Detection and Identification of Assembly Characteristics of Lithium-Ion Battery Modules Using RGB-D Imagery

Abstract A large diversity of product variants often leads to decision making problems during product and assembly process development. This paper presents a novel method that is used to quickly gather assembly characteristics of product variants for the enrichment of criteria for decision making methods and acceleration of assembly process planning. The collection of data is accomplished through a depth camera-based surveillance of a flexible, reconfigurable assembly workspace. For the surveillance, a robust and efficient motion tracking approach is developed theoretically and implemented practically in a software environment. The evaluation of the presented method is done by assembling different lithium-ion battery variants.

[1]  Vinayak Ashok Prabhu,et al.  Dynamic Alignment Control Using Depth Imagery for Automated Wheel Assembly , 2014 .

[2]  B Bonnechère,et al.  Validity and reliability of the Kinect within functional assessment activities: comparison with standard stereophotogrammetry. , 2014, Gait & posture.

[3]  Dimitris Kiritsis,et al.  Integrated product relationships management: a model to enable concurrent product design and assembly sequence planning , 2012 .

[4]  Michele Germani,et al.  A method to optimize assemblability of industrial product in early design phase: from product architecture to assembly sequence , 2012 .

[5]  Geoffrey Boothroyd,et al.  Product design for manufacture and assembly , 1994, Comput. Aided Des..

[6]  Giles Tewkesbury,et al.  An expert system for automatic design‐for‐assembly , 2009 .

[7]  Reiner Korthauer,et al.  Handbuch Lithium-Ionen-Batterien , 2013 .

[8]  Oliver Korn,et al.  Context-aware assistive systems for augmented work: a framework using gamification and projection , 2014 .

[9]  Claude Petitpierre,et al.  Automated Training and Maintenance through Kinect , 2012, ArXiv.

[10]  Dean Hunter Galarowicz.  An RGB-D Camera Based Online Error Correction System for Autonomous Robotic Assembly , 2014 .

[11]  Vinayak Ashok Prabhu,et al.  Monitoring and Digitising Human-Workpiece Interactions during a Manual Manufacturing Assembly Operation Using KinectTM , 2013 .

[12]  Alberto Menache.  Understanding Motion Capture for Computer Animation , 2010 .

[13]  Kiyoshi Kiyokawa,et al.  The effectiveness of an AR-based context-aware assembly support system in object assembly , 2014, 2014 IEEE Virtual Reality (VR).

[14]  Dieter Fox,et al.  DuploTrack: a real-time system for authoring and guiding duplo block assembly , 2012, UIST.

[15]  Toby Sharp,et al.  Real-time human pose recognition in parts from single depth images , 2011, CVPR.

[16]  Matthias Loskyll,et al.  Enabling virtual assembly training in and beyond the automotive industry , 2012, 2012 18th International Conference on Virtual Systems and Multimedia.