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Updated: Jun 27, 2026
08:16
Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
Published on: October 24, 2025
ORACLE: Object-Centric Autonomous Coverage Exploration Planner for Discrete Trunk Inspection Under Canopy.
1School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China.
Sensors (Basel, Switzerland)
|June 26, 2026
Summary
Autonomous drones now use target-guided exploration for inspecting obstacles like trees. This new Object-centric Autonomous Coverage Exploration (ORACLE) framework significantly improves coverage completeness and reduces mission overhead in forests.
Area of Science:
- Robotics
- Artificial Intelligence
- Computer Vision
Background:
- Autonomous Unmanned Aerial Vehicles (UAVs) are crucial for inspecting discrete obstacles in complex environments like forests.
- Current space-guided exploration methods are inefficient, leading to incomplete coverage and redundant paths by ignoring target locations.
Purpose of the Study:
- To introduce ORACLE, an Object-centric Autonomous Coverage Exploration framework for efficient UAV-based obstacle inspection.
- To shift the planning paradigm from space-guided to target-guided exploration using obstacles as natural planning anchors.
Main Methods:
- ORACLE integrates online target detection using occupied-voxel connected component labelling.
- A density-aware global coverage planner prioritizes target-dense regions by modulating ATSP costs.
- A target-guided local planner utilizes Sequential Ordering Problem formulation for direct obstacle observation.
Main Results:
- ORACLE achieved 98.8% and 99.7% target coverage in dense forest environments, vastly outperforming a space-guided baseline (22.7% and 25.1%).
- Mission overhead ratio was reduced from over 200% to approximately 127% in tested environments.
- Ablation studies confirmed zone reactivation is critical for coverage completeness and density weighting enhances path efficiency.
Conclusions:
- The Object-centric Autonomous Coverage Exploration (ORACLE) framework offers a significant advancement in autonomous inspection tasks.
- Target-guided exploration, leveraging environmental features, is more effective than traditional space-guided methods for complete and efficient coverage.
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