Black Swift Technologies (BST) has unveiled a thorough whitepaper outlining the advancements of its SwiftSTL (Safe To Land) technology designed for unmanned aerial vehicles (UAVs). This innovative system leverages cutting-edge machine learning and artificial intelligence to autonomously pinpoint secure landing zones during critical aircraft system failures.
Access the complete whitepaper on BST’s official website.
Developed under a NASA Small Business Innovation Research (SBIR) grant,BST’s technology is currently undergoing testing in readiness for commercial deployment. It rapidly analyzes high-resolution imagery onboard the UAV, enabling the detection of obstacles and terrain features that must be avoided during emergency landings.Utilizing a machine vision approach called semantic segmentation, the system classifies objects at the pixel level, categorizing each pixel into specific labels. This allows for the identification of humans, animals, vehicles, structures, and other critical elements that should be avoided during landing.
SwiftSTL equips UAVs with a comprehensive understanding of their surroundings by efficiently processing vast amounts of data to facilitate real-time decision-making. Beyond ensuring safe landings, this classification technology has versatile applications, including asset management, firefighting, and wildlife conservation. Additionally, it holds promise for deployment in underwater remotely operated vehicles (ROVs) for tasks such as pipeline inspections.
For further insights, download the complete whitepaper from BST’s website.
