[ICRA20] Congestion-aware Evacuation Routing using Augmented Reality Devices

Evacuation routing with a density map. (Top) The floorplan and the distribution of evacuees (blue dots). (Bottom) The population density map indicates the magnitude of congestion. If a human agent (green star) followed the route generated by a naive planner in terms of the shorted path (in red) to Exit B, s/he would compete with other agents. Instead, the proposed system suggests a further, but more time-efficient evacuation route (in green) toward Exit D.

Abstract

We present a congestion-aware routing solution for indoor evacuation, which produces real-time individual-customized evacuation routes among multiple destinations while keeping tracks of all evacuees’ locations. A population density map, obtained on-the-fly by aggregating locations of evacuees from user-end AR devices, is used to model the congestion distribution inside a building. To efficiently search the evacuation route among all destinations, a variant of A* algorithm is devised to obtain the optimal solution in a single pass. In a series of simulated studies, we show that the proposed algorithm is more computationally optimized compared to classic path planning algorithms; it generates a more time-efficient evacuation route for each individual that minimizes the overall congestion. A complete system using AR devices is implemented for a pilot study in real-world environments, demonstrating the efficacy of the proposed approach.

Publication
In Proceedings of the IEEE International Conference on Robotics and Automation
Zeyu Zhang
Zeyu Zhang
Ph.D. Candidate
Hangxin Liu
Hangxin Liu
Research Scientist
Ziyuan Jiao
Ziyuan Jiao
Ph.D. Candidate
Yixin Zhu
Yixin Zhu
Assistant Professor

I build humanlike AI.

Song-Chun Zhu
Song-Chun Zhu
Chair Professor

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