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.
This paper presents a decentralized multi-agent unmanned aerial system designed for search, pickup, and relocation of objects. The system integrates multi-agent aerial exploration, object detection/tracking, and aerial gripping. The decentralized system uses global state estimation, reactive collision avoidance, and sweep planning for exploration. Why it matters: The system's successful deployment in demonstrations and competitions like MBZIRC highlights the potential of integrated robotic solutions for complex tasks such as search and rescue in the region.
This paper details an autonomous cooperative wall-building system using UAVs developed for Challenge 2 of the MBZIRC 2020 competition. The system employs scanning, RGB-D detection, precise grasping, and multi-UAV coordination to place bricks on a wall. The CTU-UPenn-NYU approach achieved the highest score in the competition by correctly placing the most bricks. Why it matters: This demonstrates advanced capabilities in robotics and autonomous systems relevant for construction and infrastructure development in challenging environments.
TII's Secure Systems Research Center (SSRC) has partnered with Purdue University on a three-year cybersecurity project focused on ensuring the safe and efficient use of Unmanned Aerial Vehicles (UAVs) in urban environments. The collaboration will study security and resilience in cyber-physical and autonomous systems, addressing vulnerabilities in communication, navigation, and command and control. The project includes four phases: modeling and analysis of UAS security, developing algorithms for high-assurance autonomy, constructing an experimental environment, and testing mitigation strategies. Why it matters: The partnership enhances the UAE's capabilities in securing critical digital systems and fosters the growth of commercial autonomous drones and robots, opening new opportunities for enterprises.
This paper presents two robotic systems developed for the MBZIRC 2020 competition, designed for autonomous wall construction. The systems utilize a UGV with 3D LiDAR for precise brick pose estimation and a UAV employing real-time visual servoing. The authors report results from the competition and lab experiments, discussing lessons learned from the autonomous wall-building task. Why it matters: The work highlights advancements in mobile manipulation and autonomous robotics, with potential applications in construction and infrastructure development in the region.
KAUST's Image and Video Understanding Lab is developing machine learning algorithms for computer vision and object tracking, with applications in video content search and UAV navigation. Their algorithms can detect specific activities in videos, helping platforms detect unwanted content and deliver relevant ads. The object tracking algorithm is also used to empower UAVs, enabling them to follow objects autonomously. Why it matters: This research enhances video content analysis and UAV capabilities, positioning KAUST as a leader in computer vision and AI applications within the region.
KAUST Ph.D. student Matthias Müller won the Best Paper/Presentation Award at the 2nd International Workshop on Computer Vision for Unmanned Aerial Vehicles for his paper on teaching UAVs to navigate a racecourse autonomously. The paper, "Teaching UAVs to Race: End to End Regression of Agile Controls in Simulation," details research on training a deep neural network to predict UAV controls from raw image data. The research uses imitation learning with data augmentation to allow for correction of navigation mistakes, outperforming state-of-the-art methods. Why it matters: This award recognizes KAUST's contributions to computer vision and autonomous drone navigation, important areas for future applications in logistics, surveillance, and environmental monitoring in the region.
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.