ICA  Vol.3 No.1 , February 2012
Building an Intelligent Home Space for Service Robot Based on Multi-Pattern Information Model and Wireless Sensor Networks
ABSTRACT
This paper is concerned with constructing a prototype intelligent home environment for home service robot. In this environment, multi-pattern information can be represented by some intelligent artificial marks. Light-packs service robots can provide reliable and intelligent service by interacting with the environment through the wireless sensor networks. The intelligent space consists the following main components: smart devices with intelligent artificial mark; home server that connects the smart device and maintains the information through wireless sensor network; and the service robot that perform tasks in collaboration with the environment. In this paper, the multi-pattern information model is built, the construction of wireless sensor networks is presented, the smart and agilely home service is introduced. Fi- nally, the future direction of intelligent space system is discussed.

Cite this paper
F. Lu, G. Tian, F. Zhou, Y. Xue and B. Song, "Building an Intelligent Home Space for Service Robot Based on Multi-Pattern Information Model and Wireless Sensor Networks," Intelligent Control and Automation, Vol. 3 No. 1, 2012, pp. 90-97. doi: 10.4236/ica.2012.31011.
References
[1]   G. H. Tian, “Wide Future for Home Service Robot Research,” International Academic Developments, No. 1, 2007, pp. 28-29.

[2]   S.-H. Baeg, J.-H. Park, J. Koh, et al., “Building a Smart Home Environment for Service Robots Based on RFID and Sensor Networks,” International Conference on Control, Automation and Systems, Seoul Korea, 17-20 October 2007, pp. 1078-1082.

[3]   G. H. Tian, X. L. Li, S. P. Zhao, et al., “Research and Development of Intelligent Space Technology for Home Service Robot,” Journal of Shandong University: Engineering Science, Vol. 37, No. 5, 2007, pp. 53-59.

[4]   J.-H. Lee and H. Hashimoto, “Intelligent Space,” Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Takamatsu, October 30-5 November 2000, pp. 1358-1363.

[5]   J.-H. Lee, N. Ando and H. Hashimoto, “Design Policy of Intelligent Space,” Proceedings of the IEEE International Conference on Systems, Man, and Cyberneticsi, Tokyo, 12-15 October 1999, pp. 1077-1082.

[6]   R. Katsuki, J. Ota, T. Mizuta, T. Kito, T. Arai, et al., “Design of an Artificial Mark to Determine 3D Pose by Monocular Vision,” IEEE International Conference on Robotics and Automation, Taipei, 14-19 September 2003, pp. 995-1000.

[7]   R. Katsuki, J. Ota, Y. Tamura, T. Mizuta, T. Kito, et al., “Handling of Objects with Marks by a Robot,” IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, 27-31 October 2003, pp. 130-135.

[8]   H.-T. Xue, G.-H. Tian, X.-L. Li and F. Lu, “Application of the QR Code for Various Object Identification and Manipulation,” Journal of Shandong University (Engineering Science), Vol. 37, No. 6, 2007, pp. 25-30.

[9]   D. Scharstein and A. J. Briggs, “Real-time Recognition of Self-Similar Landmarks,” Image and Vision Computing, Vol. 19, No. 11, 2001, pp. 763-772. doi:10.1016/S0262-8856(00)00105-0

[10]   B. Zitova and J. Flusser, “Landmark Recognition Using Invariant Features,” Pattern Recognition Letters, Vol. 20, No. 5, 1999, pp. 541-547. doi:10.1016/S0167-8655(99)00031-8

[11]   D. Liu and X.-Q. Gao, “Research on Algorithm of Processing and Identification of QR Barcode Image,” Information Technology, Vol. 28, No. 1, 2004, pp. 61-63.

[12]   N. Strobel, S. Spors and R. Rabenstein, “Joint AudioVideo Object Localization and Tracking,” IEEE Signal Processing Magazine, Vol. 18, No. 1, 2001, pp. 22-31. doi:10.1109/79.911196

[13]   C. Cerrada, S. Salamanca, A. Adan, E. Perez, J.-A. Cerrada and I. Abad, “Improved Method for Object Recognition in Complex Scenes by Fusioning 3-D Information and RFID Technology,” IEEE Transactions on Instrumentation and Measurement, Vol. 58, No. 10, 2009, pp. 3473-3480. doi:10.1109/TIM.2009.2018000

[14]   T. Kim, J. Shin and S. Tak, “Cell Planning for Indoor Object Tracking Based on RFID,” International Conference on Mobile Data Management: Systems, Services and Middleware, Taipei, 18-21 May 2009, pp. 709-713.

