Human-Robot Interaction in Search and Rescue: Modeling Victim Behaviour and Trust
How can trust between victims and autonomous rescue robots be computationally modeled and incorporated into a search and rescue simulation to improve evacuation outcomes?
M.P. Grouev (TU Delft - Electrical Engineering, Mathematics and Computer Science)
A. Jamshidnejad – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
S. Schoonebeek – Mentor (TU Delft - Mechanical Engineering)
L. Cavalcante Siebert – Graduation committee member (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
Autonomous robots increasingly support Search and Rescue (SaR), yet victim trust in these robots remains largely unaddressed in simulation, where robots typically neither estimate nor respond to it. This paper implements a transparent, bidirectional trust model in a SaR simulation with cognitively realistic victims based on the GT-BDI framework. Victims maintain a true internal trust state driven by observable signals–proximity and perceived danger–while the robot estimates this trust from proximity and switches between approaching the victim to build trust and leading it to the exit. An ablation study over 100 matched seeds compared trust-aware against trust-blind robots. Trust-aware behavior saved significantly more victims (+0.74 on average, p < 0.001), reducing victims stranded at timeout rather than lowering mortality. This gain stemmed not from higher trust, but from better robot positioning. These results suggest that how a robot responds to trust can meaningfully improve evacuation outcomes, offering a practical and interpretable path to better robot-guided rescue.