Simulation of Detecting Function object for AGV Using Computer Vision with Neural Network

The objective of this paper is to simulate the algorithm to detect object for Automated Guide Vehicle (AGV) guidance problem, namely to obstacle avoidance. Currently, the AGV is a transport vehicle widely used in manufacturing factories and plays an important role in the design of material handling system, moving goods to raw materials or finished product to rightful destination that work automatically. This method has been designed for use in security of internal transportation system in order to prevent a collision to the AGVs and the obstacles, which do not know the exact shape, size and color. Such real-time obstacle detection was crucial, we need to classify the obstacles that are real obstacles or fake obstacles. The computer vision with artificial neural networks (ANN) can help the AGV can see and recognize like a human by imitating the functions of the human brain. In this paper, we proposed an algorithm for detecting obstacle that work automatically with no human is required. The algorithm is divided in to two parts: First, a preliminary feature extraction stage by using the Histograms of Oriented Gradients (HOG) to extract height objects feature from the actual video images. Second, the recognition and classification of the obstacles that are real obstacles or fake obstacles such as a painting or text on the floor by a feed-forward time delay neural network (TDNN) in sequences of black and white video images taken by an on-board camera, then decide whether to hold or continue to be moved. As a result, this paper presents the simulate the algorithm to detect the obstacles for AGV, which can work to destination are defined and avoided obstacles by computer vision with TDNN as an alternative principle with high accuracy and reliability.