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Understanding Pedestrian Dynamics for Seamless Human-Robot Interaction

了解行人动力学以实现无缝人机交互

基本信息

批准号：
1825709
负责人：
Yi Guo
金额：
$ 35.85万
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：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1825709&HistoricalAwards=false
关键词：
Understanding Pedestrian Dynamics Seamless Human

项目摘要

In the near future, robots will navigate alongside people in busy, crowded and unconstrained environments such as shopping malls, airports and elder-care facilities. Current mobile robot motion planners are inadequate because current models of pedestrian dynamics do not fully capture the complexity of human motion behavior in crowds. As a result, robots can "freeze" in places where crowd density is high, which further impedes pedestrian traffic flow. This project will use novel machine learning techniques to develop a robot motion planner with human-like navigations features. The project team will use objective and subjective performance measures to evaluate the predictability and acceptability of human-robot interactions with robots and their novel control algorithms deployed in shopping malls and campus buildings. This project serves the national interest because the resulting pedestrian dynamics modeling methods and robot controllers may result in public safety applications such as emergency evacuations and crowd planning/management. The project will involve an educational component that provides engineering and research methods training to graduate and undergraduate students, as well as STEM outreach to high-school and middle-school students. Additional efforts will be made to attract and retain women into careers in science and engineering. This research investigates new control methodologies that promise improved pedestrian/mobile-robot navigation through crowded and unconstrained environments. Methods include the extraction of features related to pedestrian behavior from existing datasets and their use in training a deep neural network (DNN) to model pedestrian dynamics; the use of inverse reinforcement learning to generate a cost map that humans follow to navigate; the implementation of the map within a robot motion controller; and the experimental testing and validation of the controller in real-world human environments using objective and subjective performance criteria. The experimental and evaluation data will be made available for use by the research community.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.

在不久的将来，机器人将在购物中心、机场和老年人护理设施等忙碌、不受约束的环境中与人一起导航。当前的移动的机器人运动规划器是不够的，因为当前的行人动力学模型没有完全捕捉人群中人类运动行为的复杂性。因此，机器人可能会在人群密度高的地方“冻结”，这进一步阻碍了行人交通流量。该项目将使用新的机器学习技术来开发具有类似人类导航功能的机器人运动规划器。项目团队将使用客观和主观的性能指标来评估人机交互的可预测性和可接受性，以及在购物中心和校园建筑中部署的机器人及其新颖的控制算法。该项目服务于国家利益，因为由此产生的行人动力学建模方法和机器人控制器可能会导致公共安全应用，如紧急疏散和人群规划/管理。该项目将包括一个教育部分，为研究生和本科生提供工程和研究方法培训，并向高中和初中学生提供STEM推广。将作出更多努力，吸引和留住妇女从事科学和工程职业。这项研究探讨了新的控制方法，承诺改善行人/移动机器人导航通过拥挤和不受约束的环境。方法包括从现有数据集中提取与行人行为相关的特征，并将其用于训练深度神经网络（DNN）以模拟行人动态;使用反向强化学习来生成人类遵循导航的成本地图;在机器人运动控制器中实现地图;以及使用客观和主观性能标准在真实世界人类环境中对控制器进行实验测试和验证。该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估的支持。

项目成果

期刊论文数量（7）

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

Optimization of Merging Pedestrian Flows Based on Adaptive Dynamic Programming

DOI：
10.23919/acc.2019.8814597
发表时间：
2019-07
期刊：
2019 American Control Conference (ACC)
影响因子：
0
作者：
Chao Jiang;Yi Guo;Z. Ni;Haibo He
通讯作者：
Chao Jiang;Yi Guo;Z. Ni;Haibo He

Multi-Robot Guided Policy Search for Learning Decentralized Swarm Control

DOI：
10.1109/lcsys.2020.3005441
发表时间：
2021-07-01
期刊：
IEEE CONTROL SYSTEMS LETTERS
影响因子：
3
作者：
Jiang, Chao;Guo, Yi
通讯作者：
Guo, Yi

Learning Human Navigation Behavior Using Measured Human Trajectories in Crowded Spaces

DOI：
10.1109/iros45743.2020.9341038
发表时间：
2020-10
期刊：
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
影响因子：
0
作者：
M. Fahad;Guang Yang;Yi Guo
通讯作者：
M. Fahad;Guang Yang;Yi Guo

Multi-robot formation control: a comparison between model-based and learning-based methods