arXiv Robotics — research abstracts· Jok\=ubas Dim\v{s}a, Maria Grouev, Filip Keerberg, Nicolas Laplagne, Tigo Schutgens, Sven Schoonebeek, Anahita Jamshidnejad·2026-11-10 05:00 UTC· 2 days agoSignalEditorial score75

PA-SaR: Perception-Aware Learning, Control, and Coordination for Multi-Robot Search and Rescue

Summary

This research introduces PA-SaR, a framework integrating local control, learning-based exploration, supervisory task allocation, and adaptive victim guidance for autonomous search and rescue. Simulation studies reveal trade-offs between collision avoidance and search completion, show that increased exploration does not always lead to more rescues, and demonstrate that adaptive guidance improves rescue outcomes relative to continuous leading without improving victim trust or reducing mortality.

Editorial context

The study presents a mathematical and architectural framework for coordinating heterogeneous robots in search and rescue missions, demonstrating through simulations that supervisory task allocation improves search completion beyond information sharing alone and that adaptive victim guidance increases rescues without necessarily improving victim trust or reducing mortality.

Source: arXiv Robotics — research abstracts · Read original article ↗

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What the source reports

Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.

Environment: SimulationOpen source S4
Control: Reported autonomousOpen source S2

paper: not_reported

Source excerpts and review record

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is for integrating complementary mechanisms and analyzing their interactions across mission stages. We demonstrate the framework through single- and multi-robot simulation studies incorporating model-predictive and learning-based exploration, perception-based safety shielding, event-driven task allocation, and adaptive victim guidance. The results show trade-offs between collision avoidance and search completion, ind
Open source S4

arXiv:2611.11648v2 Announce Type: replace Abstract: Autonomous SaR requires robots to explore unknown or partially known environments, avoid hazards, coordinate team activities, and guide discovered victims to safety. Improving individual capabilities of robots does not necessarily improve mission outcomes: safer motion can restrict the exploration behavior, faster discovery can increase the rescue demand, and effect
Open source S2

Source：arXiv Robotics — research abstracts · arxiv.org

#Research #VLA #Robot policies #Imitation learning #Reinforcement learning #Sim-to-real