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. In this article, we describe our winning entry to MBZIRC Challenge 2: the mobile manipulation robot Mario. It is capable of autonomously solving a valve manipulation task using a wrench tool detected, grasped, and finally employed to turn a valve stem. Mario's omnidirectional base allows both fast locomotion and precise close approach to the manipulation panel. We describe an efficient detector for medium-sized objects in 3D laser scans and apply it to detect the manipulation panel. An object detection architecture based on deep neural networks is used to find and select the correct tool from grayscale images. Parametrized motion primitives are adapted online to percepts of the tool and valve stem in order to turn the stem. We report in detail on our winning performance at the challenge and discuss lessons learned.
@article{arxiv.1810.02997,
title = {Team NimbRo at MBZIRC 2017: Autonomous Valve Stem Turning using a Wrench},
author = {Max Schwarz and David Droeschel and Christian Lenz and Arul Selvam Periyasamy and En Yen Puang and Jan Razlaw and Diego Rodriguez and Sebastian Schüller and Michael Schreiber and Sven Behnke},
journal= {arXiv preprint arXiv:1810.02997},
year = {2018}
}
Comments
Accepted for Journal of Field Robotics (JFR), Wiley, to appear 2018