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Research Paper | Renewable and Sustainable Energy | Volume 15 Issue 3, March 2026 | Pages: 711 - 720 | India
Physical AI-Driven Robotic Maintenance System with Automatic Tool Changer for a Wind Turbine
Abstract: Ensuring safer and more efficient wind?turbine maintenance has led the industry to adopt robotic systems that can operate directly inside the nacelle. This work introduces a compact rail?mounted robot designed for the narrow and crowded nacelle space. Equipped with an Automatic Tool Changer (ATC) and an organized onboard toolbox, the robot can autonomously pick, use, and return tools for tasks such as bolt tightening, thermal inspections, and lubrication?unit servicing. Its mobility system enables stable movement through confined pathways, making it compatible with different turbine models without structural changes. By taking over repetitive tool?handling and inspection work, the robot reduces technician exposure to hazardous conditions and minimizes downtime. A Physical?AI?based control layer supports autonomous operation by interpreting Service Work Instructions (SWI), planning task sequences, and adjusting actions using real?time sensor and environmental feedback. High?resolution cameras assist with continuous fault and alarm monitoring, while a compact lifting unit helps manage heavier components in restricted areas. Integrated diagnostics and data?driven planning further enhance reliability by identifying failures early. Overall, the system offers a scalable and intelligent approach to nacelle?level maintenance, improving safety, lowering lifecycle costs, and increasing turbine availability.
Keywords: Wind turbine maintenance, Nacelle Inspection Robot, Automatic Tool Changer (ATC), Autonomous service planning, predictive fault monitoring, Service Work Instruction (SWI)
How to Cite?: Mrunal Vemuganti, Akshay V, "Physical AI-Driven Robotic Maintenance System with Automatic Tool Changer for a Wind Turbine", Volume 15 Issue 3, March 2026, International Journal of Science and Research (IJSR), Pages: 711-720, https://www.ijsr.net/getabstract.php?paperid=SR26303170112, DOI: https://dx.dx.doi.org/10.21275/SR26303170112