The ETH‐MAV Team in the MBZ International Robotics Challenge

DOI Web Site Web Site 被引用文献2件
  • Rik Bähnemann
    Autonomous Systems Lab (ASL) ETH Zurich—Swiss Federal Institute of Technology Zurich Switzerland
  • Michael Pantic
    Autonomous Systems Lab (ASL) ETH Zurich—Swiss Federal Institute of Technology Zurich Switzerland
  • Marija Popović
    Autonomous Systems Lab (ASL) ETH Zurich—Swiss Federal Institute of Technology Zurich Switzerland
  • Dominik Schindler
    Autonomous Systems Lab (ASL) ETH Zurich—Swiss Federal Institute of Technology Zurich Switzerland
  • Marco Tranzatto
    Autonomous Systems Lab (ASL) ETH Zurich—Swiss Federal Institute of Technology Zurich Switzerland
  • Mina Kamel
    Autonomous Systems Lab (ASL) ETH Zurich—Swiss Federal Institute of Technology Zurich Switzerland
  • Marius Grimm
    Autonomous Systems Lab (ASL) ETH Zurich—Swiss Federal Institute of Technology Zurich Switzerland
  • Jakob Widauer
    Autonomous Systems Lab (ASL) ETH Zurich—Swiss Federal Institute of Technology Zurich Switzerland
  • Roland Siegwart
    Autonomous Systems Lab (ASL) ETH Zurich—Swiss Federal Institute of Technology Zurich Switzerland
  • Juan Nieto
    Autonomous Systems Lab (ASL) ETH Zurich—Swiss Federal Institute of Technology Zurich Switzerland

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<jats:title>Abstract</jats:title><jats:p>This study describes the hardware and software systems of the Micro Aerial Vehicle (MAV) platforms used by the ETH Zurich team in the 2017 Mohamed Bin Zayed International Robotics Challenge (MBZIRC). The aim was to develop robust outdoor platforms with the autonomous capabilities required for the competition, by applying and integrating knowledge from various fields, including computer vision, sensor fusion, optimal control, and probabilistic robotics. This paper presents the major components and structures of the system architectures and reports on experimental findings for the MAV‐based challenges in the competition. Main highlights include securing the second place both in the individual search, pick, and place the task of Challenge 3 and the Grand Challenge, with autonomous landing executed in less than 1 min and a visual servoing success rate of over <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="graphic/rob21824-math-0001.png" xlink:title="urn:x-wiley:15564959:media:rob21824:rob21824-math-0001"/> for object pickups.</jats:p>

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