ORACLE: Object-Centric Autonomous Coverage Exploration Planner for Discrete Trunk Inspection Under Canopy

Autonomous inspection of discrete obstacles (e.g., tree trunks in orchards and forests) requires UAVs to visit every target with proper observation distance and heading, while simultaneously exploring the unknown environment. Existing space-guided exploration methods focus on eliminating unknown space and are inherently agnostic to the inspection targets themselves, leading to incomplete coverage and redundant traversal. We observe that the obstacles themselves encode the spatial topology of the environment and can serve as natural planning anchors. Based on this insight, we propose ORACLE, an Object-centric Autonomous Coverage Exploration framework that shifts the planning paradigm from space-guided to target-guided exploration. ORACLE integrates: (1) an online target detection and persistent identification module via occupied-voxel connected component labelling, (2) a density-aware global coverage planner that modulates ATSP costs to prioritize target-dense regions, and (3) a target-guided local planner that replaces frontier viewpoints with direct obstacle observation points in a Sequential Ordering Problem formulation. Experiments in two point-cloud environments reconstructed from real-world forests with contrasting tree densities (Environment I: 50 trunks, $\overline{n}=1.56$; Environment II: 70 trunks, $\overline{n}=2.19$; both with non-uniform spacing) show that ORACLE achieves $98.8\%$ and $99.7\%$ target coverage compared to $22.7\%$ and $25.1\%$ for the space-guided baseline, while reducing the mission overhead ratio from $202.9\%$ to $129.2\%$ (Environment I) and from $176.8\%$ to $126.6\%$ (Environment II). Ablation studies confirm that zone reactivation is the decisive factor for coverage completeness ($-18.8$ and $-17.2$ percentage points when disabled in Environments I and II, respectively) and that density weighting improves path efficiency.