ICRA 2026: ASAR — Minimum Expected-detection-time Paths for Search and Rescue (SAR)
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Eric's paper on optimal SAR planning has been accepted to ICRA 2026 in Vienna, Austria. ASAR finds search patterns that minimize the expected detection time of an object given uncertainty about its location. There is an open access version of the paper on arXiv, a trailer video on YouTube, and we're really looking forward to sharing more about it at ICRA!
- Publication
- Conference
- Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)
- Location
- Vienna, Austria
- Date
- Notes
- To appear
Abstract
Searches are conducted to find missing persons and/or objects given uncertain information, imperfect observers and large search areas in Search and Rescue (SAR). In many scenarios, such as Maritime SAR, expected survival times are short and optimal search could increase the likelihood of success. This optimization problem is complex for nontrivial problems given its probabilistic nature.
Stochastic optimization methods search large problems by nondeterministically sampling the space to reduce the effective size of the problem. This has been used in SAR planning to search otherwise intractably large problems but the stochastic nature provides no formal guarantees on the quality of solutions found in finite time.
This paper instead presents ASAR, an ε-optimal search algorithm for SAR planning. It calculates a heuristic to bound the search space and uses graph-search methods to find solutions that are formally guaranteed to be within a user-specified factor, ε, of the optimal solution. It finds better solutions faster than existing optimization approaches in operational simulations. It is also demonstrated with a real-world field trial on Lake Ontario, Canada, where it was used to locate a drifting manikin in only 150s.