Getting Real About AR's Potential for Industrial Training
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Presentation given by Mark Billinghurst on August 26th 2015 about Augmented Reality and industrial training applications.
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Getting Real About AR's Potential for Industrial Training
- 1. GETTING REAL ABOUT AR Mark Billinghurst mark.billinghurst@unisa.edu.au August 26th 2015
- 2. 50 Years of Progress • From lab to living room • AR devices in every pocket
- 3. 1998 vs. 2008 CPU: 300 Mhz HDD; 9GB RAM: 512 mb Camera: VGA 30fps Graphics: 500K poly/sec 1998: SGI O2 2008: Nokia N95 CPU: 332 Mhz HDD; 8GB RAM: 128 mb Camera: VGA 30 fps Graphics: 2m poly/sec
- 4. AR Business Today • Around $600 Million USD in 2014 (>$2B 2015) • > 80% Games and Marketing applications
- 5. Market Projections cf. 2014 computer game market = $84 Billion
- 7. Getting from Here to There • New markets • Medical, Education, Industry • New types of applications • Training, Information Presentation, Collaboration • New devices/platforms • Head mounted displays, projected AR • New Interaction Techniques • Gesture based, empathic interfaces, multimodal
- 8. 2012 – Iron Man 2
- 9. AR for Industrial Training • Potential Benefits • Training on target equipment • Showing spatial information in place • Allow worker to remain focused on work site • Able to view content from any angle
- 10. Case Study 1: Vehicle Maintenance • Using AR for armoured vehicle maintence Henderson, S. J., & Feiner, S. (2009, October). Evaluating the benefits of augmented reality for task localization in maintenance of an armored personnel carrier turret. In ISMAR 2009. 8th IEEE International Symposium on (pp. 135-144). IEEE.
- 11. Current System • Electronic Manual on laptop computer • 2D images, pdf, fixed views
- 12. Interfaces • Three display conditions • AR, HUD, LCD panel • Using stereo video-see through AR HMD system AR HUD LCD
- 13. Experiment • 6 Mechanics as subjects • Subjects completed 18 common tasks • Installing fasteners, connecting cables, etc • Measured • Performance time, localisation time • Errors made • Head movement, exertion • Subjective feedback (satisfaction, intuitiveness)
- 14. Key Results • No difference in times between AR & LCD • But LCD (34.5s) faster than HUD (55.2s) • AR able to locate task areas faster • 4.9s in AR, vs 9.2s in LCD, vs. 11.1s in HUD • Fewer head motions in AR • Typically 50% less motions than other conditions • AR rated as good as LCD in satisfaction • AR rated as most intuitive of conditions
- 15. Case Study 2: Boeing & Iowa State • Work instructions presented in 3 conditions • Desktop interface • Tablet interface • Tablet AR interface T. Richardson, S. Gilbert, J. Holub, F. Thompson, A. MacAllister, R. Radkowski, E. Winer, P. Davies, and S. Terry. "Fusing Self-Reported and Sensor Data from Mixed-Reality Training." Desktop Interface Tablet AR Interface
- 16. Experiment Design • Reproduced industrial work cell • Task • Subjects completed a five-step physical assembly process • Data Collected • User motion, activity analysis • Task performance (accuracy, completion time) • Time looking at instructions, moving between work areas • Subjective feedback
- 17. Results • Users liked AR condition the best • Fewer errors in AR condition, faster performance time • AR has 30% faster performance, 90% few errors Performance Time Errors
- 18. Commercial Systems • Ngrain • http://www.ngrain.com/ • Training authoring tool • Model based AR tracking • ScopeAR • http://www.scopear.com/ • Remote assistance • Image based tracking
- 19. Ngrain
- 20. IntelligentTraining • Most AR systems stupid • Don’t recognize user behaviour • Don’t provide feedback • Don’t adapt to user • Especially important for training • Scaffolded learning • Moving beyond check-lists of actions
- 21. Intelligent Interfaces • AR interface + intelligent tutoring system • ASPIRE constraint based system (from UC) • Constraints • relevance cond., satisfaction cond., feedback Westerfield, G., Mitrovic, A., & Billinghurst, M. (2013). Intelligent Augmented Reality Training for Assembly Tasks. In Artificial Intelligence in Education (pp. 542-551). Springer Berlin Heidelberg.
- 22. Domain Ontology
- 23. Intelligent Feedback • Actively monitors user behaviour • Implicit vs. explicit interaction • Provides corrective feedback
- 25. Evaluation Results • 16 subjects, with and without ITS • Improved task completion • Improved learning
- 26. IntelligentAgents • AR characters • Virtual embodiment of system • Multimodal input/output • Examples • AR Lego,Welbo, etc • MrVirtuoso • AR character more real, more fun • On-screen 3D and AR similar in usefulness Wagner, D., Billinghurst, M., & Schmalstieg, D. (2006). How real should virtual characters be?. In Proceedings of the 2006 ACM SIGCHI international conference on Advances in computer entertainment technology (p. 57). ACM.
- 27. Conclusions • AR is becoming commonly available • In order to achieve significant growth AR needs to • Expand into new markets • Move onto new platforms • Create new types of applications • AR for Training is a particularly promising area • Spatial skills, intelligent interfaces