Robust Visual Servoing of Robot Manipulators Based on Passivity

Abstract

Servo applications (regulation and tracking) are an important class of tasks for robots. Robustness in a servo controller must be guaranteed when the robot manipulator operates in an uncertain environment to ensure the stability and performance in the presence of uncertain robot manipulator dynamics, objects to be manipulated by the robot, and obstacles to be avoid. Also, robots must have sensing capability to adapt to the new tasks without reprogramming. Servoing based on visual measurements, also referred to as visual servoing (Hutchinson et al. (1996)), provided an alternative solution to these applications. Commonly used schemes include position-based and image-based visual servoing, being the main difference how to use the visual measurements: if the visual measurements are used to infer the end-effector pose to implement a cartesian control, we get a position-based visual servoing (PBVS) (see, e.g., Fujita et al. (2007)); if the visual measurements are used directly to calculate the control torque to the manipulator, we get an image-based visual servoing (IBVS) (see, e.g., Espiau et al. (1992) andKelly (1996)). It is generally recognized that IBVS has the advantages of having less on-line computation burden, being more accurate, while the PBVS is more flexible for its implementation. Dynamic visual servoing was proposed by Weiss et al. (1987). A key point in this approach is to view the visual measurements as the output of a dynamic system. By adopting this point of view, Dickmanns & Graefe (1988) set up a dynamic model of curvature evolution of the road in a driving application. However, stability and robustness issues were not addressed . To address these important questions and to investigate further the applications of IBVS for general scenarios, Hashimoto et al. (1996) andMa et al. (1999) studied the image dynamics, i.e., how the image feature of an object moving in a 3-D space evolutes in the 2-D image plane. The visual system in a robotics application is linearized in Hashimoto et al. (1996) at the desired point yielding a linear-time-invariant (LTI) multi-input-multi-output (MIMO)model. Ma et al. (1999) proposed a curve dynamic model in vision guided navigation application and based on it designed a linearizing control law that controls the curvature dynamics in the image plane using only perspective projection thanks to a state-observer. By combining passivity of the visual feedback system and the manipulator dynamics, Fujita et al. (2007) addressed the PBVS to track a 3-D object in a camera-in-hand configuration. However, the resulting control law was more complicated than that obtained with the transposed jacobian approach 9