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Active Recognition and Manipulation for Mobile Robot Bin Picking

  • Conference paper
Gearing Up and Accelerating Cross‐fertilization between Academic and Industrial Robotics Research in Europe:

Part of the book series: Springer Tracts in Advanced Robotics ((STAR,volume 94))

Abstract

Grasping individual objects from an unordered pile in a box has been investigated in stationary scenarios so far. In this work, we present a complete system including active object perception and grasp planning for bin picking with a mobile robot. At the core of our approach is an efficient representation of objects as compounds of simple shape and contour primitives. This representation is used for both robust object perception and efficient grasp planning. For being able to manipulate previously unknown objects, we learn object models from single scans in an offline phase. During operation, objects are detected in the scene using a particularly robust probabilistic graph matching. To cope with severe occlusions we employ active perception considering not only previously unseen volume but also outcomes of primitive and object detection. The combination of shape and contour primitives makes our object perception approach particularly robust even in the presence of noise, occlusions, and missing information. For grasp planning, we efficiently pre-compute possible grasps directly on the learned object models. During operation, grasps and arm motions are planned in an efficient local multiresolution height map. All components are integrated and evaluated in a bin picking and part delivery task.

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Author information

Authors and Affiliations

  1. Computer Science Institute, University of Bonn, Bonn, Germany

    Dirk Holz, Matthias Nieuwenhuisen, David Droeschel, Jörg Stückler, Alexander Berner, Jun Li, Reinhard Klein & Sven Behnke

Authors
  1. Dirk Holz
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  2. Matthias Nieuwenhuisen
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  3. David Droeschel
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  4. Jörg Stückler
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  5. Alexander Berner
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  6. Jun Li
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  7. Reinhard Klein
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  8. Sven Behnke
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Corresponding author

Correspondence to Dirk Holz .

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Editors and Affiliations

  1. Department of Informatics VI, Technical University of Munich, Garching, Germany

    Florian Röhrbein

  2. Feup Campus, Inesc Porto, Porto, Portugal

    Germano Veiga

  3. Department of Information Engineering, Second University of Naples, Aversa, Italy

    Ciro Natale

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Holz, D. et al. (2014). Active Recognition and Manipulation for Mobile Robot Bin Picking. In: Röhrbein, F., Veiga, G., Natale, C. (eds) Gearing Up and Accelerating Cross‐fertilization between Academic and Industrial Robotics Research in Europe:. Springer Tracts in Advanced Robotics, vol 94. Springer, Cham. https://doi.org/10.1007/978-3-319-03838-4_7

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  • DOI: https://doi.org/10.1007/978-3-319-03838-4_7

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03837-7

  • Online ISBN: 978-3-319-03838-4

  • eBook Packages: EngineeringEngineering (R0)

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