Overview of Computer Vision Techniques in Robotized Wire Harness Assembly: Current State and Future Opportunities
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 5 of its 32 references that are in the map. First posted on arXiv 24 Sep 2023.
Builds on
5 of 32 references.
References · 5
- 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
- 2015Learning Deep Features for Discriminative Localizationcited by 2,436
- 2020Image Segmentation Using Deep Learning: A Surveycited by 660
- 2019Deep Learning for 3D Point Clouds: A Surveycited by 558
- 2019Object Detection in 20 Years: A Surveycited by 531
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