# Enhanced discrete particle swarm optimization path planning for UAV vision-based surface inspection

arXiv: https://arxiv.org/abs/1706.04399 · HTML: https://mlsanity.com/paper/1706.04399/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2017.

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

- [Safety-enhanced UAV Path Planning with Spherical Vector-based Particle Swarm Optimization](https://mlsanity.com/paper/2104.10033/) (2021)
- [Drone-as-a-Service Composition Under Uncertainty](https://mlsanity.com/paper/2103.06513/) (2021)
- [AutoRepo: A general framework for multi-modal LLM-based automated construction reporting](https://mlsanity.com/paper/2310.07944/) (2023)
- [Navigation Variable-based Multi-objective Particle Swarm Optimization for UAV Path Planning with Kinematic Constraints](https://mlsanity.com/paper/2501.03261/) (2025)
- [Enhanced Teaching-Learning-based Optimization for 3D Path Planning of Multicopter UAVs](https://mlsanity.com/paper/2205.15913/) (2022)
- [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)

## Abstract (arXiv)

In built infrastructure monitoring, an efficient path planning algorithm is essential for robotic inspection of large surfaces using computer vision. In this work, we first formulate the inspection path planning problem as an extended travelling salesman problem (TSP) in which both the coverage and obstacle avoidance were taken into account. An enhanced discrete particle swarm optimization (DPSO) algorithm is then proposed to solve the TSP, with performance improvement by using deterministic initialization, random mutation, and edge exchange. Finally, we take advantage of parallel computing to implement the DPSO in a GPU-based framework so that the computation time can be significantly reduced while keeping the hardware requirement unchanged. To show the effectiveness of the proposed algorithm, experimental results are included for datasets obtained from UAV inspection of an office building and a bridge.