Maximizing Detection Probability in UAV Search Missions using Random-Key Optimizers
Maximizing Detection Probability in UAV Search Missions using Random-Key Optimizers
This research introduces a configurable, data-driven routing architecture for Unmanned Aerial Vehicles (UAVs) operating in Search and Rescue (SAR) missions. The authors model the problem as a dynamic probabilistic variant of the Open Vehicle Routing Problem (OVRP), utilizing Random-Key Optimizers (RKO) to adaptively switch between maximizing detection probability and minimizing resource usage. The framework is designed to handle stochastic constraints and noisy priors, though it performs approximately half as well when operating under systematically biased maps compared to flying without prior information. Computational tests indicate the scheme is scalable and competitive with single-path scenarios, serving as a decision guide for intelligent planning in unstable environments.
The paper proposes a stochastic routing architecture for UAV search and rescue missions that balances detection probability maximization with resource minimization using Random-Key Optimizers, noting specific performance degradation under biased map priors.
Source: arXiv Robotics — research abstracts · Read original article ↗
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arXiv:2611.12317v1 Announce Type: cross Abstract: This paper focuses on the stochastic and real-time constraints of Unmanned Aerial Vehicle (UAV) path planning in Search and Rescue (SAR) missions. We introduce a configurable data-driven and intelligent routing architecture that combines a closed-loop probability update with the Random-Key Optimizers (RKO). Our problem is mathematically modeled as a dynamic probabilis
Open source S2
tic variant of the Open Vehicle Routing Problem (OVRP), enabling the solver to switch adaptively between two objectives, thereby balancing probability maximization and resource minimization. The framework degrades elegantly under noisy priors but not under biased ones: a systematically biased map performs about half as well as flying with no prior at all. Computational tests show that the proposed scheme is scalable
Open source S3
Maximizing Detection Probability in UAV Search Missions using Random-Key Optimizers
Open source S1
and competitive with single-path scenarios in terms of solution performance and serves as a strong decision guide for intelligent planning in SAR operations in unstable environments
Open source S4
Source：arXiv Robotics — research abstracts · arxiv.org