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.
This paper introduces a decentralized multi-agent decision-making framework for search and action problems under time constraints, treating time as a budgeted resource where actions have costs and rewards. The approach uses probabilistic reasoning to optimize decisions, maximizing reward within the given time. Evaluated in a simulated search, pick, and place scenario inspired by the Mohamed Bin Zayed International Robotics Challenge (MBZIRC), the algorithm outperformed benchmark strategies. Why it matters: The framework's validation in a Gazebo environment signals potential for real-world robotic applications, particularly in time-sensitive and cooperative tasks within the robotics domain in the UAE.
This paper presents a UAV-UGV team designed for autonomous firefighting, developed for the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2020. The system uses LiDAR for localization in GNSS-restricted environments and fuses LiDAR and thermal camera data to track fires. Relative navigation enables successful fire extinguishing. Why it matters: This research demonstrates the potential of robotic systems in autonomous firefighting, addressing challenges in dangerous and inaccessible environments, and advancing robotics research within the UAE.
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.