連続ビジュアルサーボによる高速でロバストなペグ挿入
Fast robust peg-in-hole insertion with continuous visual servoing
複数カメラと合成データで学習した深層学習によるペグと穴の位置推定を用い、システム校正や把持の不確実性にロバストなビジュアルサーボ法を提案し、従来法より高速なペグ挿入を実現した。
著者: Rasmus Laurvig Haugaard, Jeppe Langaa, Christoffer Sloth, Anders Glent Buch
分類: cs.RO
原文アブストラクト
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.
