Control and path prediction of an Automate Guided Vehicle

• Autorzy: Butdee S. , Suebomran A. , Vignat F. , Yarlagadda P. K.
• Języki publikacji: EN

Abstrakty

EN

Purpose: In this paper a new architecture and control strategy of an AGV is proposed. It is organized as follows. The system architecture is explained in section 2. Section 3 deals with the kinematics model of the AGV path, prediction and control. Section 4 describes the experiments. The conclusion and recommendation are given in section 5. Design/methodology/approach: It is a three wheels vehicle. The front wheel is used for driving and steering the AGV and the two rear wheels are free. The steering and driving are DC motor. Two encoders are individually attached on the two rear wheels in order to measure the vehicle displacement and then calculate its real timeposition and orientation. The choice of positioning the encoders on the free wheels provides to the vehicle anaccurate measurement of its progression. A programmable logic control (PLC) is used for motion control. Findings: In this paper, An Automate Guided Vehicle (AGV) is presented. The developed algorithm is based on memorised path and kinematics determination of the movement. The vehicle position and deviation are calculated from rear wheels rotation measurement. The steering and driving command are determined from this deviation. Localization of AGV by Kaman filtering algorithm is presented. Control of AGV motion is implemented by using PID control scheme. Displacement axis and steering axis are separated to implement the motion control. We proposed the localization system for estimation of AGV. Position and orientation are estimated by Kalman filtering in state-space model. Position and orientation of AGV are measured and used for simulation for localization system. We conclude that the vehicle can reach from the initial position moved along with generated path with accurate location. A Schneider PLC is used to implement this control. The tests reveal a smooth movement and convenient deviation. Practical implications: The first prototype working, the next research steps will be development of a correction system to correct none detected errors. It will also be necessary to develop the fleet management strategy and software. Originality/value: Future work is planed to increase the accuracy of the system by equip more sensors for observation technique. Treatment of dynamic model and machine vision application of automated vehicle are also planed to the next step.

Słowa kluczowe

EN

automate guided vehicle (AGV); PID control

• Wydawca: International OCSCO World Press
• Czasopismo: Journal of Achievements in Materials and Manufacturing Engineering
• Rocznik: 2008
• Tom: Vol. 31, nr 2
• Strony: 442--448
• Opis fizyczny: Bibliogr. 33 poz., wykr.

Twórcy

autor

Butdee S.

autor

Suebomran A.

autor

Vignat F.

autor

Yarlagadda P. K.

• Department of Production Engineering, Faculty of Engineering, King Mongkut's Institute of Technology North, Bangkok, Bangkok, Thailand,

