NRI: Collaborative Research: Dynamic Robot Guides for Emergency Evacuations

点击翻译按钮获取中文摘要

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.