Deep Reinforcement Learning for Reconnaissance Drone Path Planning with Clustered Targets
TL;DR
This research addresses the reconnaissance drone path planning problem that maximizes total reconnaissance value while ensuring at least one target per cluster is visited within flight distance constraints by proposing a deep reinforcement learning approach combining a Graph Attention Network encoder with an attention-based decoder for sequential decision-making.
Abstract
In modern military operations, unmanned aerial vehicles have become essential intelligence gathering assets. Military targets are distributed in clustered areas according to tactical formations and command structures, requiring comprehensive coverage across all operational areas. This research addresses the reconnaissance drone path planning problem that maximizes total reconnaissance value while ensuring at least one target per cluster is visited within flight distance constraints. We propose a deep reinforcement learning approach combining a Graph Attention Network(GATv2) encoder with an attention-based decoder for sequential decision-making. The encoder captures spatial relationships and cluster structures through edge attribute modeling, while the decoder generates routing decisions via multi-head attention mechanisms with dynamic constraint masking. Computational experiments across three problem scales demonstrate that the proposed approach achieves near-optimal performance on small instances and significantly outperforms time-limited exact optimization methods on larger scales, achieving solutions in seconds enabling real-time mission planning in dynamic operational environments.