Vision-guided robotic grasping: issues and experiments

Many researchers have turned to sensing, and in particular computer vision, to create more flexible robotic systems. Computer vision is often required to provide data for the grasping of a target. Using a vision system for grasping presents several issues with respect to sensing, control, and system configuration. This paper presents some of these issues in concert with the options available to the researcher and the trade-offs to be expected when integrating a vision system with a robotic system for the purpose of grasping objects. The paper includes experimental results from a particular configuration that characterize the type and frequency of errors encountered while performing various vision-guided grasping tasks. These error classes and their frequency of occurrence lend insight into the problems encountered during visual grasping and into the possible solution of these problems