TII's Secure Systems Research Center (SSRC) is partnering with Khalifa University, University of Modena and Reggio Emilia, University of Bologna, University of Waterloo, and McMaster University to develop a RISC-V-based secure flight computer system. The project aims to create an open RISC-V-based System on a Chip (SoC) architecture and software stack for secure application processors in drone flight computers. The collaboration seeks to improve performance, efficiency, reliability, and security relative to current commercial flight computer systems. Why it matters: This international collaboration strengthens the UAE's position in advanced hardware and software co-design for critical applications like drone technology, while also fostering local expertise through partnerships with UAE universities.
This paper details the autonomous drone racing system developed for the Abu Dhabi Autonomous Racing League (A2RL) x Drone Champions League competition. The system uses drift-corrected monocular Visual-Inertial Odometry (VIO) fused with YOLO-based gate detection for global position measurements, managed via Kalman filter. A perception-aware planner generates trajectories balancing speed and gate visibility. Why it matters: The system's podium finishes validate the effectiveness of monocular vision-based autonomous drone flight and showcases advancements in AI-powered robotics within the UAE.
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.
The article discusses the potential of AI in piloting planes, noting current autopilot systems still require human input. Martin Takáč from MBZUAI expresses confidence in AI's ability to handle flight scenarios, citing its capacity for extensive simulation and error minimization through reinforcement learning. AI is already used in aviation for tasks like route planning and maintenance. Why it matters: The piece highlights the growing role of AI in aviation and raises important questions about the future of autonomous flight in the region.
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.