Observability of Depth Estimation for a Hand‐Eye Robot System

Abstract

This paper deals with the depth observability problem of a hand-eye robot system. In contrast to earlier works, this paper presents a complete study of this observability problem. The velocity of the active camera in the handeye robot system is considered as the input. The observability of depth estimation is then related to the velocity of the camera. A necessary and sufficient condition for the types of camera velocities necessary to ensure observability is found. This compensates for the results of earlier works, in which the velocity of camera was estimated. The theory is also verified by both simulations and experiments in this paper. Furthermore, a modified LQ visual servo control law is proposed to vary the weighting matrices so that depth estimation is improved while the level of control performance is still retained.

Quasi‐Min‐Max Model Predictive Control for Image‐Based Visual Servoing with Tensor Product Model Transformation

“…It should be noted that in the subfigures "Image plane" of the simulation results Figs 6-13, the symbols of "black+" and "black ★" denote the coordinates of the four image points in the initial image plane and the reference image plane, respectively, and the black solid lines are the image feature trajectories of these four points correspondingly. Meanwhile, the solid lines and the Suppose that the depth Z(t) can be obtained at each sampling time by using 3-D reconstruction [3] or some depth estimation [35] methods. Classic IBVS gives the best image 2D trajectory(see Fig.…”

Reconstruction of 3D contour with an active laser‐vision robotic system

“…In particular, vision plays an important role in intelligent robotic systems [3]. Typical applications include eye-in-hand [4][5][6][7] or eye-tohand [8,9] configuration for autonomously controlling robots with real-time visual feedback. Typical applications include eye-in-hand [4][5][6][7] or eye-tohand [8,9] configuration for autonomously controlling robots with real-time visual feedback.…”

“…By employing visual feedback, control issues concerning uncertain processes and unknown environments could be possibly overcome. Typical applications include eye-in-hand [4][5][6][7] or eye-tohand [8,9] and detailed survey of visual servo systems can be found in [10][11][12].…”