NRI: Collaborative Research: Dynamic Robot Guides for Emergency Evacuations

NRI：协作研究：紧急疏散动态机器人指南

基本信息

批准号：
1527016
负责人：
Yi Guo
金额：
$ 31.53万
依托单位：
Stevens Institute of Technology
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：
2015-09-01 至 2019-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1527016&HistoricalAwards=false
关键词：
NRI Collaborative Research Dynamic Robot

项目摘要

Crowd stampede is one of the most harmful collective human behaviors. In incidents throughout history, panic due, for example, to the outbreak of fire or the unexpected discharge of firearms has been a greater hazard than the original triggering events. This project supports fundamental research on the influence of human-robot interaction on crowd dynamics, towards the design of dynamic robot control algorithms to assist humans and prevent panic in emergency situations. The ultimate goal of this research will be reconfigurable robot guides that can respond to a variety of needs. These include different types of emergency evacuation, as well as non-emergency situations involving mass movement of crowds, such as at parades, concerts, or other large public events. The project integrates research with educational activities through robot-centric education and short course development. To engage the younger generation with science and technology, the project will partner with a university educational center and a community college for various outreach activities.The objective of the project is to investigate human-robot interaction in crowd dynamics, develop optimal feedback control to regulate human flow distribution, and design robot-assisted emergency evacuation algorithms. The research will advance the state-of-the-art in human-robot interaction, and fill a gap in robotics research by experimentally validating and measuring the interaction forces governing human-robot interaction in crowd dynamics. The proposed robot motion primitive design leads to new approaches for learning-based robot motion planning to efficiently engage humans. The project validates the use of dynamic robot guides in real human-robot interaction experiments in indoor environments. Simulation validation in benchmark environments such as shopping-malls and campus buildings will also be performed, and the efficiency of alternative robot-assisted evacuation strategies will be evaluated. While primarily for intelligent robots, the research results are anticipated to be cross-cutting and applicable to other areas such as transportation, communication, and control.

人群踩踏是人类最有害的集体行为之一。在整个历史上发生的事件中，恐慌是由于火灾爆发或枪支意外释放的危险比原始的触发事件更大。该项目支持对人类机器人互动对人群动态影响的影响，对动态机器人控制算法设计的影响，以帮助人类并防止在紧急情况下恐慌。这项研究的最终目标将是可以响应各种需求的可重新配置机器人指南。这些包括不同类型的紧急撤离以及涉及人群大规模流动的非紧急情况，例如在游行，音乐会或其他大型公共活动中。该项目通过以机器人为中心的教育和短期课程开发将研究与教育活动融为一体。为了使年轻一代与科学技术联系起来，该项目将与大学教育中心和社区学院合作，以进行各种外展活动。该项目的目的是调查人群动态中的人类机器人互动，开发最佳反馈控制以调节人类流动分配，并设计机器人辅助的紧急紧急撤离算法。这项研究将通过实验验证和测量控制人群动态中人类机器人相互作用的相互作用的实验验证和衡量相互作用，填补机器人研究的差距。提出的机器人运动原始设计为基于学习的机器人运动计划提供了新的方法，从而有效地吸引了人类。该项目验证了在室内环境中实际人类机器人相互作用实验中使用动态机器人指南的使用。还将进行基准测试环境中的仿真验证，例如购物者和校园建筑物，并将评估替代机器人辅助撤离策略的效率。虽然主要用于智能机器人，但预计研究结果将是横切，并且适用于其他领域，例如运输，通信和控制。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Pedestrian Flow Optimization to Reduce the Risk of Crowd Disasters Through Human–Robot Interaction

DOI：
10.1109/tetci.2019.2930249
发表时间：
2020-06
期刊：
IEEE Transactions on Emerging Topics in Computational Intelligence
影响因子：
5.3
作者：
Chao Jiang;Z. Ni;Yi Guo;Haibo He
通讯作者：
Chao Jiang;Z. Ni;Yi Guo;Haibo He

Robot-Assisted Pedestrian Regulation Based on Deep Reinforcement Learning

DOI：
10.1109/tcyb.2018.2878977
发表时间：
2020-04
期刊：
IEEE Transactions on Cybernetics
影响因子：
11.8
作者：
Zhiqiang Wan;Chao Jiang;M. Fahad;Z. Ni;Yi Guo;Haibo He
通讯作者：
Zhiqiang Wan;Chao Jiang;M. Fahad;Z. Ni;Yi Guo;Haibo He

Dynamic Plume Tracking by Cooperative Robots

协作机器人动态羽流跟踪

DOI：
10.1109/tmech.2019.2892292
发表时间：
2019
期刊：
IEEE/ASME Transactions on Mechatronics
影响因子：
0
作者：
Wang, Jun-Wei;Guo, Yi;Fahad, Muhammad;Bingham, Brian
通讯作者：
Bingham, Brian

Learning How Pedestrians Navigate: A Deep Inverse Reinforcement Learning Approach

DOI：
10.1109/iros.2018.8593438
发表时间：
2018-01-01
期刊：
2018 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
影响因子：
0
作者：
Fahad, Muhammad;Chen, Zhuo;Guo, Yi
通讯作者：
Guo, Yi

Learning Decentralized Control Policies for Multi-Robot Formation

DOI：
10.1109/aim.2019.8868898
发表时间：
2019-01-01
期刊：
2019 IEEE/ASME INTERNATIONAL CONFERENCE ON ADVANCED INTELLIGENT MECHATRONICS (AIM)
影响因子：
0
作者：
Jiang, Chao;Chen, Zhuo;Guo, Yi
通讯作者：
Guo, Yi