Abstract
This paper presents a study of vision guidance system of AGV using ANFIS(adaptive network fussy inference system). The vision guidance system is based on driving method that recognize obvious characteristic object like driving line and landmark. It has an advantage in its ability to get more data points than other induction sensor of AGV. However, it is hard to build such a system because the camera used for vision guidance system is severely affected by disturbance factor caused by varying brightness of light. Therefore, we have designed and created a dark-room environment to minimize this disturbance factor. However, due to the reduction of viewing-angle by minimized dark-room design, it is difficult to control using PID which is commonly used in driving control, on fast converted driving line. Therefore, this paper proposes vision guidance method of AGV using ANFIS. AGV modeling is done through kinematic analysis for camera and created dark-room environment. Steering angle by double wheel input is revised through FIS. This data is trained by hybrid ANFIS training method, and it is used for driving control. To do performance test of proposed method, we have conducted series of experiments by creating a simulation model of AGV. We also conducted a comparative analysis of proposed method with PID control.
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Jung, K., Lee, I., Song, H., Kim, J., Kim, S. (2012). Vision Guidance System for AGV Using ANFIS. In: Su, CY., Rakheja, S., Liu, H. (eds) Intelligent Robotics and Applications. ICIRA 2012. Lecture Notes in Computer Science(), vol 7506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33509-9_37
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DOI: https://doi.org/10.1007/978-3-642-33509-9_37
Publisher Name: Springer, Berlin, Heidelberg
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