A sensor planning system for automated headlamp lens inspection

Usually, the automated systems for quality control based on computer vision have been centered on the design of algorithms for detecting different types of defects. Nevertheless, the issues related to planning suitable sensor poses for the inspection task have received less attention. In addition, the applications where a vision sensor can only sample a portion of a part from a single viewpoint, the sensor planning problem becomes critically important. This is the case of the automated inspection of vehicle headlamp lens, that due to its geometry and dimensions, requires multiple sensor poses to observe the whole part. Moreover, the customer requirements that define the maximum defect size should also be taken into account in the inspection process. This paper presents a vision sensor planning system applied to the quality control of headlamp lenses. The system uses the lens CAD, a vision sensor model and the customer requirements, included through a fuzzy approach, to achieve an optimal set of viewpoints. To compute the number and distribution of the viewpoints, a genetic algorithm is used. Experimental results demonstrate the effectiveness of the sensor planning system on commercial lenses.

[1]  Peter Kovesi,et al.  Automatic Sensor Placement from Vision Task Requirements , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Bernd Kuhlenkötter,et al.  Automated inspection system for headlamp reflectors , 2007 .

[3]  Enrique Dunn,et al.  Pareto optimal camera placement for automated visual inspection , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  J. G. García-Bermejo,et al.  Total quality control for automotive raw foundry brake disks , 2005 .

[5]  Tomomasa Sato,et al.  Automatic planning of light source and camera placement for an active photometric stereo system , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[6]  Y.F. Li,et al.  Automatic sensor placement for model-based robot vision , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[7]  J. Gamez Garcia,et al.  AN EXPERT KNOWLEDGE BASED SENSOR PLANNING SYSTEM FOR CAR HEADLIGHT LENS INSPECTION , 2008 .

[8]  Theodora Varvarigou,et al.  Automated inspection of gaps on the automobile production line through stereo vision and specular reflection , 2001, Comput. Ind..

[9]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[10]  Carl G. Looney,et al.  Interactive clustering and merging with a new fuzzy expected value , 2002, Pattern Recognit..

[11]  Jindong Tan,et al.  Minimum viewpoint planning for dimensional inspection of sheet metal parts , 2003, Proceedings 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2003).

[12]  J. Gomez Ortega,et al.  SENSOR PLANNING FOR CARS HEADLAMPS INSPECTION USING EXPERT KNOWLEDGE , 2007 .

[13]  G. Roth,et al.  View planning for automated three-dimensional object reconstruction and inspection , 2003, CSUR.

[14]  Robert M. Haralick,et al.  Automatic sensor and light source positioning for machine vision , 1990, [1990] Proceedings. 10th International Conference on Pattern Recognition.

[15]  Enrique Dunn,et al.  Multi-objective Sensor Planning for Efficient and Accurate Object Reconstruction , 2004, EvoWorkshops.

[16]  Hongbing Lu,et al.  A Knowledge-Based Fuzzy Clustering Method with Adaptation Penalty for Bone Segmentation of CT images , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[17]  E. H. Mamdani,et al.  Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis , 1976, IEEE Transactions on Computers.

[18]  Rui Xu,et al.  Survey of clustering algorithms , 2005, IEEE Transactions on Neural Networks.

[19]  Konstantinos A. Tarabanis,et al.  The MVP sensor planning system for robotic vision tasks , 1995, IEEE Trans. Robotics Autom..

[20]  John J. Craig,et al.  Introduction to Robotics Mechanics and Control , 1986 .

[21]  Ching-Chang Wong,et al.  K-means-based fuzzy classifier design , 2000, Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063).

[22]  Konstantinos A. Tarabanis,et al.  A survey of sensor planning in computer vision , 1995, IEEE Trans. Robotics Autom..

[23]  Zbigniew Michalewicz,et al.  Handbook of Evolutionary Computation , 1997 .

[24]  Yifan Chen,et al.  CAD-guided sensor planning for dimensional inspection in automotive manufacturing , 2003 .

[25]  Youfu Li,et al.  A method of automatic sensor placement for robot vision in inspection tasks , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).