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Vision-guided robotics has been one of the major research areas in the mechatronics community in recent years. The aim is to emulate the visual system of humans and allow intelligent machines to be developed. With higher intelligence, complex tasks that require the capability of human vision can be performed and replaced by machines. The applications of visually guided systems are many, from automatic manufacturing (Krar and Gill 2003), product inspection (Abdullah, Guan et al. 2004; Brosnan and Sun 2004), counting and measuring (Billingsley and Dunn 2005) to medical surgery (Burschka, Li et al. 2004; Yaniv and Joskowicz 2005; Graham, Xie et al. 2007). They are often found in tasks that demand high accuracy and consistent quality which are hard to achieve with manual labour. Tedious, repetitive and dangerous tasks, which are not suited for humans, are now performed by robots. Using visual feedback to control a robot has shown distinctive advantages over traditional methods, and is commonly termed visual servoing (Hutchinson et al. 1996). Visual features such as points, lines, and regions can be used, for example, to enable the alignment of a manipulator with an object. Hence, vision is a part of a robot control system providing feedback about the state of the interacting object. The development of new methods and algorithms for object tracking and robot control has gained particular interest in industry recently since the world has stepped into the century of automation. Research has been focused primarily on two intertwined aspects: tracking and control. Tracking provides a continuous estimation and update of features during robot/object motion. Based on this sensory input, a control sequence is generated for the robot. More recently, the area has attracted significant attention as computational resources have made real-time deployment of vision-guided robot control possible. However, there are still many issues to be resolved in areas such as camera calibration, image processing, coordinate transformation, as well as real time control of robot for complicated tasks. This chapter presents a vision-guided robot control system that is capable of recognising and manipulating general 2D and 3D objects using an industrial charge-coupled device camera. Object recognition algorithms are developed to recognize 2D and 3D objects of different geometry. The objects are then reconstructed and integrated with the robot controller to enable a fully vision-guided robot control system. The focus of the chapter is placed on new methods and technologies for extracting image information and controlling a

Vision-Guided Robot Control for 3D Object Recognition and Manipulation