MUMBAI, India, Sept. 28 -- Intellectual Property India has published a patent application (202511087713 A) filed by Netaji Subhas University Of Technology on September 15, 2025, for “remote-Access Uav Learning Platform For Safe Indoor Op-Eration With Digital Twin Integration And Camera-Based Mon-Itoring.
Inventor includes Prof. Vijyant Agarwal.
The application for the patent was published on September 25, 2026, under issue no. 39/2026.
Abstract: The present invention discloses a cloud-enabled remote-access unmanned aerial vehicle (UAV) learning platform designed for safe indoor laboratory use. UAVs are confined within a controlled enclosure through virtual geofencing and trajectory bounding, enabling full three-dimensional flight while ensuring safety. The platform uniquely integrates physical UAVs with digital twin simulations and a multi-camera visualization system, allowing remote learners to observe real-time drone behavior alongside modeled dynamics. A key feature is the integration of edge computing with the cloud-based remote-access framework, wherein computationally intensive tasks such as trajectory optimization, latency modeling, and AI-driven flight control are offloaded to distributed edge nodes. This architecture ensures low-latency responsiveness for real-time UAV command execution while maintaining global accessibility for learners, thereby bridging physical experimentation with scalable online deployment. The system supports progressive learning, beginning with aerial kinematics, advancing to six-degree-of-freedom dynamics, and extending to trajectory following using PID controllers and AI algorithms. A constraint filtering engine enforces limits on altitude, velocity, and thrust, preventing crashes during experimentation. A further novelty lies in the intentional latency injection framework, which converts communication delays into an instructional feature to demonstrate instability and phase lag in closed-loop control. An AI-driven trajectory generator personalizes flight tasks based on student performance, while gamified benchmarking and collective reinforcement learning enable performance evaluation and policy optimization. The invention thus provides a scalable, interactive, and adaptive UAV education platform that leverages cloud-edge integration for remote accessibility, safety, and real-time responsiveness, making it applicable to academic teaching, online courses, workforce training, and aerial robotics research
Disclaimer: Curated by HT Syndication.
