# Multi-UAV Coverage Path Planning for the Inspection of Large and Complex Structures

arXiv: https://arxiv.org/abs/2007.13065 · HTML: https://mlsanity.com/paper/2007.13065/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2020.

## Built on by (most cited first, 7 of 7)

- [A Review on Viewpoints and Path-planning for UAV-based 3D Reconstruction](https://mlsanity.com/paper/2205.03716/) (2022)
- [PaintNet: Unstructured Multi-Path Learning from 3D Point Clouds for Robotic Spray Painting](https://mlsanity.com/paper/2211.06930/) (2022)
- [VLM-RRT: Vision Language Model Guided RRT Search for Autonomous UAV Navigation](https://mlsanity.com/paper/2505.23267/) (2025)
- [MaskPlanner: Learning-Based Object-Centric Motion Generation from 3D Point Clouds](https://mlsanity.com/paper/2502.18745/) (2025)
- [UAS-based Automated Structural Inspection Path Planning via Visual Data Analytics and Optimization](https://mlsanity.com/paper/2312.15109/) (2023)
- [Ant Colony Optimization for Cooperative Inspection Path Planning Using Multiple Unmanned Aerial Vehicles](https://mlsanity.com/paper/2402.08246/) (2024)
- [Coverage Path Planning: Classical Foundations, Recent Advances, and Future Directions](https://mlsanity.com/paper/2607.10649/) (2026)

## Abstract (arXiv)

We present a multi-UAV Coverage Path Planning (CPP) framework for the inspection of large-scale, complex 3D structures. In the proposed sampling-based coverage path planning method, we formulate the multi-UAV inspection applications as a multi-agent coverage path planning problem. By combining two NP-hard problems: Set Covering Problem (SCP) and Vehicle Routing Problem (VRP), a Set-Covering Vehicle Routing Problem (SC-VRP) is formulated and subsequently solved by a modified Biased Random Key Genetic Algorithm (BRKGA) with novel, efficient encoding strategies and local improvement heuristics. We test our proposed method for several complex 3D structures with the 3D model extracted from OpenStreetMap. The proposed method outperforms previous methods, by reducing the length of the planned inspection path by up to 48%