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

Engineering notes

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为 VLA、具身智能、人形机器人控制、机器人操作等高价值论文补充中文说明。

Original 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 effective guidance depends on victim responses. We introduce PA-SaR, a mathematical and architectural framework for coordinating heterogeneous local control, learning-based exploration, supervisory task allocation, and adaptive approach-lead victim guidance around mission-level objectives. PA- SaR distinguishes environmental ground-truth, robot-specific perceptions, and simulated victim behavior. This provides a common basis 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, indicate that supervisory allocation improves search completion beyond information sharing alone, and demonstrate that increased exploration does not necessarily yield more rescues. Adaptive guidance increases rescues relative to continuous leading, without a detected improvement in mean or final victim trust or a reduction in mortality. These findings support evaluating local control, team coordination, and evacuation as related but distinct aspects of autonomous SaR within the considered simulation environments.

Links and sources

• Official / arXiv page
• PDF from original source

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