オートエンコーダとロバストな中心線抽出による弾性ロッドの潜在空間ベース操作
Towards Latent Space Based Manipulation of Elastic Rods using Autoencoder Models and Robust Centerline Extractions
深層オートエンコーダで弾性ロッドの形状を低次元表現し、自己組織化ネットワークで画像から中心線をリアルタイム抽出して2次元目標形状へ自動制御する手法を提案した。
著者: Jiaming Qi, Guangfu Ma, Peng Zhou, Haibo Zhang, Yueyong Lyu, David Navarro-Alarcon
分類: cs.RO
原文アブストラクト
The automatic shape control of deformable objects is a challenging (and currently hot) manipulation problem due to their high-dimensional geometric features and complex physical properties. In this study, a new methodology to manipulate elastic rods automatically into 2D desired shapes is presented. An efficient vision-based controller that uses a deep autoencoder network is designed to compute a compact representation of the object's infinite-dimensional shape. An online algorithm that approximates the sensorimotor mapping between the robot's configuration and the object's shape features is used to deal with the latter's (typically unknown) mechanical properties. The proposed approach computes the rod's centerline from raw visual data in real-time by introducing an adaptive algorithm on the basis of a self-organizing network. Its effectiveness is thoroughly validated with simulations and experiments.
