A New Error Model Based on Adjustable Exponential Basis for Image-Based Visual Servoing

Abstract

Error model has a great effect on the performance of image-based visual servoing scheme, such as convergence rate, smoothness of control inputs (e.g. velocity commands), etc. For a long time, the classical first-order error model dominates the design of the visual servoing control law, and the second-order error model is not employed to design the control law until recent years, both of which guarantee that the error decreases exponentially. In this paper, by adding an adjustable exponential basis to the feature errors, a new error model for the image-based visual servoing scheme is proposed. To the best of our knowledge, this is the first time that such an error model is developed. Compared with the existing first-order error model and second-order error model, the proposed error model has comprehensive advantages in computational efficiency, convergence rate and depth errors robustness, although it has a disadvantage in velocity smoothness. Comparative numerical simulations and real experiments conducted on a six-axis industrial robot confirm the performance of the proposed error model.