Autonomous coordination
Drones work together around the turbine to cover the blades from complementary viewpoints.
FFG-funded cooperative research project
DORBINE
Wind-turbine inspection with autonomous drone swarms
Developing an innovative inspection framework that uses multiple drones, high-resolution cameras, and artificial intelligence to inspect wind-turbine blades in real time without impacting turbine operation.
Project start
1 April 2025
Project end
31 March 2028
Focus
Wind-energy infrastructure
Technology
AI · Drones · Robotics
Grant no.
FO999923600
About
Drone-based inspection for operating wind turbines
DORBINE is a cooperative project funded by the Austrian Research Promotion Agency (FFG). It brings together expertise from Alpen-Adria-Universität (AAU) Klagenfurt , including the Department of Information Technology (ITEC) and the Control of Networked Systems (CNS) research group from the Institute of Intelligent System Technologies, in collaboration with AIR6 Systems.
Project Description
Renewable energy plays a critical role in the global transition to sustainable and environmentally friendly power sources, and among the various technologies, turbines stand out as a key contributor. Wind turbines, for example, can convert up to 45% of the available wind energy into electricity, with modern designs reaching efficiencies as high as 50%, depending on conditions. The DORBINE project aims to enhance wind turbine efficiency in electricity production by developing an innovative inspection framework powered by cutting-edge AI techniques. It leverages a swarm of drones equipped with high-resolution cameras and advanced sensors to perform real-time, detailed blade inspections without the need for turbine shutdowns.
Research
Coordinated drone inspection and AI-supported assessment
DORBINE connects autonomous flight, coordinated image capture, and intelligent analysis in a single workflow for inspecting operating wind turbines.
Continuous operationInspect blades without planned turbine shutdowns.
Earlier insightSupport faster assessment through coordinated imagery and AI.
Practical researchDevelop and test the workflow through simulation and field activities.
Detailed image capture
High-resolution cameras collect visual inspection data while the turbine remains operational.
Intelligent assessment
AI methods help turn the collected imagery into timely, actionable information about blade condition.
Operational efficiency
Inspection without turbine shutdowns can reduce downtime and support better-informed maintenance planning.
Publications
This section lists peer-reviewed publications that acknowledge or result from the DORBINE project.
Updates
Resources
Open tools and models developed for the project