UAV Aeromagnetic Path Planning in Complex Terrain Based on a Q-Learning-Assisted Multi-Strategy Starfish Optimization Algorithm.

Low-altitude terrain-following flight is essential for obtaining high-quality data in unmanned aerial vehicle (UAV) aeromagnetic surveys, but achieving efficient and safe path planning within complex terrains remains challenging. To address this issue, a Q-learning-assisted multi-strategy Starfish Optimization Algorithm (QMSFOA) is proposed for offline path planning. The proposed algorithm integrates four improvement strategies: (1) employing a Sobol sequence combined with Refraction Opposition-based Learning for population initialization to enhance population diversity; (2) adopting a hybrid adaptive differential mutation mechanism to improve search efficiency; (3) utilizing Q-learning to intelligently schedule optimization modes, thereby accelerating convergence speed; (4) introducing an adaptive t-distribution elite perturbation strategy to refine convergence accuracy. Experimental results on the CEC-2022 benchmark suite indicate that QMSFOA achieves the best convergence accuracy on nine functions and exhibits a superior performance across most metrics compared with the competing algorithms. Simulation experiments of aeromagnetic surveys in complex 3D terrains demonstrate that paths planned by QMSFOA satisfy kinematic and obstacle avoidance constraints while reducing path costs by approximately 25% compared with the standard Starfish Optimization Algorithm (SFOA). Additionally, the standard deviation is reduced by one to two orders of magnitude compared with the competing algorithms. These results demonstrate that the proposed method provides an efficient, reliable, and intelligent solution for high-precision UAV geophysical exploration in complex environments.