Section: New Results

Visual servoing

Classically visual servoing considers the regulation in the image of a set of visual features (usually geometric features). Direct visual servoing schemes, such as photometric visual servoing, have been introduced in order to consider every pixel of the image as a primary source of information and thus avoid the extraction and the tracking of such geometric features. This year, we proposed a method to extend these works by using a global descriptor, namely intensity histograms, on the whole or multiple sub-sets of the images in order to achieve control of a 6 degrees of freedom (DoF) robot [30] [53] .

This work also belongs to the class of direct visual servoing. Its goal was to use photometric moments as visual features in order to increase the convergence domain of this approach by reducing the non linearity of the control problem. In order to cope with appearance and disappearance of some parts of the environment during the camera motion, a spatial weight has been introduced in the definition of photometric moments. Thanks to a particular design of this weight, the analytical form of the interaction matrix has been obtained, from which it was possible to select a set of moment combinations to control all the six degrees of freedom of the system. Satisfactory experimental results have been obtained [29] [8] , even if the loss of invariance properties makes the optimal design of visual features still an open problem.

The goal of this work is to exploit Model Predictive Control (MPC) techniques for dealing in a robust way with loss of features during a IBVS task. The work [31] provides an experimental validation of different correction schemes able to cope with loss of features due to occlusions of limited camera field of view. The reported results show the effectiveness of the proposed techniques during the servoing of four point features.

Following our work related to scanning electron micro- scope (SEM) calibration [12] we considered the control of a micro robot using a direct photometric visual servoing that uses only the pure image information as a visual feature, instead of using classic geometric features such as points or lines. However, in micro-scale, using only image intensity as a visual feature performs unsatisfactorily in cases where the photometric variation is low, such as motions along vision sensor's focal axis under a high magnification. In order to improve the performance and accuracy in those cases, an approach using hybrid visual features is proposed in this paper. Image gradient is employed as a visual feature on z axis while image intensity is used on the other 5 DoFs to control the motion. A 6-DoF micro-positioning task is accomplished by this hybrid visual servoing scheme [34] .

We also considered a full scale autofocus approach for SEM [35] . The optimal focus (in-focus) position of the microscope is achieved by maximizing the image sharpness using a vision-based closed-loop control scheme. An iterative optimization algorithm has been designed using the sharpness score derived from image gradient information. The proposed method has been implemented and validated using a tungsten gun SEM at various experimental conditions like varying raster scan speed, magnification at real-time.

This study is not concerned with visual servoing, but to the application of the same principle of sensor-based control to audio sensors. It is made in collaboration with Nancy Bertin from Panama group at Irisa, Inria Rennes-Bretagne Atlantique. In a first step, we have determined the analytical form of the interaction matrix of audio features based on the time difference of arrival on two microphones. From this modeling step, we have determined the different virtual linkages that can be realized in function of the number and configuration of sources [41] . First experimental results using two microphones monunted on the Pioneer mobile robot (see Section  6.9 ) have been recently obtained.