NSF Org:	CNS Division Of Computer and Network Systems
Recipient:	UNIVERSITY OF NOTRE DAME DU LAC
Initial Amendment Date:	September 17, 2018
Latest Amendment Date:	September 17, 2018
Award Number:	1830335
Award Instrument:	Standard Grant
Program Manager:	Ralph Wachter rwachter@nsf.gov  (703)292-8950 CNS  Division Of Computer and Network Systems CSE  Directorate for Computer and Information Science and Engineering
Start Date:	October 1, 2018
End Date:	September 30, 2023 (Estimated)
Total Intended Award Amount:	$509,527.00
Total Awarded Amount to Date:	$509,527.00
Funds Obligated to Date:	FY 2018 = $509,527.00
History of Investigator:	Hai Lin (Principal Investigator) hlin1@nd.edu
Recipient Sponsored Research Office:	University of Notre Dame 940 GRACE HALL NOTRE DAME IN  US  46556-5708 (574)631-7432
Sponsor Congressional District:	02
Primary Place of Performance:	University of Notre Dame 940 Grace Hall Notre Dame IN  US  46556-5708
Primary Place of Performance Congressional District:	02
Unique Entity Identifier (UEI):	FPU6XGFXMBE9
Parent UEI:	FPU6XGFXMBE9
NSF Program(s):	NRI-National Robotics Initiati
Primary Program Source:	01001819DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):	8086
Program Element Code(s):	801300
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.070

ABSTRACT

Many emergencies require people to evacuate a building quickly. During an emergency, evacuees must make quick decisions, so they tend to rely on default decision making that may put them at risk, such as exiting the way they entered, following a crowd, or sheltering in place. When a crowd attempts to exit through a single exit, choke points and crowd congestion may impede the safe flow of evacuees, potentially resulting in a stampede of people and the loss of human lives. Mobile robots are increasingly being deployed as assistants on city streets and in hotels, shopping centers and hospitals. The future ubiquity of these systems offers an opportunity to change how people are evacuated from dangerous situations. In particular, when compared with traditional emergency infrastructure, such as fire alarms and smoke detectors, mobile robots can achieve better situation awareness and use this information to expedite evacuation and enhance safety. Additionally, mobile robots can be used in risky and life-threatening situations, such as chemical spills or active shooter scenarios, which present dangers to human first responders.

This project aims to derive a scalable design framework and develop an embodied multi-robot evacuation system where multiple mobile robots, originally tasked for different purposes, serve as emergency evacuation first responders leading people to safety. In particular, multiple mobile robots efficiently coordinate with each other and actively interact with evacuees to maximize their egress. The project significantly contributes to the understanding of how people respond to a robots' directions and authoritative commands. Furthermore, the project implements these findings and demonstrates their effectiveness using real-world experiments with human subjects. Beyond emergency evacuation, the research findings can be extended to many other related areas, especially those involving cooperative robot teams that are embodied in an uncertain and dynamic physical world with the need to actively interact with humans; e.g., battlefield, law enforcement, urban transportation systems, manufacturing systems, rehabilitation and health management.

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.

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

During an emergency, individuals may need to evacuate a building quickly. However, they often rely on instinctive decision-making, which can put them in danger, such as following the crowd or exiting through the same entrance they came in. This can create congestion and choke points, making it difficult for people to evacuate safely and potentially leading to stampedes and loss of life. 
To address this issue, a robot-assisted evacuation system has been developed. The system uses multiple mobile robots, originally designated for diverse purposes such as cleaning or ushering, as the first responders in an emergency evacuation. These robots will lead people to safety by efficiently coordinating with each other and actively interacting with evacuees to maximize their escape. 
Compared to traditional emergency infrastructure, these robots provide better situation awareness and can aid in expediting evacuation and enhancing safety. Additionally, they can be employed in hazardous situations, such as chemical spills or active shooter scenarios.

The main outcome from this project is a set of systematic methods of designing coordinated robot decision-making and motion planning in crowded environments to achieve an efficient evacuation. Furthermore, the human-robot interaction issues associated with evacuation are investigated through real human-robotic experimental studies, and the effectiveness of our theoretical and experimental results are evaluated by creating a coordinated multi-robot evacuation system and conducting field tests.  Additionally, the project contributes to our understanding of how people respond to a robot's directions and authoritative commands, which will be informed by human-robot interaction studies. The insights gained from this project can be extended to other domains such as cooperative robot teams that need to interact actively with humans in uncertain and dynamic physical environments, such as battlefield, law enforcement, urban transportation systems, manufacturing systems, rehabilitation, and health management.

 

 

Last Modified: 01/29/2024
Modified by: Hai Lin

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