A multirobot system for autonomous deployment and recovery of a blade crawler for operations and maintenance of offshore wind turbine blades

Offshore wind farms will play a vital role in the global ambition of net zero energy generation. Future offshore wind farms will be larger and further from the coast, meaning that traditional human-based operations and maintenance approaches will become infeasible due to safety, cost, and skills shortages. The use of remotely operated or autonomous robotic assistants to undertake these activities provides an attractive alternative solution. This paper presents an autonomous multirobot system which is able to transport, deploy and retrieve a wind turbine blade inspection robot using an unmanned aerial vehicle (UAV). The proposed solution is a fully autonomous system including a robot deployment interface for deployment, a mechatronic link-hook module (LHM) for retrieval, both installed on the underside of a UAV, a mechatronic on-load attaching module installed on the robotic payload and an intelligent global mission planner. The LHM is integrated with a 2-DOF hinge that can operate either passively or actively to reduce the swing motion of a slung load by approximately 30%. The mechatronic modules can be coupled and decoupled by special maneuvers of the UAV, and the intelligent global mission planner coordinates the operations of the UAV and the mechatronic modules for synchronous and seamless actions. For navigation in the vicinity of wind turbine blades, a visual-based localization merged with the location knowledge from Global Navigation Satellite System has been developed. A proof-of-concept system was field tested on a full-size decommissioned wind-turbine blade. The results show that the experimental system is able to deploy and retrieve a robotic payload onto and from a wind turbine blade safely and robustly without the need for human intervention. The vicinity localization and navigation system have shown an accuracy of 0.65 and 0.44 m in the horizontal and vertical directions, respectively. Furthermore, this study shows the feasibility of systems toward autonomous inspection and maintenance of offshore windfarms.