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http://dx.doi.org/10.5302/J.ICROS.2009.15.4.365

Intelligent Hexapod Mobile Robot using Image Processing and Sensor Fusion  

Lee, Sang-Mu (국립한경대학교 정보제어공학과)
Kim, Sang-Hoon (국립한경대학교 정보제어공학과, 전자기술연구소)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.15, no.4, 2009 , pp. 365-371 More about this Journal
Abstract
A intelligent mobile hexapod robot with various types of sensors and wireless camera is introduced. We show this mobile robot can detect objects well by combining the results of active sensors and image processing algorithm. First, to detect objects, active sensors such as infrared rays sensors and supersonic waves sensors are employed together and calculates the distance in real time between the object and the robot using sensor's output. The difference between the measured value and calculated value is less than 5%. This paper suggests effective visual detecting system for moving objects with specified color and motion information. The proposed method includes the object extraction and definition process which uses color transformation and AWUPC computation to decide the existence of moving object. We add weighing values to each results from sensors and the camera. Final results are combined to only one value which represents the probability of an object in the limited distance. Sensor fusion technique improves the detection rate at least 7% higher than the technique using individual sensor.
Keywords
hexapod robot; multi-sensor fusion; object detection; color transform; moving color;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 K. B. Han, J. W. Yang, and Y. S. Baek, Real Time 3D Motion Estimation using Vision System, 2002
2 G. Hager and P. Belhumeur, 'Real-time tracking of image regions with changes in geometry and illumination,' in Proc. IEEE Can! on Computer Vision and Pattern Recognition, San Francisco, CA, pp. 403-410, 1996
3 K.Sobottka and LPatas, 'Segmentation and Tracking of Faces in Color Images,' Proc. Int'I Coif. Face and Gesture Recognition, Vermont(U.S.A), pp. 236-241, Oct. 1996
4 한규범, 백윤수, 동적윤곽모델을 이용한 동적 물체 추적, 대한기계학회논문집 A권 27권 5호, pp. 697-704, 2003   DOI
5 D. Comaniciu and P. Meer, 'Mean shift: A robust approach toward feature space analysis,' IEEE Trans. Pattern Anal. Machine Intell., vol. 24, no. 5, pp. 603-619, 2002   DOI   ScienceOn
6 H. Gharaviad Mike Mills, 'Blockmatching Motion Estimation Algorithm - New Results,' IEEE Trans. Circuits and System, vol. 37, no. 5, May 1990   DOI   ScienceOn
7 J. Yang and A. Waybill, 'Tracking Human Faces in Real Time,' Technical Report CMU-CS-95-21O, Carnage Melon University, 1995
8 정보통신연구진흥원, IT 차세대 성장 동력 기획부(지능형서비스로봇), 12. 2003
9 E. Marchand, P. Bouthemy, F. Chaumette, and V. Moreau, Robust Real-Time Visual Tracking using a 2D-3D Model Based Approach, Proc. of the Seventh IEEE International Conference on Computer Vision. vol. I, pp. 262-268, 1999
10 목임수, 이동 물체에서의 초음파 센서 기술, http://www.autocontrol.co.kr/magazine, 2006
11 G. D. Finlayson, Color Normalization for Object Recognition, ATR Symposium on Face and Object Recognition, Japan, pp. 47-48, April 1998