Bibliografia

• [1] E. Sung, Ng. K. Loon, Y. C. Yin, Parallel Linkage Steering for an Automated Guided Vehicle, IEEE Control Systems Magazine 9/6 (1989) 3-8.
• [2] R. J. Mentel, H. R. A. Landeweerd, Design and operation control of an AGV system, International Journal of Production Economics 41 (1995) 257-256.
• [3] D. Y. Lee, F. DiCesare, Integrated Scheduling of Flexible Manufacturing System Employing Automated Guided Vehicles, IEEE Transactions on Industrial Electronics 41/6 (1994) 602-610.
• [4] M. Gourgand, Xiao-Chao Sun, N. Tchernev, Choice of the Guide Path Layout for an AGV Based Material Handling, IEEE Choice of the guide path layout for an AGV based material handling system, IEEE Emerging Technologies and Factory Automation 2 (1995) 475-483.
• [5] P. S. Liu and L. C Fu, Planning and Scheduling in a Flexible Manufacturing System Using a Dynamic Routing Method for Automated Guided Vehicles, IEEE 12 (1989)1584-1589.
• [6] Wu Naiqi, M. C. ZFou, AGV Routing for Conflict Planning and Scheduling in a Flexible Manufacturing System Using at Resolution in AGV Systems, Proceeding of the 2003 IEEE International Conference on Robotics and Automation, Taipei, Taiwan, 2003, 1428-1433.
• [7] Wu Naiqi, M-C. ZFou, Modeling and Deadlock Control of Automated Guided Vehicle Systems, IEEE/ASME Transactions on Mechatronics 9/1 (2004) 50-57.
• [8] M. P. Fanti, B. Turchiano, Deadlock Avoidance in Automated Guided Vehicle Systems, Proceedings of the International Conference on Advanced Intelligent Mechatronics Proceedings, IEEE/ASME, Como, Italy, 2001, 1017-1022.
• [9] M. Ertugrul, O. Kaynak, A. Sabanovic, A Comparison of Various VSS Techniques on the Control of Automated Guided Vehicles, IEEE 2 (1995) 837-842.
• [10] Y. Dianyong, X. Hui, Application of Fuzzy Control Method to AGV, Proceeding of the 2003 IEEE International Conference ”Robotics, Intelligent Systems and Signal Processing”, Changsha, 2003, 768-772.
• [11] S. Senoo, M. Mino, S. Funabiki, Steering Control of Automated Guided Vehicle for Steering Energy Saving by Fuzzy Reasoning, IEEE 2 (1989) 1712-1716.
• [12] Y. S Kim, K. S Hong, A Tracking Galgorithm for Autonomous Navigation of AGVs in a Container, Proceedings of the 30th Annual Conference of the IEEE Industrial Electronics Society, Busan, 2004, 401-406.
• [13] L. Beji, Y. Bestaoui, Motion Generation and Adaptive Control Method of Automated Guided Vehicles in Road Following, IEEE Transactions on Intelligent Transportation Systems 6/1 (2005) 113-123.
• [14] Q. Fang C. Xie, A Study on Intelligent Path Following and Control for Vision-based Automated Guided Vehicle, Proceedings of the 5th World Congress on Intelligent Control and Automation, Hangzhou, P. R. China, 2004, 4811-4815.
• [15] H. B. Zhang, K. Yuan, S. Q. Mei, Q. R. Zhou, Visual Navigation of an Automated Guided Vehicle based on Path Recognition, Proceedings of the Third International Conference on Machine Learning and Cybernetics, Shanghai, China, 2004, 3877-3881.
• [16] G. A. Borges, A. M. N. Lima and G. S. Deep, Characterization of a Trajectory Recognition Optical Sensor for an Automated Guided Vehicle, IEEE Transactions on Instrumentation and Measurement 49/4 (2000) 813-819.
• [17] S. Butdee, A. Suebsomran, Localization Based on Matching Location of AGV, Proceeding of the 24th International Manufacturing Conference, IMC24, Waterford Institute of Technology, Ireland, 2007, 1121-1128.
• [18] S. Butdee and A Suebsomran, Learning and recognition algorithm of intelligent AGV system, Proceedings of the Global Congress on Manufacturing and Management, Santos, Brazil, 2006, 13-72.
• [19] S. Butdee, F. Vignat, and A. Suebsomran, Self-alignment control of an automated unguided vehicle, Proceedings of the 6th International Conference on Integrated Design and Manufacturing in Mechanical Engineering, Grenoble, France, 2006, 48-64.
• [20] S. M. Bozic, Digital and Kalman filtering, Edward Arnold Ltd, London, 1990.
• [21] G. C. Anousaki, K. J. Kyriakopoulos, Simultaneous Localization and map building for mobile robot navigation, IEEE Robotic and Automation Magazine, 6 (1999) 42-53.
• [22] J. J. Leonard, H. F. Durrant-Whyte, Mobile robot localization by tracking geometric beacons, IEEE Transactions on Robotics and Automation 7 (1991) 376-382.
• [23] K. T. Song, W. H. Tang, Environment perception for a mobile robot using double ultrasonic sensors and a CCD camera, IEEE Transactions on Industrial Electronics 43 (1996) 372-379.
• [24] R. Gutierrez-Osuna, J. A. Janet, R. C. Luo, Modeling of ultrasonic range sensors for localization of autonomous mobile robot, IEEE Transactions on Industrial Electronics 45 (1998) 654-662.
• [25] C. C. Tsai, A localization system of a mobile robot by fusing dead-rokening and ultrasonic measurements, IEEE Transactions on Instrumentation and Measurement 47 (1998) 1399-1404.
• [26] C. F. Olson, Probabilistic self-localization for mobile robots, IEEE Transactions on Robotics and Automation 16 (2000) 55-66.
• [27] P. Bonnifait, G. Garcia, Design and experimental validation of an odometric and goniometric localization system for outdoor robot vehicles, IEEE Transactions on Robotics and Automation 14 (1998) 541-548.
• [28] S. Panzieri, F. Pascucci, G. Ulivi, An outdoor navigation system using GPS and inertial platform, IEEE/ASME Transactions on Mechatronics 7 (2002) 134-142.
• [29] A. Georgiev, P. K. Allen, Localization methods for a mobile robot in urban environments, IEEE Transactions on Robotics 20 (2004) 851-864.
• [30] J. M. Lee, K. Son, M. C. Lee, J. W. Choi, S. H. Han, M. H. Lee, Localization of a mobile robot using the image of a moving object, IEEE Transactions on Industrial Electronics 50 (2003) 612-619.
• [31] J. Wolf, W. Burgard, H. Barkhardt, Robust vision-based localization by combining an image-retrival system with monte carlo localization, IEEE Transactions on Robotics 21 (2005) 208-215.
• [32] S. Se, D. G. Lowe, J. J. Litle, Vision-based global localization and mapping for mobile robots, IEEE Transactions on Robotics 21 (2005) 364-375.
• [33] J. Huang, S. M. Farritor, A. Qadi, S. Goddard, Localization and follow-the-leader control of a heterogeneous group of mobile robots, IEEE/ASME Transactions on Mechatronics 11 (2006) 205-215.