# Multi-UAV trajectory planning for 3D visual inspection of complex structures

arXiv: https://arxiv.org/abs/2204.10070 · HTML: https://mlsanity.com/paper/2204.10070/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2022.

## 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)
- [MaskPlanner: Learning-Based Object-Centric Motion Generation from 3D Point Clouds](https://mlsanity.com/paper/2502.18745/) (2025)
- [Invisible Servoing: a Visual Servoing Approach with Return-Conditioned Latent Diffusion](https://mlsanity.com/paper/2409.13337/) (2024)
- [Ant Colony Optimization for Cooperative Inspection Path Planning Using Multiple Unmanned Aerial Vehicles](https://mlsanity.com/paper/2402.08246/) (2024)
- [Methods for the Segmentation of Reticular Structures Using 3D LiDAR Data: A Comparative Evaluation](https://mlsanity.com/paper/2507.20589/) (2025)
- [An End-to-end Flight Control Network for High-speed UAV Obstacle Avoidance based on Event-Depth Fusion](https://mlsanity.com/paper/2603.27181/) (2026)

## Abstract (arXiv)

The application of autonomous UAVs to infrastructure inspection tasks provides benefits in terms of operation time reduction, safety, and cost-effectiveness. This paper presents trajectory planning for three-dimensional autonomous multi-UAV volume coverage and visual inspection of infrastructure based on the Heat Equation Driven Area Coverage (HEDAC) algorithm. The method generates trajectories using a potential field and implements distance fields to prevent collisions and to determine UAVs' camera orientation. It successfully achieves coverage during the visual inspection of complex structures such as a wind turbine and a bridge, outperforming a state-of-the-art method by allowing more surface area to be inspected under the same conditions. The presented trajectory planning method offers flexibility in various setup parameters and is applicable to real-world inspection tasks. Conclusively, the proposed methodology could potentially be applied to different autonomous UAV tasks, or even utilized as a UAV motion control method if its computational efficiency is improved.