# A distributed differential game approach to trajectory planning for offshore wind farm inspection

## TL;DR

This paper introduces a new method for coordinating multiple drones to inspect offshore wind farms efficiently and safely.

## Contribution

A distributed differential game framework is proposed for energy-aware trajectory planning in multi-UAV systems.

## Key findings

- The proposed method reduces task completion time by up to 18.7% compared to conventional approaches.
- The strategy ensures safe return of all UAVs while optimizing inspection efficiency.
- The approach guarantees convergence to a global Nash equilibrium under energy and round-trip constraints.

## Abstract

To address the complex challenges associated with multiple unmanned aerial vehicles (multi-UAVs) cooperative inspection in offshore wind farms, including limited sensing and communication ranges, constrained battery capacity, and round-trip mission requirements, this paper introduces an optimal coordinated trajectory method for multi-UAV based on a distributed differential game (DDG) framework. The approach explicitly accounts for energy consumption, incorporating round-trip requirements into a game-theoretic objective function to facilitate energy-aware trajectory planning. Each UAV operates based solely on local information from neighboring UAVs, enabling distributed decision-making that ensures collision-free coordination while optimizing global inspection time and overall energy efficiency. The convergence of the proposed strategy to a global Nash equilibrium (G-NE), as confirmed by theoretical analysis, ensures system-level coordination optimality subject to round-trip and energy constraints. Simulation results demonstrate that the method significantly enhances inspection efficiency and reduces task completion time by up to 18.7% compared to conventional approaches, while guaranteeing the safe return of all UAVs.