Homography-Based Visual Servoing for Eye-in-Hand Robots with Unknown Feature Positions

Visual servoing can effectively control robots using visual feedback information to improve the intelligence and reliability. In most existing dynamics-based image-based visual servoing methods, a restricted condition that the number of the feature points is no larger than 3 is needed to achieve pixel error convergence, which makes them difficlut to achieve three-dimensional (3-D) pose control since at least 4 feature points on a plane are needed to determine the unique end-effector pose. This paper puts forward to a dynamics-based adaptive homography-based visual servoing (HBVS) controller to regulate robot manipulators with eye-in-hand monocular cameras to the desired pose under unknown but constant feature positions. The uncertain depth is represented as a linear form of its position parameters in the Cartesian space, and a composite learning technique is applied to guarantee parameter convergence under a much weaker condition of interval excitation than persistent excitation, resulting in exact depth estimation and 3-D pose regulation. Experiments on a collaborative robot with 7 degrees of freedom named Franka Emika Panda have illustrated the effectiveness of the proposed method.