Nicolas Andreff

In this paper, a tight coupling between computer vision and parallel robotics is exhibited through the projective line geometry. Indeed, contrary to the usual methodology where the robot is modeled independently from the control law that will be implemented, the proposed method takes into account, from the early modeling stage, the fact that vision will be used for control. Hence, kinematic modeling and projective geometry are fused into a control-devoted projective kinematic model. Thus, starting from a vision-based kinematic modeling of a Gough—Stewart manipulator, a visual servoing scheme is presented, where the image projection (edges) of the non-rigidly linked legs are servoed, rather than the end-effector pose or the leg directions.

This paper proposes a method to control an I4R parallel robot by the observation of its legs with a calibrated camera. We show that the control law depends only on the edges of its forearms extracted from the image and that no proprioceptive sensors are used. Indeed, the variables needed for control (namely, the directions of the arms and forearms as well as the positions of the wrists) can be reconstructed from the forearms edges. Experimental validation of the reconstruction is given and simulation of the control with realistic noises is performed showing the validity of the approach.

In this paper, the original approach is proposed for the vision-based control of Par4 parallel robot. Observing the mechanism legs with a calibrated camera vision simplifies the control law based on the geometry of lines and the image projection of cylindrical legs. The method is illustrated and validated on a Par4 platform simulation

This paper is related to the vision-based control of parallel robots. Indeed, a method is proposed to estimate the reduced set of kinematic parameters appearing in such a control. To do so, it extends a linear method, obtained for a perspective camera, to the case of an omnidirectional camera, using an existing unifying projection model. The proposed method remains linear, once adequate information is extracted from the omnidirectional images, and does not require any calibration pattern. It works both with perspective and omnidirectional cameras, which is underlined by the reported experiments

In this paper, we show that visual feedback reduces the complexity of parallel robot Cartesian control. Namely, 3D pose visual servoing, where the end-effector pose is indirectly measured and used for regulation, is shown to be well suited to this task since it relieves the control from the difficult forward kinematic problem. Moreover, this complexity reduction is not coming with an increase of the implementation complexity since off-the-shelf hardware and software are now available for visual servoing. It is also shown that such a control gets rid of joint sensors. All this makes 3D pose visual servoing the most straightforward Cartesian control for parallel robots. Experimental results are provided using an open source visual servoing C++ library