モデルフリー適応制御とニューラルネットワークを用いた非校正ビジュアルサーボコントローラ
A Novel Uncalibrated Visual Servoing Controller Baesd on Model-Free Adaptive Control Method with Neural Network
ロボットアームの非校正画像ベースビジュアルサーボにおいて、モデルフリー適応制御とニューラルネットワークを組み合わせ、静止目標と移動軌跡の両方に対応する制御器を提案した。
著者: Haibin Zeng, Yueyong Lyu, Jiaming Qi, Shuangquan Zou, Tanghao Qin, Wenyu Qin
分類: cs.RO
原文アブストラクト
Nowadays, with the continuous expansion of application scenarios of robotic arms, there are more and more scenarios where nonspecialist come into contact with robotic arms. However, in terms of robotic arm visual servoing, traditional Position-based Visual Servoing (PBVS) requires a lot of calibration work, which is challenging for the nonspecialist to cope with. To cope with this situation, Uncalibrated Image-Based Visual Servoing (UIBVS) frees people from tedious calibration work. This work applied a model-free adaptive control (MFAC) method which means that the parameters of controller are updated in real time, bringing better ability of suppression changes of system and environment. An artificial intelligent neural network is applied in designs of controller and estimator for hand-eye relationship. The neural network is updated with the knowledge of the system input and output information in MFAC method. Inspired by "predictive model" and "receding-horizon" in Model Predictive Control (MPC) method and introducing similar structures into our algorithm, we realizes the uncalibrated visual servoing for both stationary targets and moving trajectories. Simulated experiments with a robotic manipulator will be carried out to validate the proposed algorithm.
