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The Next Step in Robot Commissioning: Autonomous Picking and Palletizing
Örebro University, School of Science and Technology. (Centre for Applied Autonomous Sensor Systems ( AASS ))
Örebro University, School of Science and Technology. (Centre for Applied Autonomous Sensor Systems ( AASS ))ORCID iD: 0000-0002-6013-4874
University of Pisa, Pisa, Italy. (Interdepart. Research Center “E. Piaggio”)
Örebro University, School of Science and Technology. (Centre for Applied Autonomous Sensor Systems ( AASS ))ORCID iD: 0000-0002-2953-1564
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2016 (English)In: IEEE Robotics and Automation Letters, ISSN 2377-3766, Vol. 1, no 1, 546-553 p.Article in journal (Refereed) Published
Abstract [en]

So far, autonomous order picking (commissioning) systems have not been able to meet the stringent demands regarding speed, safety, and accuracy of real-world warehouse automation, resulting in reliance on human workers. In this letter, we target the next step in autonomous robot commissioning: automatizing the currently manual order picking procedure. To this end, we investigate the use case of autonomous picking and palletizing with a dedicated research platform and discuss lessons learned during testing in simplified warehouse settings. The main theoretical contribution is a novel grasp representation scheme which allows for redundancy in the gripper pose placement. This redundancy is exploited by a local, prioritized kinematic controller which generates reactive manipulator motions on-the-fly. We validated our grasping approach by means of a large set of experiments, which yielded an average grasp acquisition time of 23.5 s at a success rate of 94.7%. Our system is able to autonomously carry out simple order picking tasks in a humansafe manner, and as such serves as an initial step toward future commercial-scale in-house logistics automation solutions.

Place, publisher, year, edition, pages
Piscataway, USA: Institute of Electrical and Electronics Engineers (IEEE), 2016. Vol. 1, no 1, 546-553 p.
Keyword [en]
Logistics, grasping, autonomous vehicle navigation, robot safety, mobile manipulation
National Category
Computer Science Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:oru:diva-53370DOI: 10.1109/LRA.2016.2519944Scopus ID: 2-s2.0-84981762372OAI: oai:DiVA.org:oru-53370DiVA: diva2:1044259
Available from: 2016-11-02 Created: 2016-11-02 Last updated: 2017-10-18Bibliographically approved

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Krug, RobertStoyanov, TodorAndreasson, HenrikMosberger, RafaelLilienthal, Achim J.
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