Yee Yang Yap
ABSTRACT
Automated guided vehicle (AGV) is a solution for warehouse goods transportation, but robot localization is crucial for this application and existing methods are expensive. Therefore, in this paper, a low-cost landmark based AGV algorithm localization algorithm with single camera is proposed for warehouse application. The proposed algorithm includes the computer vision algorithm to recognize the landmark and estimate the distance between the landmark and AGV with single camera. Previous localization algorithm based on triangulation is using three landmarks for localization, the proposed localization algorithm uses only two landmarks which is based on concept of intersection of two circles. The landmarks in the scene were detected with Canny edge detection method and transformed back to straight square from skewed image with perspective transform to provide consistent landmark recognition result. The landmark then was recognized with Tesseract open source character recognition library and custom trained database. The performance of the proposed algorithm was evaluated using images captured by a single camera setup on a trolley and maneuvered through the library and laboratory at Universiti Sains Malaysia with landmarks. The recognition accuracy for landmark is 93.26% overall. The average error of the localization algorithm was 237.29mm and standard deviation 184.27mm. As a conclusion, landmark based AGV localization algorithm for warehouse application was successfully developed.
- A. K. Kar, N. K. D., S. S. F. Nawaz, R. Chandola and N. K. Verma Automated Guided Vehicle Navigation with Obstacle Avoidannce in Normal and GUided Environments. 2016 11th International Conference on Industrial and Information Systems (ICIIS)2016), pp. 77--82.Google Scholar
- Maniscalco, U., Infantino, I. and Manfre, A. Robust Mobile Robot Self-Localization by Soft Sensor Paradigm. 2017 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS)2017), 19--24.Google Scholar
- Olson, C. F. Probabilistic self-localization for mobile robots. IEEE Transactions on Robotics and Automation, 162000), 55--66.Google Scholar
- Markom, M. A., Adom, A. H., Shukor, S. A. A., Rahim, N. A., Tan, E. S. M. M. and Irawan, A. Scan matching and KNN classification for mobile robot localisation algorithm. 2017 IEEE 3rd International Symposium in Robotics and Manufacturing Automation (ROMA)2017), 1--6.Google Scholar
- Chao, H., Zhongqing, F., Yupeng, R., Yueyue, C., Haixiang, L., Xiaodong, W. K. and Jianmeng, B. An efficient magnetic localization system for indoor planar mobile robot. 2015 34th Chinese Control Conference (CCC)2015), 4899--4904.Google Scholar
- Sanpechuda, T. and Kovavisaruch, L. A review of RFID localization: Applications and techniques. 2008 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 22008), 769--772.Google Scholar
- Biswas, J. and Veloso, M. Depth camera based indoor mobile robot localization and navigation. 2012 IEEE International Conference on Robotics and Automation2012), 1697--1702.Google Scholar
- Borenstein, J., Everett, H. R., Feng, L. and Wehe, D. Mobile Robot Positioning: Sensors and Techniques. Journal of Robotic System 19971997), 231--249.Google Scholar
- Hossain, S. G. M., Jamil, H., Ali, M. Y. and Haq, M. Z. Automated guided vehicles for industrial logistics - Development of intelligent prototypes using appropriate technology. 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE), 52010), 237--241.Google Scholar
- Seong, J., Kim, J. and Chung, W. Mobile robot localization using indistinguishable artificial landmarks. 2013 10th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)2013), 222--224.Google Scholar
- Joglekar, A., Joshi, D., Khemani, R., Nair, S. and Sahare, S. Depth Estimation Using Monocular Camera. International Journal of Computer Science and Information Technologies (IJCSIT), 22011), 1758--1763.Google Scholar
- Wang, G. and Yang, K. A New Approach to Sensor Node Localization Using RSS Measurements in Wireless Sensor Networks. IEEE Transactions on Wireless Communications, 102011), 1389--1395.Google Scholar
- Font, J. M. and Batlle, J. A. Mobile Robot Localization. Revisiting the Triangulation Methods. IFAC Proceedings Volumes, 392006), 340--345.Google Scholar
- Zhang, H., Zhang, C., Yang, W. and Chen, C. Localization and navigation using QR code for mobile robot in indoor environment. 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO)2015), 2501--2506.Google Scholar
- Borenstein, J. and Feng, L. Measurement and correction of systematic odometry errors in mobile robots. IEEE Transactions on Robotics and Automation, 121996), 869--880.Google Scholar
- Park, H. R., Hyun, D. J., Yang, H. S. and Park, H. S. A dead reckoning sensor system and a tracking algorithm for mobile robot. 2009 ICCAS-SICE2009), 5559--5563.Google Scholar
- Park, S. and Hashimoto, S. Indoor localization for autonomous mobile robot based on passive RFID. 2008 IEEE International Conference on Robotics and Biomimetics2009), 1856--1861.Google Scholar
- Shan-shan, C., Wu-heng, Z. and Zhi-lin, F. Depth estimation via stereo vision using Birchfield's algorithm. 2011 IEEE 3rd International Conference on Communication Software and Networks2011), 403--407.Google Scholar
- Venet, T., Capitaine, T., Hamzaoui, M. and Fazzino, F. One active beacon for an indoor absolute localization of a mobile vehicle. Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292), 12002), 1--6.Google ScholarCross Ref
- Taha, Z., Mat-Jizat, J. A. and Ishak, I. Bar code detection using omnidirectional vision for automated guided vehicle navigation. International Conference on Automatic Control and Artificial Intelligence (ACAI 2012)2012), 589--592.Google ScholarCross Ref
- Kobayashi, H. A new proposal for self-localization of mobile robot by self-contained 2D barcode landmark. 2012 Proceedings of SICE Annual Conference (SICE)2012), 2080--2083.Google Scholar
- Lee, S. C., Choi, J. S. and Lee, D. Trilateration based multi-robot localization under anchor-less outdoor environment. 2012 7th International Conference on Computer Science Education (ICCSE)2012), 958--961.Google Scholar
- Gao, X., Wang, J. and Chen, W. Land-mark Placement for Reliable Localization of Automatic Guided Vehicle in Warehouse Environment. Proceedings of the 2015 IEEE Conference on Robotics and Biomimetics2015), 1900--1905.Google Scholar
- Smith, R. An Overview of Tesseract OCR Engine. Ninth International Conference on Document Analysis and Recognition (ICDAR 2007)2017), 629--633Google Scholar
Index Terms
- Landmark-based Automated Guided Vehicle Localization Algorithm for Warehouse Application
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