Al-Robotics team: A cooperative multi-unmanned aerial vehicle approach for the Mohamed Bin Zayed International Robotic Challenge

The Al-Robotics team was selected as one of the 25 finalist teams out of 143 applications received to participate in the first edition of the Mohamed Bin Zayed International Robotic Challenge (MBZIRC), held in 2017. In particular, one of the competition Challenges offered us the opportunity to develop a cooperative approach with multiple unmanned aerial vehicles (UAVs) searching, picking up, and dropping static and moving objects. This paper presents the approach that our team Al-Robotics followed to address that Challenge 3 of the MBZIRC. First, we overview the overall architecture of the system, with the different modules involved. Second, we describe the procedure that we followed to design the aerial platforms, as well as all their onboard components. Then, we explain the techniques that we used to develop the software functionalities of the system. Finally, we discuss our experimental results and the lessons that we learned before and during the competition. The cooperative approach was validated with fully autonomous missions in experiments previous to the actual competition. We also analyze the results that we obtained during the competition trials.Unión Europea H2020 73166

Mapping beyond what you can see: Predicting the layout of rooms behind closed doors

The availability of maps of indoor environments is often fundamental for autonomous mobile robots to efficiently operate in industrial, office, and domestic applications. When robots build such maps, some areas of interest could be inaccessible, for instance, due to closed doors. As a consequence, these areas are not represented in the maps, possibly causing limitations in robot localization and navigation. In this paper, we provide a method that completes 2D grid maps by adding the predicted layout of the rooms behind closed doors. The main idea of our approach is to exploit the underlying geometrical structure of indoor environments to estimate the shape of unobserved rooms. Results show that our method is accurate in completing maps also when large portions of environments cannot be accessed by the robot during map building. We experimentally validate the quality of the completed maps by using them to perform path planning tasks.(c) 2022 Elsevier B.V. All rights reserved