| Project Reference: | ITP/045/25LP |
| Project Title: | AI-assisted UWB Locationing and Obstacle Avoidance Using Video Analytics for LAE Landing / Take-off Operations |
| Hosting Institution: | LSCM R&D Centre (LSCM) |
| Abstract: | In recent years, the application scenarios of unmanned aerial vehicles (UAVs) have rapidly expanded across sectors such as logistics, transportation, emergency response, surveying, and entertainment. From consumer-grade drones to industrial-grade systems, UAVs are driving the development of the low-altitude economy (LAE). However, as the market continues to grow, safety concerns have emerged — including incidents of uncontrolled landings leading to collisions, and flight path deviations causing delays to manned aircraft. Ensuring the safe and healthy development of the low-altitude economy requires strengthened measures for secure drone operations. This project focuses on addressing two critical safety challenges. First, to tackle the issue of undetected obstacles during drone landings due to sensor blind spots or dynamic objects, we will develop AI-powered models for intelligent obstacle recognition and foreign object detection. Second, to overcome the limitations of GPS, which is prone to interference from urban obstructions, electromagnetic sources, or ionospheric scintillation, we will introduce high-precision UWB positioning systems and multi-source data fusion algorithms to enhance landing accuracy. Finally, we will develop a real-time safety monitoring system that integrates positioning data, obstacle detection results, and drone flight telemetry. This platform-based system will provide real-time visualization and risk alerts, helping to improve situational awareness and enhance overall flight safety. |
| Project Coordinator: | Dr To Bun Ng |
| Approved Funding Amount: | HK$ 2.74M |
| Project Period: | 15 Aug 2025 - 15 Nov 2026 |