[15]   S. Roh, H. and R. Choi, “3-D Tag-Based RFID System for Recognition of Object,” IEEE Transactions on Automation Science and Engineering, Vol. 6, No. 1, 2009, pp. 55-65. doi:10.1109/TASE.2008.2008119

[16]   P. Kamol, S. Nikolandis, R. Ueda and T. Arai, “RFID Based Object Localization System Using Ceiling Cameras with Particle Filter,” International Conference on Future Generation Communication and Networking, Jeju Island, 6-8 December 2007, pp. 37-42.

[17]   Y. H. Xue, G. H. Tian, R. K. Li and H. T. Jiang, “A New Object Search and Recognition Method Based on Artificial Object Mark in Complex Indoor Environment,” The 8th World Congress on Intelligent Control and Automation, Jinan, 7-9 July 2010, pp. 6648-6653.

[18]   D. Smith and S. Singh, “Approaches to Multisensor Data Fusion in Target Tracking: A Survey,” IEEE Transactions on Knowledge and Data Engineering, Vol. 18, No. 12, 2006, pp. 1696-1710. doi:10.1109/TKDE.2006.183

[19]   H. S. Carvalho, W. B. Heinzelman, A. L. Murphy, et al., “ A General Data Fusion Architecture,” Proceedings of the IEEE International Conference on Information Fusion, Queensland, 8-11 July 2003, pp. 1465-1472.

[20]   I. F. Akyildiz, W. Su and Y. Sankarasubramaniam, “Wireless Sensor Networks: A Survey,” Computer Networks, Vol. 38, No. 4, 2001, pp. 393-422. doi:10.1016/S1389-1286(01)00302-4

[21]   J.-S. Lee, Y.-W. Su and C.-C. Shen, “A Comparative Study of Wireless Protocols: Bluetooth, UWB, ZigBee, and Wi-Fi,” Proceedings of 33rd Annual Conference of the IEEE Industrial Electronics Society, Taipei, 5-8 November 2007, pp. 46-51.

[22]   D. Y. He, “The ZigBee Wirelesss Sensor Network in Medical Care Application,” International Conference on Computer, Mechatronics, Control and Electronic Engineering, Changchun, 24-26 August 2010, pp. 497-500.

[23]   E. Callaway, P. Gorday, L. Hester, et al., “Home Networking with IEEE 802.15.4 Developing Standard for Low-Rate Wireless Personal Area Networks,” IEEE Communications Magazine, Vol. 40, No. 8, 2002, pp. 70-77. doi:10.1109/MCOM.2002.1024418

[24]   K. Gill, S.-H. Yang, F. Yao and X. Lu, “A ZigBee-Based Home Automation System,” IEEE Transactions on Consumer Electronics, Vol. 55, No. 2, 2009, pp. 422-430. doi:10.1109/TCE.2009.5174403

[25]   M. Brejl and M. Sonka, “Object Localization and Border Detection Criteria Design in Edge-Based Image Segmentation: Automated Learning from Examples,” IEEE Transactions on Medical Imaging, Vol. 19, No. 10, 2000, pp. 973-985. doi:10.1109/42.887613

[26]   S. Ekvall, D. Kragic and F. Hoffmann, “Object Recognition and Pose Estimation Using Color Cooccurrence Histograms and Geometric Modeling,” Image and Vision Computing,” Vol. 23, No. 11, 2005, pp. 943-955. doi:10.1016/j.imavis.2005.05.006

[27]   M. Ulrich, C. Steger and A. Baumgartner, “Real-Time Object Recognition Using a Modified Generalized Hough Transform,” Pattern Recognition, Vol. 36, No. 11, 2003, pp. 2557-2570. doi:10.1016/S0031-3203(03)00169-9

[28]   P. Viola and M. Jones, “Rapid Object Detection Using a Boosted Cascade of Simple Features,” Proceedings. of International Conference on Computer Vision and Pattern Recognition, Kauai, 8-14 December 2001, pp. 511518.

 
 
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