Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
169 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Overview of Computer Vision Techniques in Robotized Wire Harness Assembly: Current State and Future Opportunities (2309.13745v3)

Published 24 Sep 2023 in cs.RO, cs.AI, and cs.CV

Abstract: Wire harnesses are essential hardware for electronic systems in modern automotive vehicles. With a shift in the automotive industry towards electrification and autonomous driving, more and more automotive electronics are responsible for energy transmission and safety-critical functions such as maneuvering, driver assistance, and safety system. This paradigm shift places more demand on automotive wire harnesses from the safety perspective and stresses the greater importance of high-quality wire harness assembly in vehicles. However, most of the current operations of wire harness assembly are still performed manually by skilled workers, and some of the manual processes are problematic in terms of quality control and ergonomics. There is also a persistent demand in the industry to increase competitiveness and gain market share. Hence, assuring assembly quality while improving ergonomics and optimizing labor costs is desired. Robotized assembly, accomplished by robots or in human-robot collaboration, is a key enabler for fulfilling the increasingly demanding quality and safety as it enables more replicable, transparent, and comprehensible processes than completely manual operations. However, robotized assembly of wire harnesses is challenging in practical environments due to the flexibility of the deformable objects, though many preliminary automation solutions have been proposed under simplified industrial configurations. Previous research efforts have proposed the use of computer vision technology to facilitate robotized automation of wire harness assembly, enabling the robots to better perceive and manipulate the flexible wire harness. This article presents an overview of computer vision technology proposed for robotized wire harness assembly and derives research gaps that require further study to facilitate a more practical robotized assembly of wire harnesses.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (33)
  1. Economic comparison of wire harness assembly systems. Journal of Manufacturing Systems 13, 276–288. doi:10.1016/0278-6125(94)90035-3.
  2. Vision-force guided monitoring for mating connectors in wiring harness assembly systems. Journal of Robotics and Mechatronics 24, 666–676. doi:10.20965/jrm.2012.p0666.
  3. Hybrid vision-force guided fault tolerant robotic assembly for electric connectors, in: 2009 International Symposium on Micro-NanoMechatronics and Human Science, pp. 86–91. doi:10.1109/MHS.2009.5352078.
  4. Visual recognition method for deformable wires in aircrafts assembly based on sequential segmentation and probabilisitic estimation, in: 2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC), pp. 598–603. doi:10.1109/ITOEC53115.2022.9734432.
  5. Deep learning for 3d point clouds: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 43, 4338–4364. doi:10.1109/TPAMI.2020.3005434.
  6. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 doi:10.48550/arXiv.1704.04861.
  7. Robotized assembly of a wire harness in car production line, in: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 490–495. doi:10.1109/IROS.2010.5653133.
  8. Robotized assembly of a wire harness in a car production line. Advanced Robotics 25, 473–489. URL: https://doi.org/10.1163/016918610X551782, doi:10.1163/016918610X551782, arXiv:https://doi.org/10.1163/016918610X551782.
  9. Robotized recognition of a wire harness utilizing tracing operation. Robotics and Computer-Integrated Manufacturing 34, 52–61. URL: https://www.sciencedirect.com/science/article/pii/S0736584514001069, doi:10.1016/j.rcim.2014.12.002.
  10. Artoolkit. URL: http://www.hitl.washington.edu/artoolkit.html. accessed: 2023-01-08.
  11. Marker tracking and hmd calibration for a video-based augmented reality conferencing system, in: Proceedings 2nd IEEE and ACM International Workshop on Augmented Reality (IWAR’99), pp. 85–94. doi:10.1109/IWAR.1999.803809.
  12. Tell me, what do you see?—interpretable classification of wiring harness branches with deep neural networks. Sensors 21. URL: https://www.mdpi.com/1424-8220/21/13/4327, doi:10.3390/s21134327.
  13. Development of a robot car wiring system, in: 2008 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 862–867. doi:10.1109/AIM.2008.4601774.
  14. Deep learning. nature 521, 436–444. doi:10.1038/nature14539.
  15. Object recognition from local scale-invariant features, in: Proceedings of the Seventh IEEE International Conference on Computer Vision, pp. 1150–1157 vol.2. doi:10.1109/ICCV.1999.790410.
  16. Distinctive image features from scale-invariant keypoints. International journal of computer vision 60, 91–110. doi:10.1023/B:VISI.0000029664.99615.94.
  17. Dynamic modeling and control of deformable linear objects for single-arm and dual-arm robot manipulations. IEEE Transactions on Robotics 38, 2341–2353. doi:10.1109/TRO.2021.3139838.
  18. Image segmentation using deep learning: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 3523–3542. doi:10.1109/TPAMI.2021.3059968.
  19. A novel vision-based method for 3d profile extraction of wire harness in robotized assembly process. Journal of Manufacturing Systems 61, 365–374. URL: https://www.sciencedirect.com/science/article/pii/S0278612521002089, doi:10.1016/j.jmsy.2021.10.003.
  20. Gaussian mixture models. Encyclopedia of biometrics 741. doi:10.1007/978-1-4899-7488-4_196.
  21. Erfnet: Efficient residual factorized convnet for real-time semantic segmentation. IEEE Transactions on Intelligent Transportation Systems 19, 263–272. doi:10.1109/TITS.2017.2750080.
  22. Motion planning for robotic manipulation of deformable linear objects, in: Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006., pp. 2478–2484. doi:10.1109/ROBOT.2006.1642074.
  23. Robotic manipulation and sensing of deformable objects in domestic and industrial applications: a survey. The International Journal of Robotics Research 37, 688–716. doi:10.1177/0278364918779698.
  24. Tracking deformable objects with point clouds, in: 2013 IEEE International Conference on Robotics and Automation, pp. 1130–1137. doi:10.1109/ICRA.2013.6630714.
  25. Electric connector assembly based on vision and impedance control using cable connector-feeding system. Journal of Mechanical Science and Technology 31, 5997–6003. doi:10.1007/s12206-017-1144-7.
  26. Robotic wiring harness assembly system for fault-tolerant electric connectors mating, in: 2010 International Symposium on Micro-NanoMechatronics and Human Science, pp. 202–205. doi:10.1109/MHS.2010.5669533.
  27. High-speed manipulation of cable connector using a high-speed robot hand, in: 2013 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 1598–1604. doi:10.1109/ROBIO.2013.6739695.
  28. A framework for manipulating deformable linear objects by coherent point drift. IEEE Robotics and Automation Letters 3, 3426–3433. doi:10.1109/LRA.2018.2852770.
  29. The effect of learning factors due to low volume order fluctuations in the automotive wiring harness production. Procedia CIRP 19, 129–134. doi:10.1016/j.procir.2014.05.019.
  30. Preliminary connector recognition system based on image processing for wire harness assembly tasks, in: 2020 20th International Conference on Control, Automation and Systems (ICCAS), pp. 1146–1150. doi:10.23919/ICCAS50221.2020.9268291.
  31. Learning deep features for discriminative localization, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2921–2929. doi:10.1109/CVPR.2016.319.
  32. A practical solution to deformable linear object manipulation: A case study on cable harness connection, in: 2020 5th International Conference on Advanced Robotics and Mechatronics (ICARM), pp. 329–333. doi:10.1109/ICARM49381.2020.9195380.
  33. Object detection in 20 years: A survey. Proceedings of the IEEE 111, 257–276. doi:10.1109/JPROC.2023.3238524.
Citations (3)

Summary

We haven't generated a summary for this paper yet.