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NRI: INT: Balancing Collaboration and Autonomy for Multi-Robot Multi-Human Search and Rescue

NRI: INT: Balancing Collaboration and Autonomy for Multi-Robot Multi-Human Search and Rescue
NRI:INT:平衡多机器人多人搜索和救援的协作与自主
批准号:
1830414
负责人:
Ryan Williams
金额:
$147.47万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2018
资助国家:
美国
项目状态:
已结题
起止时间:
2018-10-01 至 2023-09-30

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中文摘要
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英文摘要
Each year, thousands of people go missing in the United States. Coordinated search and rescue (SAR) operations provide the best chance to locate a missing individual alive. However, challenging terrain and other search constraints represent a barrier to human searchers. This project therefore seeks to enable collaboration between human searchers and unmanned aerial vehicles (UAVs) to improve searches while reducing human effort. Specifically, this project asks: How do we select and assign search tasks that ensure long-term human-robot collaboration, while deploying robots to complement human searchers in real-time? This project aims to answer this question through optimization, behavioral modeling, human-robot interaction, and computation, with evaluation in large-scale prototypes supported by the SAR community. Finally, impacts beyond SAR include: (1) education focused on co-robots; (2) theory for human-robot interaction in collaborative settings; (3) portable, low-cost, low-power computational infrastructure suitable for a wide range of applications; (4) technologies that foster economic opportunity around co-robots; and (5) field experiences for students from groups underrepresented in engineering.This project focuses on new theory and technologies for: (1) a minimally invasive, adaptive, multi-UAV control system leveraging risk-aware multi-robot planning for human-in-the-loop (HITL) control; (2) distributed computing that opportunistically exploits and balances multi-robot interaction, communication, computation, and decision-making; and (3) an interface between human searchers and UAVs that allows control from complete autonomy to manual operation, including testable interfaces for exploration vs. exploitation strategies. This project addresses fundamental challenges critical to the scalability of multi-robot multi-human teams: (1) planning and control systems for UAVs that can autonomously gather information in a cooperative and distributed way while adapting to uncertain human plans; (2) interfaces for human-robot interactions that allows collaboration that appropriately balances exploration with exploitation; and (3) real-time support to analyze, store, and share data subject to the power and connectivity constraints typical of real-world deployments.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(7)
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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.
Collaborative Multi-Robot Multi-Human Teams in Search and Rescue
搜索救援中的协作多机器人多人团队
DOI: --
发表时间: 2020
期刊: International Conference on Information Systems for Crisis Response and Management
影响因子: --
作者: [Ryan K. Williams, N. Abaid, J. McClure, Nathan Lau, Larkin Heintzman, Amanda Hashimoto, Tianzi Wang, Chinmaya Patnayak, Akshay Kumar]
通讯作者: Akshay Kumar
Level of detail in visualization for human autonomy teaming: Speed, accuracy, and workload effects
人类自主团队可视化的详细程度:速度、准确性和工作负载影响
DOI: 10.1177/21695067231193673
发表时间: 2023
期刊: Proceedings of the Human Factors and Ergonomics Society Annual Meeting
影响因子: --
作者: [Wang, Tianzi, Lau, Nathan]
通讯作者: Lau, Nathan
WASP: A Wearable Super-Computing Platform for Distributed Intelligence in Multi-Agent Systems
WASP:用于多代理系统中分布式智能的可穿戴超级计算平台
DOI: 10.1109/hpec49654.2021.9622784
发表时间: 2021
期刊: 2021 IEEE High Performance Extreme Computing Conference (HPEC
影响因子: --
作者: [Patnayak, Chinmaya, McClure, James E., Williams, Ryan K.]
通讯作者: Williams, Ryan K.
CAREER: Robots that Plan Interactions, Come and Go, and Build Trust
AF: Small: Lower Bounds in Complexity Theory Via Algorithms
CPS: Medium: Computation-Aware Autonomy for Timely and Resilient Multi-Agent Systems
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