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020 _a9783030356798
_9978-3-030-35679-8
024 7 _a10.1007/978-3-030-35679-8
_2doi
050 4 _aTK5101-5105.9
072 7 _aTJK
_2bicssc
072 7 _aTEC041000
_2bisacsh
072 7 _aTJK
_2thema
082 0 4 _a621.382
_223
245 1 0 _aAdvances on Robotic Item Picking
_h[electronic resource] :
_bApplications in Warehousing & E-Commerce Fulfillment /
_cedited by Albert Causo, Joseph Durham, Kris Hauser, Kei Okada, Alberto Rodriguez.
250 _a1st ed. 2020.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2020.
300 _aVIII, 152 p. 84 illus., 74 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aIntroduction -- The challenges of automated item picking: the last mile of logistics for e-commerce -- Robotic Sensing for Item Picking -- Gripper Design and Grasping Strategies -- Machine Learning for Item Identification and Pose Estimation -- Machine Learning for Motion Planning -- Efficient Task Planning Strategies.
520 _aThis book is a compilation of advanced research and applications on robotic item picking and warehouse automation for e-commerce applications. The works in this book are based on results that came out of the Amazon Robotics Challenge from 2015-2017, which focused on fully automated item picking in warehouse setting, a topic that has been assumed too complicated to solve or has been reduced to a more tractable form of bin picking or single-item table top picking. The book’s contributions reveal some of the top solutions presented from the 50 participant teams. Each solution works to address the time-constraint, accuracy, complexity, and other difficulties that come with warehouse item picking. The book covers topics such as grasping and gripper design, vision and other forms of sensing, actuation and robot design, motion planning, optimization, machine learning and artificial intelligence, software engineering, and system integration, among others. Through this book, the authors describe how robot systems are built from the ground up to do a specific task, in this case, item picking in a warehouse setting. The compiled works come from the best robotics research institutions and companies globally. Presents an inside look at the various solutions for automated warehouse item picking based on the Amazon Robotics Challenge (ARC) Contains details of the challenges and solutions involved in automating item picking Provides details and insights on the solutions of the winning teams Includes chapters written by scientists and engineers at the forefront of robotics research.
650 0 _aTelecommunication.
_910437
650 0 _aControl engineering.
_931970
650 0 _aRobotics.
_92393
650 0 _aAutomation.
_92392
650 0 _aArtificial intelligence.
_93407
650 0 _aComputational intelligence.
_97716
650 0 _aComputer simulation.
_95106
650 1 4 _aCommunications Engineering, Networks.
_931570
650 2 4 _aControl, Robotics, Automation.
_931971
650 2 4 _aArtificial Intelligence.
_93407
650 2 4 _aComputational Intelligence.
_97716
650 2 4 _aControl and Systems Theory.
_931972
650 2 4 _aComputer Modelling.
_935238
700 1 _aCauso, Albert.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_935239
700 1 _aDurham, Joseph.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_935240
700 1 _aHauser, Kris.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_935241
700 1 _aOkada, Kei.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_935242
700 1 _aRodriguez, Alberto.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_935243
710 2 _aSpringerLink (Online service)
_935244
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030356781
776 0 8 _iPrinted edition:
_z9783030356804
776 0 8 _iPrinted edition:
_z9783030356811
856 4 0 _uhttps://doi.org/10.1007/978-3-030-35679-8
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
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