Maximizing Detection Probability in UAV Search Missions using Random-Key Optimizers
Overview
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.
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- arXiv Robotics — research abstractsSignalMaximizing 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.
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