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.
A Carnegie Mellon team (Tartan) presented their approach to rapidly deployable and robust autonomous aerial vehicles at the 2020 Mohamed Bin Zayed International Robotics Challenge (MBZIRC). The system utilizes common techniques in vision and control, encoding robustness into mission structure through outcome monitoring and recovery strategies. Their system placed fourth in Challenge 2 and seventh in the Grand Challenge, with achievements in balloon popping, block manipulation, and autonomous firefighting. Why it matters: The work highlights strategies for building robust autonomous systems that can operate without central communication or high-precision GPS in challenging real-world environments, directly addressing key needs in the development of field robotics for the Middle East.
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.
This paper introduces ADR-VINS, a monocular visual-inertial state estimation framework based on an Error-State Kalman Filter (ESKF) designed for autonomous drone racing, integrating direct pixel reprojection errors from gate corners as innovation terms. It also introduces ADR-FGO, an offline Factor-Graph Optimization framework for generating high-fidelity reference trajectories for post-flight evaluation in GNSS-denied environments. Validated on the TII-RATM dataset, ADR-VINS achieved an average RMS translation error of 0.134 m and was successfully deployed in the A2RL Drone Championship Season 2. Why it matters: The framework provides a robust and efficient solution for drone state estimation in challenging racing environments, and enables performance evaluation without relying on external localization systems.
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.
The paper details the hardware and software systems of ETH Zurich's Micro Aerial Vehicles (MAVs) used in the 2017 Mohamed Bin Zayed International Robotics Challenge (MBZIRC). The team integrated computer vision, sensor fusion, and control to develop autonomous outdoor platforms. They achieved second place in Challenge 3 and the Grand Challenge, demonstrating autonomous landing in under a minute and a 90%+ visual servoing success rate for object pickups. Why it matters: The work highlights the advanced state of robotics research and development showcased at the MBZIRC, contributing to the growth of autonomous systems in the region.