently computing interaction plans; a combinatorial optimization framework for modeling optimally open multi-robot systems yielding plans for robots entering and exiting systems while guaranteeing the correctness of underlying collaborative objectives; a framework for trust-building in collaborative multi-robot multi-human decision-making based on multi-armed bandits, a concept we call the trustful multi-armed bandit; a set of search and rescue case studies for evaluating our research thrusts; and a portable, indoor/outdoor, multi-scale testbed for experimental validation of heterogeneous multi-robot teams. This project addresses fundamental challenges in four areas critical to the flexibility, scalability, and resilience of autonomous coordination: (1) systems that plan their interactions in a manner that adapts to high-level mission objectives and the deployment environment, while respecting low-level collaboration requirements; (2) systems whose composition changes over time while remaining resilient to such changes; (3) systems that select actions that actively build trust from other systems over time; and (4) systems that are prototyped and tested under realistic conditions across varying scales of deployment.This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
期刊论文(1)
专著(0)
科研奖励(0)
会议论文
Multi-Agent Intermittent Interaction Planning via Sequential Greedy Selections Over Position Samples
通过位置样本的顺序贪婪选择进行多智能体间歇性交互规划
DOI:
10.1109/lra.2020.3047788
发表时间:
2021
期刊:
IEEE Robotics and Automation Letters
影响因子:
5.2
作者:
[Heintzman, Larkin, Williams, Ryan K.]
通讯作者:
Williams, Ryan K.
Examining relationships among teacher professional learning and associated teacher and student outcomes in math and science: A meta-analytic approach to mediation and moderation
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批准号:2300544
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项目类别:Continuing Grant
-
资助金额:$130.97万
-
财政年份:2023
-
负责人:Ryan Williams
-
依托单位:
AF: Small: Lower Bounds in Complexity Theory Via Algorithms
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批准号:2127597
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项目类别:Standard Grant
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资助金额:$50.0万
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财政年份:2021
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负责人:Ryan Williams
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依托单位:
CPS: Medium: Computation-Aware Autonomy for Timely and Resilient Multi-Agent Systems
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批准号:1932074
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项目类别:Standard Grant
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资助金额:$119.77万
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财政年份:2019
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负责人:Ryan Williams
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依托单位:
NRI: INT: Balancing Collaboration and Autonomy for Multi-Robot Multi-Human Search and Rescue
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批准号:1830414
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项目类别:Standard Grant
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资助金额:$147.47万
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财政年份:2018
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负责人:Ryan Williams
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依托单位:
CAREER: Common Links in Algorithms and Complexity
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批准号:1741615
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项目类别:Continuing Grant
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资助金额:$50.33万
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财政年份:2017
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负责人:Ryan Williams
-
依托单位:
CRII: RI: Distributed, Stable and Robust Topology Control: New Methods for Asymmetrically Interacting Multi-Robot Teams
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批准号:1657235
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项目类别:Standard Grant
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资助金额:$17.43万
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财政年份:2017
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负责人:Ryan Williams
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依托单位:
AF:Small:Limitations on Algebraic Methods via Boolean Complexity Theory
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批准号:1741638
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项目类别:Standard Grant
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资助金额:$7.06万
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财政年份:2017
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负责人:Ryan Williams
-
依托单位:
NRI: Coordinated Detection and Tracking of Hazardous Agents with Aerial and Aquatic Robots to Inform Emergency Responders
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批准号:1637915
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项目类别:Standard Grant
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资助金额:$90.08万
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财政年份:2016
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负责人:Ryan Williams
-
依托单位:
AF:Small:Limitations on Algebraic Methods via Boolean Complexity Theory
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批准号:1617580
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项目类别:Standard Grant
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资助金额:$10.99万
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财政年份:2016
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负责人:Ryan Williams
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依托单位:
CAREER: Common Links in Algorithms and Complexity
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批准号:1552651
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项目类别:Continuing Grant
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资助金额:$55.0万
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财政年份:2015
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负责人:Ryan Williams
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依托单位:
AF: Large: Collaborative Research: Exploiting Duality between Algorithms and Complexity
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批准号:1212372
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项目类别:Continuing Grant
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资助金额:$45.0万
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财政年份:2012
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负责人:Ryan Williams
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依托单位:
海外基金