ARRC researchers in collaboration with the University of Bologna and ETH Zürich have developed a CNN-based AI deck to enable autonomous navigation of a 27g nano-drone in unknown environments. The CNN allows the drone to recognize and avoid obstacles using only an onboard camera, running 10x faster and using 10x less memory than previous versions. The demo also featured a swarm of nano-drones flying in formation using ultra-wideband communication. Why it matters: This advancement could significantly enhance the capabilities of nano-drones for applications such as disaster response, where quick and efficient intervention is crucial.
The Autonomous Robotics Research Center (ARRC) at TII won the Nanocopter AI Challenge 2022, part of the International Micro Air Vehicle Conference. The challenge involved developing AI-enabled solutions for Bitcraze’s Crazyflie nanocopters to perform vision-based obstacle avoidance. The ARRC team's nano-drone completed a 110m flight in 5 minutes with no crashes in a dynamic environment. Why it matters: This victory demonstrates the growing expertise in autonomous robotics and AI-powered drone technology within the UAE, with potential applications in search and rescue, industrial inspection, and precision agriculture.
The paper presents MonoRace, an onboard drone racing approach using a monocular camera and IMU. The system combines neural-network-based gate segmentation with a drone model for robust state estimation, along with offline optimization using gate geometry. MonoRace won the 2025 Abu Dhabi Autonomous Drone Racing Competition (A2RL), outperforming AI teams and human world champions, reaching speeds up to 100 km/h. Why it matters: This demonstrates a significant advancement in autonomous drone racing, achieving champion-level performance with a resource-efficient monocular system, validated in a real-world competition setting in the UAE.