A Supervised Learning Model for AGV Perception in Unstructured Environment

A Supervised Learning Model for AGV Perception in Unstructured Environment

Rizwan Aqeel (University Institute of Information Technology, Pakistan), Saif Ur Rehman (University Institute of Information Technology, Pakistan), Saira Gillani (Corvinus University of Budapest, Hungary) and Sohail Asghar (COMSATs Institute of Information Technology, Pakistan)
DOI: 10.4018/978-1-4666-8513-0.ch016
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This chapter focuses on an Autonomous Ground Vehicle (AGV), also known as intelligent vehicle, which is a vehicle that can navigate without human supervision. AGV navigation over an unstructured road is a challenging task and is known research problem. This chapter is to detect road area from an unstructured environment by applying a proposed classification model. The Proposed model is sub divided into three stages: (1) - preprocessing has been performed in the initial stage; (2) - road area clustering has been done in the second stage; (3) - Finally, road pixel classification has been achieved. Furthermore, combination of classification as well as clustering is used in achieving our goals. K-means clustering algorithm is used to discover biggest cluster from road scene, second big cluster area has been classified as road or non road by using the well-known technique support vector machine. The Proposed approach is validated from extensive experiments carried out on RGB dataset, which shows that the successful detection of road area and is robust against diverse road conditions such as unstructured nature, different weather and lightening variations.
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An AGV is a kind of vehicle having ability to navigate autonomously (Wit et al., 2004). An AGV comprises of four major interrelated components (Crane et al., 2007), (a) - Perception; (b) - Planning; (c) -Control; (d) - Intelligence. Of all the four components, perception is the most vital component as it detects, classify, track and predict the future position of different objects of environment such as road, obstacle, pedestrian, etc. (Ilas, 2013). As it can be seen from Figure 1.

Figure 1.

Perception phase of AGV

Accurate detection, classification, tracking and prediction guarantee safe navigation of an AGV from the source towards the destination and can be helpful in avoidance of accidents and collisions of an AGV. The Road is an important object of the environment where AGV navigates to reach its destination. Road detection has gained researcher's attention in recent years as an important research problem because of complex environmental conditions like cloudy or rainy weather, muddy roads, shadows, unstructured road and night time driving etc.

Research contributions have productively achieved for road detection based on data mining techniques in recent decades. During the road detection, pattern identification and classification is needed and according to (Laskshmi and Raghunandhan., 2011) data mining is the process of discovering patterns. In (Maurya et al., 2011), (Song and Civco., 2004), (Qin et al., 2013), (Wen Hung., 2013), different data mining techniques such as support vector machine, Bayesian, k-means clustering and artificial neural network has been used for solving road detection problem. Some research works (Vitor et al., 2013), (Shang et al., 2013) even examines this problem under diverse conditions like rainy, sunny or cloudy weather. Existing approaches are based on features, activity and model for the road detection, which are successful under given and specific road conditions, however, these algorithms likely to perform poorly as the road condition changes.

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