Recent advances in automatic alignment system for the National Ignition Facility

Author(s):  
Karl Wilhelmsen ◽  
Abdul A. S. Awwal ◽  
Dan Kalantar ◽  
Richard Leach ◽  
Roger Lowe-Webb ◽  
...  
2012 ◽  
Vol 87 (12) ◽  
pp. 1989-1993 ◽  
Author(s):  
K. Wilhelmsen ◽  
A. Awwal ◽  
G. Brunton ◽  
S. Burkhart ◽  
D. McGuigan ◽  
...  

2016 ◽  
Vol 87 (11) ◽  
pp. 11D813 ◽  
Author(s):  
N. Gharibyan ◽  
D. A. Shaughnessy ◽  
K. J. Moody ◽  
P. M. Grant ◽  
J. D. Despotopulos ◽  
...  

2009 ◽  
Vol 36 (9) ◽  
pp. 2341-2345
Author(s):  
刘小勤 Liu Xiaoqin ◽  
吴毅 Wu Yi ◽  
胡顺星 Hu Shunxin ◽  
汪建业 Wang Jianye ◽  
翁宁泉 Weng Ningquan

2013 ◽  
Vol 40 (10) ◽  
pp. 1002003 ◽  
Author(s):  
李红 Li Hong ◽  
王东方 Wang Dongfang ◽  
邹伟 Zou Wei ◽  
林强 Lin Qiang ◽  
张艳丽 Zhang Yanli ◽  
...  

2014 ◽  
Vol 625 ◽  
pp. 627-632
Author(s):  
Chi Ying Lin ◽  
Yu Sheng Zeng

Over the past few decades, vision based alignment has been accepted as an important technique to achieve higher economic benefits for precision manufacturing and measurement applications. Also referred to as visual servoing, this technique basically applies the vision feedback information and drives the moving parts to the desired target location using some appropriate control laws. Although recently rapid development of advanced image processing algorithms and hardware have made this alignment process an easier task, some fundamental issues including inevitable system constraints and singularities, still remain as a challenging research topic for further investigation. This paper aims to develop a visual servoing method for automatic alignment system using model predictive control (MPC). The reason for using this optimal control for visual servoing design is because of its capability of handling constraints such as motor and image constraints in precision alignment systems. In particular, a microassembly system for peg and hole alignment application is adopted to illustrate the design process. The goal is to perform visual tracking of two image feature points based on a XYθ motor-stage system. From the viewpoint of MPC, this is an optimization problem that minimizes feature errors under given constraints. Therefore, a dynamic model consisting of camera parameters and motion stage dynamics is first derived to build the prediction model and set up the cost function. At each sample step the control command is obtained by solving a quadratic programming optimization problem. Finally, simulation results with comparison to a conventional image based visual servoing method demonstrate the effectiveness and potential use of this method.


1990 ◽  
Author(s):  
Rao Yun-jiang ◽  
Huang Shang -lian ◽  
Li Ping ◽  
Wen Yu-mei ◽  
Tang Jun

2015 ◽  
Vol 19 (4) ◽  
pp. 151-154 ◽  
Author(s):  
Tomohiro Kaneda ◽  
Yasue Mitsukura ◽  
Nozomu Hamada

2013 ◽  
Vol 40 (9) ◽  
pp. 0916002
Author(s):  
李耀 Li Yao ◽  
王丁 Wang Ding ◽  
郭晓杨 Guo Xiaoyang ◽  
冷雨欣 Leng Yuxin

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