Autonomous Wind Turbine Inspection using a Quadrotor

Balanced task allocation and motion planning of a multi-robot system under fuzzy time windows

“…• 𝑁 𝑛 2 is the basis function. • 𝐾 𝑗 is the overall number of control points that compose the digital 𝑀𝑅 𝑗 path.…”

An Overview to Unmanned Aerial Vehicles from Perspectives of Quality and Safety Management in Aviation

“…• Delivering medicine, food and water to the injured Schweizer et al, (2018) • Taking images of areas or infrastructures for damage assessment after disasters Yanchao Liu, (2019) • Food delivery Delivery of Goods Bhatt et al, (2018) • Medicine and blood samples delivery Sumanth Reddy et al, (2019) • Lightweight commercial products delivery Ghazali et al, (2021) • Network gateways Providing Wireless Coverage Wu et al, (2021) • Omnipresent coverage Tazibt et al, (2022) • Data collection S. K. Khan et al, (2021) • Relay nodes between wireless devices Fu et al, (2020) • Crop maturity and yield mapping Precision Agriculture Christiansen et al, (2017) • Field tile mapping Romero et al, (2018) • Irrigation scheduling Faiçal et al, (2017) • Pesticide spraying Kerkech et al, (2020) • Disease detection Stroppiana et al, (2018) • Weed detection Huuskonen & Oksanen, (2018) • Soil texture mapping Kavoosi et al, (2020) • Residue cover and tillage mapping Kas & Johnson, (2020) • Infrastructure internal inspection Infrastructure Inspection Gopalakrishnan et al, (2018) • Surface degradation assessment and crack detection Tan et al, (2021) • Vertical inspection for towers and high rise Jalil et al, (2019) • Power transmission lines inspection Gu et al, (2020) • Wind and oil/gas turbine inspection Nordin et al, (2022) • Onshore and offshore asset inspection Other than their operational purposes, the types of UAVs also differ according to their design features which affect their flying principles. In Figure 5, UAV types are classified first according to their vehicle mass, then the heavy types are further classified to rotor and wing types (Liew et al, 2017).…”

Domain-invariant icing detection on wind turbine rotor blades with generative artificial intelligence for deep transfer learning

“…As a potential solution, there has been a rising interest in leveraging color images (RGB) of turbine rotor blades acquired through cameras installed on the nacelles and the application of computer vision techniques for detecting ice accretion on the surface of the blades (Denhof et al, 2019; Kabardin et al, 2021). A camera generally captures images of the complete rotor blade even under harsh weather conditions (e.g., foggy environments), making this technique more robust than some sensor-based approaches (Chatterjee and Dethlefs, 2020) and suitable for remote autonomous inspection (Gu et al, 2020). As a potential benefit of leveraging preventive maintenance strategies for condition monitoring (Stetco et al, 2019) through computer vision-based icing prediction systems (i.e., before ice accretion can lead to unexpected downtimes or total shutdowns)—rotor blades can be monitored and heating systems can be triggered in advance to prevent ice formation (Merizalde et al, 2019).…”