Visual Servoing via Nonlinear Predictive Control - Visual Servoing via Advanced Numerical Methods - page 363

Chapter 20
Visual Servoing via Nonlinear Predictive Control

Guillaume Allibert, Estelle Courtial, and Fran¸ois Chaumette

Abstract. In this chapter, image-based visual servoing is addressed via nonlinear
model predictive control. The visual servoing task is formulated into a nonlinear
optimization problem in the image plane. The proposed approach, named visual pre-
dictive control, can easily and explicitly take into account 2D and 3D constraints.
Furthermore, the image prediction over a finite prediction horizon plays a crucial
role for large displacements. This image prediction is obtained thanks to a model.
The choice of this model is discussed. A nonlinear global model and a local model
based on the interaction matrix are considered. Advantages and drawbacks of both
models are pointed out. Finally, simulations obtained with a 6 degrees of freedom
(DOF) free-flying camera highlight the capabilities and the efficiency of the pro-
posed approach by a comparison with the classical image-based visual servoing.

20.1 Introduction

Visual servoing has lead to many fruitful researches over the last decades. In regard
to the kind of feedback information considered, one can distinguish three main ap-
proaches: image-based visual servoing (IBVS) where the feedback is defined in the
image plane, position-based visual servoing (PBVS) where the feedback is com-
posed of 3D data such as the robotic system pose, and the 2 1/2 D visual servoing
where the feedback combines both 2D and 3D data. Further details about the dif-
ferent approaches can be found in [5, 6, 15]. Here, we focus our interest on IBVS
strategy. The IBVS task consists in determining the control input applied to the
robotic system so that a set of visual features designed from image measurements