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Topology Prediction of Branched Deformable Linear Objects Using Deep Learning
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AI
DOI：10.1109/ACCESS.2024.3518634.png)
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Abstract
En 中文
Automated wire harness handling can improve production efficiency, increase quality, and reduce assembly costs. However, due to deformation, there are an infinite number of possible wire harness configurations, making wire harness perception a challenge. Deep learning is a popular method for computer vision but lacks datasets, models, and experiments for wire harness perception. Therefore, this paper presents a novel deep learning model to predict the configuration of a wire harness using artificially generated datasets mixed with real annotated data. The model predicts keypoints which are interpolated as cubic splines to represent the wire harness configuration with reduced degrees of freedom. We benchmark our novel model against YOLOv8-Pose and experiment with different possibilities for predicting the wire harness. As a result, our proposed approach achieves mAP@50-95 of 89.8%, which could further be integrated into robotic systems to improve the automation and precision of robotic wire harness handling.
Keywords:
Wire
Splines (mathematics)
Predictive models
Image segmentation
Topology
Robots
Deep learning
Computational modeling
Annotations
Data models
Machine vision
artificial intelligence
deep learning
synthetic dataset
transfer learning
branched deformable linear objects