Methods for visual servoing of robotic systems: A state of the art survey

Abstract

This paper surveys the methods used for visual servoing of robotic systems, where the main focus is on mobile robot systems. The three main areas of research include the Direct Visual Servoing, stereo vision systems, and artificial intelligence in visual servoing. The standard methods such as Image-Based Visual Servoing (IBVS) and Position-Based Visual Servoing (PBVS) are analyzed and compared with the new method named Direct Visual Servoing (DVS). DVS methods have better accuracy, compared to IBVS and PBVS, but have limited convergence area. Because of their high accuracy, DVS methods are suitable for integration into hybrid systems. Furthermore, the use of the stereo systems for visual servoing is comprehensively analyzed. The main contribution of the stereo system is the accurate depth estimation, which is critical for many visual servoing tasks. The use of artificial intelligence (AI) in visual servoing purposes has also gained popularity over the years. AI techniques give visual servoing controllers the ability to learn by using predefined examples or empirical knowledge. The learning ability is crucial for the implementation of robotic systems in a real-world dynamic manufacturing environment. Also, we analyzed the use of visual odometry in combination with a visual servoing controller for creating more robust and reliable positioning system.

Keywords

• Visual servoing
• artificial intelligence
• machine learning
• robotic systems
• computer vision
• intelligent manufacturing systems