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Perceptual Robotics - Autonomous Drone Technology for Wind Turbine Inspections
Perceptual Robotics is pioneering advancements in wind turbine inspections by integrating cutting-edge autonomous drone technology with AI-driven analytics. Their solutions focus on enhancing the efficiency, accuracy, and safety of inspection processes for both onshore and offshore wind turbines. The company's drones, like Dot and Eve, offer versatile inspection capabilities. Dot performs comprehensive inspections using high-resolution imagery with minimal human involvement, leading to faster data collection and higher operational efficiency. Eve is designed for on-demand, rapid inspections without needing external contractors. The drones are equipped to operate in challenging offshore environments, utilizing automatic take-off and landing features to reduce personnel risk. Driven by AI, their system processes inspection data swiftly to provide actionable insights for predictive maintenance, thus minimizing downtime. With features such as high-resolution imagery and centralized data management, operators can prioritize repairs, track damages over time, and optimize fleet performance, extending the lifespan of the assets while lowering operational costs.
Dot: Smooth Inspection Campaigns
Dot, our fully autonomous drone, captures high-resolution imagery and performs detailed turbine inspections with minimal human involvement. This all-in-one solution ensures rapid, accurate data collection, enabling faster turnarounds and higher operational efficiency.
Eve: Portable Kit for On-Demand Inspections
Eve, our compact, autonomous mini-drone, is ideal for smaller or urgent inspection tasks. This lightweight solution can be deployed swiftly, reducing hassle for on-site managers without needing external contractors, saving time and reducing costs.
Reliable, High-Quality Data in Challenging Environments
Our autonomous drones thrive in offshore environments, executing inspections with automatic take-off and landing from vessels. This minimises personnel risk while ensuring consistent, high-quality data collection, even in harsh conditions.
