Terrain-Aware Adaptation for Two-Dimensional UAV Path Planners
Karakontis, Kostas, Petsanis, Thanos, Kapoutsis, Athanasios Ch., Kapoutsis, Pavlos Ch., Kosmatopoulos, Elias B.
–arXiv.org Artificial Intelligence
-- Multi-UA V Coverage Path Planning (mCPP) algorithms in popular commercial software typically treat a Region of Interest (RoI) only as a 2D plane, ignoring important 3D structure characteristics. This leads to incomplete 3D reconstructions, especially around occluded or vertical surfaces. In this paper, we propose a modular algorithm that can extend commercial two-dimensional path planners to facilitate terrain-aware planning by adjusting altitude and camera orientations. T o demonstrate it, we extend the well-known DARP (Divide Areas for Optimal Multi-Robot Coverage Path Planning) algorithm and produce DARP-3D. Compared to baseline, our approach consistently captures improved 3D reconstructions, particularly in areas with significant vertical features. An open-source implementation of the algorithm is available here: https://github.com/konskara/T
arXiv.org Artificial Intelligence
Jul-25-2025
- Country:
- Asia > Middle East
- UAE > Dubai Emirate > Dubai (0.05)
- Europe > Greece
- Attica > Athens (0.04)
- Central Macedonia > Thessaloniki (0.04)
- Asia > Middle East
- Genre:
- Research Report (0.50)
- Workflow (0.46)
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- Government (0.46)
- Transportation > Air (0.46)
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