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 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.
Researchers at the Technology Innovation Institute (TII) have released a fully-annotated dataset for autonomous drone racing, called "Race Against the Machine." The dataset includes high-resolution visual, inertial, and motion capture data from both autonomous and piloted flights, along with commands, control inputs, and corner-level labeling of drone racing gates. The specifications to recreate their flight platform using commercial off-the-shelf components and the Betaflight controller are also released. Why it matters: This comprehensive resource aims to support the development of new methods and establish quantitative comparisons for approaches in robotics and AI, democratizing drone racing research.
Abu Dhabi's Technology Innovation Institute (TII) has developed AI-driven drone technology enabling swarms to collaborate and independently organize tasks without central command. These drones utilize decentralized AI algorithms to adapt formation and behavior based on shared objectives, enhancing scalability and real-time decision-making. TII is collaborating globally to test real-world applications, including disaster management, crop health monitoring, and ecosystem restoration. Why it matters: This advancement positions the UAE as a leader in autonomous robotics and offers solutions for critical applications like disaster response and environmental monitoring.
This paper presents a fully autonomous micro aerial vehicle (MAV) developed to pop balloons using onboard sensing and computing. The system was evaluated at the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2020. The MAV successfully popped all five balloons in under two minutes in each of the three competition runs. Why it matters: This demonstrates the potential of autonomous robotics and computer vision for real-world applications in challenging environments.
TII's Secure Systems Research Center in Abu Dhabi has integrated a secure PX4 stack into a RISC-V based drone, marking a milestone in making RISC-V UAV systems a reality. The center ported DroneCode's PX4 open source software to RISC-V using a commercially available RISC-V development platform. SSRC aims to improve the security and resilience of the PX4 flight control software and NuttX real-time OS, contributing modifications back to the open-source community. Why it matters: This achievement enhances TII's position in drone and autonomous systems research, contributing to safer and more efficient smart city applications in the region.
Technology Innovation Institute (TII) has developed AI-powered autonomous drones capable of navigating complex environments at speeds up to 80 km/h using only a camera and IMU sensor. The drones use onboard AI-driven visual odometry and reinforcement learning to adapt to their environment in real time. In direct competition, the TII drone set a best lap time of 4.38s, compared to 6.32s and 5.34s for human pilots. Why it matters: This research demonstrates the potential of AI-powered UAVs to surpass human-operated drones in agility and precision, with applications for the transport of goods and potentially people.