Evacuee Behavior Modeling during Robot-Guided Evacuations
Bibliographic Data
| ID | 21199737 |
|---|---|
| Authors | Mollik Nayyar (0000-0002-5102-5756, Pennsylvania State University, corresponding author), Alan R Wagner (0000-0002-7941-3814, Pennsylvania State University) |
| Year | 2025 |
| Volume | 17 |
| Issue | 12 |
| Pages | 3155-3171 |
| Publication date | 2025-12-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Social Robotics (JOURNAL) |
| Journal identifiers | ISSN: 1875-4791 • E-ISSN: 1875-4805 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s12369-025-01259-w |
| OpenAlex | W4410447394 |
| Language | EN |
| References cited | 50 |
Evacuation robots hold promise for facilitating efficient and safe building evacuations during emergencies. While studies have demonstrated that people will follow an evacuation robot [Nayyar 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN):1–6, 1, Robinette et al. 2016 11th ACM/IEEE International Conference On:101–108, 2], many practical hurdles remain. This paper uses a human subject study in a physical environment to investigate robot-guided evacuation of individuals versus small groups and considers different strategies for using robots to guide evacuees to exits. We further show that the data collected from these human subject studies can be used to train evacuee motion models which accurately predict the movement of the evacuee (9.9 cm mean error) while following the robot. We further show that this model can be used to predict the motion of an evacuee in a different environment and show that the accuracy of our model is superior to the more standard social forces model (SFM). The results from this research will contribute to the creation of evacuation robots and to the modeling and prediction of evacuee behavior
Human–computer interaction · Mechatronics · Robot · Robotics · Simulation · Computer Science · Engineering · Evacuation and Crowd Dynamics · Human-Automation Interaction and Safety · Traffic and Road Safety · Artificial Intelligence
Smoothing and Differentiation of Data by Simplified Least Squares Procedures.
Agent-based modeling
Social force model for pedestrian dynamics
A comparative analysis of gradient boosting algorithms
A multi-agent based framework for the simulation of human and social behaviors during emergency evacuations
| Citation velocity | historical |
|---|---|
| Highly cited | No |