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arXiv · 2011.06399

Fast robust peg-in-hole insertion with continuous visual servoing

Rasmus Laurvig Haugaard·Jeppe Langaa·Christoffer Sloth·Anders Glent Buch
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Abstract

This paper demonstrates a visual servoing method which is robust towards uncertainties related to system calibration and grasping, while significantly reducing the peg-in-hole time compared to classical methods and recent attempts based on deep learning. The proposed visual servoing method is based on peg and hole point estimates from a deep neural network in a multi-cam setup, where the model is trained on purely synthetic data. Empirical results show that the learnt model generalizes to the real world, allowing for higher success rates and lower cycle times than existing approaches.

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Rasmus Laurvig Haugaard, Jeppe Langaa, Christoffer Sloth, Anders Glent Buch. 2020-11-12. Fast robust peg-in-hole insertion with continuous visual servoing. https://arxiv.org/abs/2011.06399

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