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Jan Quenzel

Publications and source records attributed to Jan Quenzel.

20 records · Page 2Linked to original sources

Team NimbRo at MBZIRC 2017: Fast Landing on a Moving Target and Treasure Hunting with a Team of MAVs

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 article 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 in order 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 multi-robot 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.

cs.RO

Fast Autonomous Flight in Warehouses for Inventory Applications

The past years have shown a remarkable growth in use-cases for micro aerial vehicles (MAVs). Conceivable indoor applications require highly robust environment perception, fast reaction to changing situations, and stable navigation, but reliable sources of absolute positioning like GNSS or compass measurements are unavailable during indoor flights. We present a high-performance autonomous inventory MAV for operation inside warehouses. The MAV navigates along warehouse aisles and detects the placed stock in the shelves alongside its path with a multimodal sensor setup containing an RFID reader and two high-resolution cameras. We describe in detail the SLAM pipeline based on a 3D lidar, the setup for stock recognition, the mission planning and trajectory generation, as well as a low-level routine for avoidance of dynamical or previously unobserved obstacles. Experiments were performed in an operative warehouse of a logistics provider, in which an external warehouse management system provided the MAV with high-level inspection missions that are executed fully autonomously.

cs.RO