Dynamic Reconnaissance Task Planning for Multi-UAV Based on Learning-Enhanced Pigeon-Inspired Optimization

Abstract

In dynamic and uncertain reconnaissance missions, effective task assignment and path planning for multiple unmanned aerial vehicles (UAVs) present significant challenges. A stochastic multi-UAV reconnaissance scheduling problem is formulated as a combinatorial optimization task with nonlinear objectives and coupled constraints. To solve the non-deterministic polynomial (NP)-hard problem efficiently, a novel learning-enhanced pigeon-inspired optimization (L-PIO) algorithm is proposed. The algorithm integrates a Q-learning mechanism to dynamically regulate control parameters, enabling adaptive exploration–exploitation trade-offs across different optimization phases. Additionally, geometric abstraction techniques are employed to approximate complex reconnaissance regions using maximum inscribed rectangles and spiral path models, allowing for precise cost modeling of UAV paths. The formal objective function is developed to minimize global flight distance and completion time while maximizing reconnaissance priority and task coverage. A series of simulation experiments are conducted under three scenarios: static task allocation, dynamic task emergence, and UAV failure recovery. Comparative analysis with several updated algorithms demonstrates that L-PIO exhibits superior robustness, adaptability, and computational efficiency. The results verify the algorithm’s effectiveness in addressing dynamic reconnaissance task planning in real-time multi-UAV applications.

Keywords

unmanned aerial vehicle (UAV) / pigeon-inspired optimization / reinforcement learning / dynamic task planning / coverage path planning