A Deterministic Search Approach for Solving Stochastic Drone Search and Rescue Planning Without Communications

Evgeny Mishlyakov, Mikhail Gruntov, Alexander Shleyfman, Erez Karpas

Research output: Contribution to journalConference articlepeer-review


In disaster relief efforts, delivering aid to areas with no communication poses a significant challenge. Unmanned aerial vehicles (UAVs) can be utilized to deliver aid kits to survivors in hard-to-reach areas; unfortunately, in some areas, lack of communication and infrastructure presents a key problem. In this paper, we address a stochastic planning problem of planning for a set of UAVs that deliver aid kits to areas that lack communications, where we do not know in advance the locations where aid kits need to be delivered, but rather have probabilistic information about the locations of aid targets. Our main insight is that, despite the stochastic nature of this problem, we can solve it through deterministic search by monitoring the expected reward for each partial solution. This insight enables the application of deterministic planning techniques, empirically demonstrating a notable improvement in efficiency and response speed. Our approach presents a promising solution to addressing the challenge of delivering aid in regions with limited radio infrastructure, as well as similar planning problems.

Original languageEnglish
Pages (from-to)73-81
Number of pages9
JournalThe International Symposium on Combinatorial Search
Issue number1
StatePublished - 2024
Event17th International Symposium on Combinatorial Search, SoCS 2024 - Kananaskis, Canada
Duration: 6 Jun 20248 Jun 2024

Bibliographical note

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© 2024, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.


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