TII's Secure Systems Research Center (SSRC) has joined Dronecode, a Linux Foundation non-profit, to enhance UAV security. SSRC will contribute to Dronecode's Security SIG, focusing on cryptography, memory protection, and code analysis for the Pixhawk autopilot hardware and PX4 software. SSRC aims to develop and share security and resilience capabilities for the open UAV platform. Why it matters: This partnership enhances the security of drone systems, addressing potential privacy, cybersecurity, and safety threats in line with the UAE's focus on secure autonomous systems.
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.
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.
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.
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.
Team NimbRo presented four UAVs tailored for the MBZIRC 2020 challenges, including target chasing, wall building, and fire fighting. The UAVs utilized onboard object detection, aerial manipulation, LiDAR, and thermal cameras to perform their tasks autonomously. The team's software stack, which is mostly open-source, includes tools for system configuration, monitoring, and agile trajectory generation. Why it matters: The work demonstrates advanced robotics capabilities developed in the context of a major regional competition, advancing machine vision and trajectory generation, and showcasing potential applications in various sectors.