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

## TL;DR

This paper introduces an enhanced discrete particle swarm optimization algorithm for UAV surface inspection path planning, effectively addressing coverage and obstacle avoidance with GPU acceleration for faster computation.

## Contribution

It presents a novel DPSO algorithm with deterministic initialization, mutation, and edge exchange, optimized for GPU implementation to improve efficiency in UAV inspection tasks.

## Key findings

- Improved path planning efficiency demonstrated on real UAV inspection datasets.
- GPU-based implementation significantly reduces computation time.
- Effective handling of coverage and obstacle avoidance in large surface inspections.

## Abstract

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.