Abstract
This chapter presents a multi-robot system to manufacture personalized product for medical purpose. This is a modularized system with three components: a personalized module, a bimanual module, and a vision module. The personalized module is designed to accommodate for different patients’ anatomy structure, while the bimanual module performs an intricate sewing task. All the robots are coordinated via the vision module, which tracks and guides their motions inreal time. Experiments show that this system can adapt to different personalized designs and achieve good accuracy and robustness. Therefore, this system can be extended to similar manipulation tasks, especially for flexible production, where multi-robot cooperation is required.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
S.A. Baert, M.A. Viergever, W.J. Niessen, Guide-wire tracking during endovascular interventions. IEEE Trans. Med. Imaging 22(8), 965–972 (2003)
D.J. Berndt, J. Clifford, Using dynamic time warping to find patterns in time series, in KDD Workshop, vol. 10, (Seattle, 1994), pp. 359–370
S. Calinon, F. Guenter, A. Billard, On learning, representing, and generalizing a task in a humanoid robot. IEEE Trans. Syst. Man Cybern. B Cybern. 37(2), 286–298 (2007)
CDC, Deaths, percent of total deaths, and death rates for the 15 leading causes of death in 5-year age groups, by race, and sex: United states. Ctr. Dis. Control Prev. (2013). www.cdc.gov/nchs/data/dvs/lcwk1_2013.pdf
S. Garrido-Jurado, R.M. Noz Salinas, F. Madrid-Cuevas, M. MarĂn-JimĂ©nez, Automatic generation and detection of highly reliable fiducial markers under occlusion. Pattern Recogn. 47(6), 2280–2292 (2014). https://doi.org/10.1016/j.patcog.2014.01.005. http://www.sciencedirect.com/science/article/pii/S0031320314000235
B. Huang, S. El-Khoury, M. Li, J.J. Bryson, A. Billard, Learning a real time grasping strategy, in 2013 IEEE International Conference on Robotics and Automation (ICRA), (2013), pp. 593–600. https://doi.org/10.1109/ICRA.2013.6630634
B. Huang, A. Vandini, Y. Hu, S.L. Lee, G.Z. Yang, A vision-guided dual arm sewing system for stent graft manufacturing, in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (IEEE, 2016), pp. 751–758
B. Huang, M. Ye, Y. Hu, A. Vandini, S.L. Lee, G.Z. Yang, A multirobot cooperation framework for sewing personalized stent grafts. IEEE Transactions on Industrial Informatics 14(4), 1776–1785 (2017a)
B. Huang, M. Ye, S.L. Lee, G.Z. Yang, A vision-guided multi-robot cooperation framework for learning-by-demonstration and task reproduction, in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (IEEE, 2017b)
S. Hutchinson, G.D. Hager, P.I. Corke, A tutorial on visual servo control. IEEE Trans. Robot. Autom. 12(5), 651–670 (1996)
S. Iyer, T. Looi, J. Drake, A single arm, single camera system for automated suturing, in 2013 IEEE International Conference on Robotics and Automation (ICRA), (IEEE, 2013), pp. 239–244
Z. Kalal, K. Mikolajczyk, J. Matas, Forward-backward error: Automatic detection of tracking failures, in 2010 20th International Conference on Pattern Recognition (ICPR), (IEEE, 2010), pp. 2756–2759
P. Koustoumpardis, N. Aspragathos, P. Zacharia, Intelligent Robotic Handling of Fabrics Towards Sewing (INTECH Open Access Publisher, 2006)
M. Kudo, Y. Nasu, K. Mitobe, B. Borovac, Multi-arm robot control system for manipulation of flexible materials in sewing operation. Mechatronics 10(3), 371–402 (2000)
V. Lepetit, F. Moreno-Noguer, P. Fua, Epnp: An accurate o (n) solution to the pnp problem. Int. J. Comput. Vis. 81(2), 155 (2009)
