應用視圖之經驗轉移於機器手臂夾取隨機堆疊物件
Other Title
Robot Bin Picking Using View-Based Experience Transfer
Journal
機械工業雜誌
Journal Issue
461
Start Page
39
End Page
51
ISSN
0255-0075
Date Issued
2021-08
Author(s)
Abstract
本文提出了一種新的夾取演算流程，可供機器手臂在隨機堆疊的物件中夾取目標物。首先，本文使用實例切割和基於視圖的經驗轉移演算法來獲得候選夾取點；隨後，利用避免碰撞和可進行穩定性分析的演算法，篩選出最佳夾取點，以供機器手臂夾取。基於視圖的經驗轉移策略是先找到物體的姿態，接著再將夾取經驗轉移到切割出的圖像中以獲得夾取點。本文並建立一機器手臂系統進行評估，實驗裝置包含一台六軸機器手臂，一個二指平行夾爪和一台Kinect V2 RGB-D相機。實驗結果顯示，本文所提出的方法可以推廣到具有複雜形狀的物體，並且能夠在密集堆疊的箱中夾取不同類別的物體。
In this study, a novel pipeline for grasp detection in robot bin picking was proposed. Firstly, a method combining instance segmentation and view-based experience transfer was developed. Subsequently, collision avoidance and stability analysis to determine the optimal grasp for robot grasping were performed. The approach for the view-based experience transfer was to acquire the object view and then transfer the grasp experience onto the clutter scenario. A robot system consisting of a six-axis robot arm with a two-jaw parallel gripper and a Kinect V2 RGB-D camera was established to validate the approach. The experimental results show that our proposed approach can be implemented to grasp objects with complex shape as well as grasp different types of objects in a bin.
Subjects
實例切割
夾取經驗轉移
堆疊夾取
Instance segmentation
Grasp experience transfer
Random bin picking
Type
journal article