Robots that solve complex tasks in environments too dangerous for humans to enter are desperately needed, e.g., for search and rescue applications. We describe our mobile manipulation robot Momaro, with which we participated successfully in the DARPA Robotics Challenge. It features a unique locomotion design with four legs ending in steerable wheels, which allows it both to drive omnidirectionally and to step over obstacles or climb. Furthermore, we present advanced communication and teleoperation approaches, which include immersive three-dimensional (3D) visualization, and 6D tracking of operator head and arm motions. The proposed system is evaluated in the DARPA Robotics Challenge, the DLR SpaceBot Cup Qualification, and lab experiments. We also discuss the lessons learned from the competitions. C 2016 Wiley Periodicals, Inc.

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Micro aerial vehicles, such as multirotors, are particularly well suited for the autonomous monitoring, inspection, and surveillance of buildings, e.g., for maintenance or disaster management. Key prerequisites for the fully autonomous operation of micro aerial vehicles are real-time obstacle detection and planning of collision-free trajectories. In this article, we propose a complete system with a multimodal sensor setup for omnidirectional obstacle perception consisting of a three-dimensional (3D) laser scanner, two stereo camera pairs, and ultrasonic distance sensors. Detected obstacles are aggregated in egocentric local multiresolution grid maps. Local maps are efficiently merged in order to simultaneously build global maps of the environment and localize in these. For autonomous navigation, we generate trajectories in a multilayered approach: from mission planning over global and local trajectory planning to reactive obstacle avoidance. We evaluate our approach and the involved components in simulation and with the real autonomous micro aerial vehicle. Finally, we present the results of a complete mission for autonomously mapping a building and its surroundings. C 2015 Wiley Periodicals, Inc.

The Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2017 has defined ambitious new benchmarks to advance the state‐of‐the‐art in autonomous operation of ground‐based and flying robots. This study covers our approaches to solve the two challenges that involved micro aerial vehicles (MAV). Challenge 1 required reliable target perception, fast trajectory planning, and stable control of an MAV to land on a moving vehicle. Challenge 3 demanded a team of MAVs to perform a search and transportation task, coined “Treasure Hunt,” which required mission planning and multirobot coordination as well as adaptive control to account for the additional object weight. We describe our base MAV setup and the challenge‐specific extensions, cover the camera‐based perception, explain control and trajectory‐planning in detail, and elaborate on mission planning and team coordination. We evaluated our systems in simulation as well as with real‐robot experiments during the competition in Abu Dhabi. With our system, we—as part of the larger team NimbRo—won the MBZIRC Grand Challenge and achieved a third place in both subchallenges involving flying robots.

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