This paper studies the open problem of uncalibrated visual servoing for constrained robots with parametric uncertainties, interacting with unknown environments. A new theoretical framework and its experimental validation are given. This approach produces global exponential convergence of the constrained visual position and the contact force between the end-effector and the unknown constrain surface. This approach is based on the formulation of the visual orthogonalization principle (VOP), which, under the framework of passivity, yields a closed-loop system that fuses camera, encoder and force sensor signals. An experimental system running on Linux-RTAI OS is implemented to visually drive a constrained direct-drive robot manipulator, equipped with six axis JR3 force sensor and a FireWire CCD digital camera posed in fixed configuration. This experimental system proves the effectiveness of the proposed approach even when the robot, camera and restriction surface parameters are assumed as unknown

Visual servoing of constrained dynamical robots has not yet met a formal treatment. Also, notices that due technological constraints, this task is done slowly at velocity reversals, thus dynamic friction arises, which complicates even more the problem. In this paper, a new adaptive scheme for visual servoing of constrained robots subject to dynamic friction is proposed. An image-based control is introduced to produce simultaneous convergence of the constrained visual position and the contact force between the end-effector and the constraint surface. Camera and robot parameters are considered uncertain. This new approach is based on a new formulation of the orthogonalization principle used in force control, coined here visual orthogonalization principle. This allows, under the framework of passivity, to yield a synergetic scheme that fuses camera, encoder and force sensor signals. Simulation results are presented and show that image errors and force errors converge despite uncertainties of friction model.

A globally convergent visual feedback control scheme is proposed for the dynamic model of uncertain planar robot manipulators with uncalibrated camera. Additionally, a time-varying feedback gain induces a terminal attractor in the visual error manifold to guarantee finite-time convergence of image-based tracking errors. Simulations results of a two degrees-of-freedom manipulator with uncalibrated CCD camera are presented to illustrate the dynamic closed-loop performance.

Godot is a mobile robot platform that serves as a testbed for the interface between a sophisticated low-level robot navigation and a symbolic high-level spoken dialogue system. The interesting feature of this combined system is that information flows in two directions: (1) the navigation system. supplies landmark; information from the cognitive map used for the interpretation of the user's utterances in the dialogue system; and (2) the semantic content of utterances analysed by the dialogue system are used to adjust probabilities about the robot's position in the navigation system.