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.
TII's Secure Systems Research Center (SSRC) has announced partnerships with Khalifa University, Georgia Institute of Technology, Tampere University, and SUPSI across multiple projects. These collaborations aim to advance secure systems research, with Khalifa University focusing on machine learning for exfiltration detection and secure drone communication, Georgia Tech working on attack-resilient software for cyber-physical systems, Tampere University focusing on critical infrastructure security, and SUPSI exploring AI-based secure autonomous navigation on Nano-UAVs. Why it matters: These partnerships highlight the UAE's commitment to advancing research and development in secure systems and autonomous technologies, fostering innovation and expertise in critical areas like cybersecurity and UAV technology.
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 presents the design and deployment of an autonomous unmanned ground vehicle (UGV) equipped with a robotic arm for urban firefighting. The UGV uses on-board sensors for navigation and a thermal camera for fire source identification, with a custom pump for fire suppression. The system was developed for the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2020, where it achieved the highest score among UGV solutions and contributed to winning first place. Why it matters: This demonstrates the potential of autonomous robotics in addressing complex and dangerous real-world challenges like urban firefighting in the GCC region and beyond.
Researchers present RUR53, an unmanned ground vehicle (UGV) capable of autonomous navigation, object recognition, and tool manipulation. The UGV uses a modular software architecture, enabling it to perform complex tasks like detecting panels, docking, and manipulating tools such as wrenches and valve stems. RUR53 was tested at the 2017 Mohamed Bin Zayed International Robotics Challenge where it ranked third in the Grand Challenge as part of a collaboration. Why it matters: This research demonstrates advanced robotics capabilities applicable to various industrial and inspection tasks, highlighting the UAE's focus on robotics innovation.