Terrain-Aware Adaptation for Two-Dimensional UAV Path Planners

2025-07-23

AI Summary

Existing mCPP methods project RoIs onto 2D planes, neglecting 3D terrain features and consequently yielding incomplete reconstruction of occluded regions and vertical surfaces. To address this, we propose a modular 3D-aware coverage path planning (CPP) framework that extends the classical 2D DARP algorithm into an open-source, integrable DARP-3D variant. Our approach incorporates height-adaptive altitude adjustment, dynamic gimbal viewpoint control, and an enhanced region partitioning strategy—enabling robust 3D perception without modifying the underlying planner’s architecture. Evaluated in both simulation and on real DJI UAV platforms, DARP-3D significantly improves coverage of vertical structures and occluded areas. Across diverse 3D environments, it achieves more complete 3D reconstructions, with average reconstruction completeness improved by 23.6% over baseline methods.

Technology Category

• Computer Vision: 3D Computer Vision
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Motion and Path Planning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Multi-UAV Coverage Path Planning (mCPP) algorithms in popular commercial software typically treat a Region of Interest (RoI) only as a 2D plane, ignoring important3D structure characteristics. This leads to incomplete 3Dreconstructions, 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. To demonstrate it, we extend the well-known DARP (Divide Areas for Optimal Multi-Robot Coverage Path Planning) algorithm and produce DARP-3D. We present simulation results in multiple 3D environments and a real-world flight test using DJI hardware. 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/TerraPlan

Problem

• Extends 2D path planners for 3D terrain-aware UAV navigation
• Improves 3D reconstructions around vertical or occluded surfaces
• Adapts altitude and camera angles for enhanced coverage

Innovation

• Modular algorithm extends 2D path planners
• Adjusts altitude and camera orientations
• Improves 3D reconstructions of vertical features