# UAV inspection path optimization in offshore wind farms using the OPTION-A*-DQN algorithm

## TL;DR

This paper introduces a new algorithm for optimizing UAV inspection paths in offshore wind farms, improving efficiency and reducing redundancy.

## Contribution

The novel OPTION-A*-DQN algorithm combines A* and deep reinforcement learning for improved UAV path planning in complex environments.

## Key findings

- The proposed method achieves a 10% higher task completion rate compared to existing methods.
- It reduces path distance by 14.9% and simulation time by 20%.
- The algorithm balances global navigation and local optimization effectively.

## Abstract

In response to the inefficiencies in offshore wind farm inspections caused by path redundancy and mission omissions, this study proposes a novel path planning method for Unmanned Aerial Vehicle (UAV) inspections, integrating multi-constraint optimization and intelligent scheduling. First, a four-dimensional constraint model is established, encompassing wind speed, charging, minimum UAV fleet size, and dynamic obstacle avoidance. Second, the OPTION-A*-DQN hybrid algorithm is developed by synergizing A* heuristic search with deep reinforcement learning (DRL) to balance global navigation and local optimization. An improved K-Means algorithm further enables efficient topological partitioning for multi-UAV collaboration. Comparative evaluations against original OPTION-DQN and conventional heuristic methods (Dijkstra and Simulated Annealing) demonstrate that the proposed method achieves three key improvements: (1) a 10% higher task completion rate, (2) a 14.9% reduction in path distance, and (3) a 20% faster simulation time. This work significantly advances intelligent path planning for offshore wind farm inspections.