UAV Inspection Modeling and Hierarchical Optimization Scheduling for Complex Open-Pit Mining Areas

Abstract

This study addresses the safety and efficiency challenges of manual inspection in complex open-pit mining environments, where terrain steepness, limited coverage, and personnel exposure to hazards render conventional methods inadequate. We propose an integrated UAV inspection framework that combines 3D environmental modeling with a hierarchical optimization paradigm. The framework operates in three sequential stages. First, a high-fidelity 3D terrain model is constructed from point cloud data via skeletal feature extraction, which reduces computational complexity while preserving topographic structure. Second, an upper-layer Traveling Salesman Problem (TSP) solver determines the optimal inspection sequence across mandatory points (loading sites, dump sites, and crushing stations). Third, a lower-layer Chaotic Adaptive Population-based Grey Wolf Optimizer (CAP-GWO) refines the 3D path between consecutive TSP-ordered points, augmented by B-spline smoothing to ensure kinematic feasibility. Key inputs include: (i) raw LiDAR point cloud data of the mining site, (ii) facility coordinates and operational constraints (safety margins, maximum pitch angle, minimum turn radius), and (iii) UAV kinematic parameters. Outputs comprise a smooth, collision-free 3D trajectory with verified constraint satisfaction. Comparative experiments against eight metaheuristic algorithms (PSO, GA, ACO, BA, COA, GWO, SRA, SFOA) demonstrate that the proposed method reduces total path length by 15–20% on synthetic benchmark scenarios while maintaining zero constraint violations. Statistical validation via the Sign Test confirms the significance of these improvements (p < 0.05) across repeated independent trials. The framework is further validated on measured airborne LiDAR data of the Bingham Canyon open-pit copper mine (Utah, USA; USGS 3D Elevation Program), one of the largest operating open-pit mines in the world: on this real terrain, CAP-GWO achieves the best performance among the GWO-family algorithms, with a statistically significant 12.5% improvement over SRA (Wilcoxon p < 0.001) and 24% lower variance than the standard GWO, and all 210 experimental runs produce collision-free trajectories. Notably, the proposed hierarchical optimization framework achieves structural symmetry between the upper-layer sequencing task and the lower-layer path refinement task. This symmetric decomposition significantly reduces computational complexity while preserving solution quality, aligning with the principles of symmetry in engineering optimization. The framework offers a practical solution for autonomous, adaptive inspection scheduling in dynamic mining environments.