This paper addresses the challenge of navigating unmanned aerial vehicles (UAVs) in contested environments by introducing a cooperative multi-agent framework that increases the likelihood of safe UAV traversal. The approach involves two types of UAVs: low-priority agents that explore and localize threats and a high-priority agent that navigates to its target destination while minimizing the risk of detection by enemy radar systems. The low-priority agents employ a decentralized optimization algorithm to balance exploration, radar localization, and the identification of safe paths for the high-priority agent. For the high-priority agent, two path-planning methods are proposed: one for deterministic scenarios using weighted Voronoi diagrams and another for uncertain scenarios that leverages generalized Voronoi diagrams (using a non-Euclidean criterion derived from uncertainty in the radar’s probability of detection) together with probabilistic constraints. Both methods use optimization techniques to refine trajectories while accounting for kinematic constraints and radar detection probabilities. Numerical simulations demonstrate the effectiveness of the proposed framework. This research advances UAV path-planning methodologies by combining heterogeneous multi-agent cooperation, probabilistic modeling, and optimization to enhance mission success in adversarial environments.
Stagg et al. (Fri,) studied this question.