Y. Liao, F. Deschamps, E.F.R. Loures, L.F.P. Ramos, Past, present and future of industry 4.0-a systematic literature review and research agenda proposal. Int. J. Prod. Res. 55(12), 3609–3629 (2017)
D. Lo, P.R. Mendonça, A. Hopper, et al., Trip: A low-cost vision-based location system for ubiquitous computing. Pers. Ubiquit. Comput. 6(3), 206–219 (2002)
B. Montreuil, M. Poulin, Demand and supply network design scope for personalized manufacturing. Prod. Plann. Control 16(5), 454–469 (2005)
Z. Pan, J. Polden, N. Larkin, S. Van Duin, J. Norrish, Recent progress on programming methods for industrial robots. Robot. Comput.- Integr. Manuf. 28(2), 87–94 (2012)
L. PĂ©rez, ĂŤ. RodrĂguez, N. RodrĂguez, R. Usamentiaga, D.F. GarcĂa, Robot guidance using machine vision techniques in industrial environments: A comparative review. Sensors 16(3), 335 (2016)
T. Resch, Custom-made devices: Current state of the art. Endovascular Today. (2016). http://evtoday.com/2016/03/custom-made-devices-current-state-of-the-art/
M. RĂĽĂźmann, M. Lorenz, P. Gerbert, M. Waldner, J. Justus, P. Engel, M. Harnisch, Industry 4.0: The Future of Productivity and Growth in Manufacturing Industries (Boston Consulting Group, Boston, 2015), p. 14
J. Schrimpf, L.E. Wetterwald, Experiments towards automated sewing with a multi-robot system, in 2012 IEEE International Conference on Robotics and Automation (ICRA), (IEEE, 2012), pp. 5258–5263
J. Schrimpf, M. Bjerkeng, G. Mathisen, Velocity coordination and corner matching in a multi-robot sewing cell, in 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), (IEEE, 2014), pp. 4476–4481
C. Staub, T. Osa, A. Knoll, R. Bauernschmitt, Automation of tissue piercing using circular needles and vision guidance for computer aided laparoscopic surgery, in 2010 IEEE International Conference on Robotics and Automation (ICRA), (IEEE, 2010), pp. 4585–4590
J. Van Den Berg, S. Miller, D. Duckworth, H. Hu, A. Wan, X.Y. Fu, K. Goldberg, P. Abbeel, Superhuman performance of surgical tasks by robots using iterative learning from human-guided demonstrations, in 2010 IEEE International Conference on Robotics and Automation (ICRA), (IEEE, 2010), pp. 2074–2081
S. Wang, J. Wan, D. Li, C. Zhang, Implementing smart factory of Industrie 4.0: An outlook. Int. J. Distrib. Sens. Netw. (2016a)
S. Wang, J. Wan, D. Zhang, D. Li, C. Zhang, Towards smart factory for industry 4.0: A self-organized multi-agent system with big data based feedback and coordination. Comput. Netw. 101, 158–168 (2016b)
M. Ye, L. Zhang, S. Giannarou, G.Z. Yang, Realtime 3d tracking of articulated tools for robotic surgery, in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer, 2016), pp. 386–394
Z. Zhang, A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1330–1334 (2000)
L. Zhang, M. Ye, P.L. Chan, G.Z. Yang, Real-time surgical tool tracking and pose estimation using a hybrid cylindrical marker. Int. J. Comput. Assist. Radiol. Surg. 12(6), 921–930 (2017). https://doi.org/10.1007/s11548-017-1558-9
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Section Editor information
Rights and permissions
Copyright information
© 2022 Springer Nature Singapore Pte Ltd
About this entry
Cite this entry
Huang, B., Tsai, YY., Yang, GZ. (2022). A Real-Time Robotic System for Sewing Personalized Stent Grafts. In: Tian, YC., Levy, D.C. (eds) Handbook of Real-Time Computing. Springer, Singapore. https://doi.org/10.1007/978-981-287-251-7_50
Download citation
DOI: https://doi.org/10.1007/978-981-287-251-7_50
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-287-250-0
Online ISBN: 978-981-287-251-7
eBook Packages: EngineeringReference Module Computer Science and Engineering