In [3], a low-resolution range sensor was investigated for an occupant classification system that distinguish person from child seats or an empty seat. The optimal deployment of vehicle airbags for maximum protection moreover requires information about the occupant's size and position. The detection of occupant's position involves the detection and localization of occupant's head. This is a challenging problem as the approaches based on local shape analysis (in 2D or 3D) alone are not robust enough as other parts of the person's body like shoulders, knee may have similar shapes as the head. This paper discusses and investigate the potential of a Reeb graph approach to describe the topology of vehicle occupants in terms of a skeleton. The essence of the proposed approach is that an occupant sitting in a vehicle has a typical topology which leads to different branches of a Reeb Graph and the possible location of the occupant's head are thus the end points of the Reeb graph. The proposed method is applied on real 3D range images and is compared to Ground truth information. Results show the feasibility of using topological information to identify the position of occupant's head.

This work presents a predictive-control approach to active mechanical filtering of complex, periodic motions of organs induced by respiration or heart beating in robotized surgery. Two different predictive-control schemes are proposed for the compensation of respiratory motions or cardiac motions. For respiratory motions, the periodic property of the disturbance has been included into the input-output model of the controlled system so as to have the robotic system learn and anticipate perturbation motions. A new cost function is proposed for the unconstrained generalized predictive controller (GPC), where reference tracking is decoupled from the rejection of predictable periodic motions. Cardiac motions are more complex, since they are the combination of two periodic nonharmonic components. An adaptive disturbance predictor is proposed which outputs future predicted disturbance values. These predicted values are used to anticipate the disturbance by using the predictive feature of a regular GPC. Experimental results are presented on a laboratory testbed and in vivo on pigs. They demonstrate the effectiveness of the two proposed methods to compensate complex physiological motion.

This work presents first in-vivo results of beating heart tracking with a surgical robot arm in off-pump cardiac surgery. The tracking is performed in a 2D visual servoing scheme using a 500 frame per second video camera. Heart motion is measured by means of active optical markers that are put onto the heart surface. Amplitude of the motion is evaluated along the two axis of the image reference frame. This is a complex and fast motion that mainly reflects the influence of both the respiratory motion and the electro-mechanical activity of the myocardium. A model predictive controller is setup to track the two degrees of freedom of the observed motion by computing velocities for two of the robot joints. The servoing scheme takes advantage of the ability of predictive control to anticipate over future references provided they are known or they can be predicted. An adaptive observer is defined along with a simple cardiac model to estimate the two components of the heart motion. The predictions are then fed into the controller references and it is shown that the tracking behaviour is greatly improved.

Periodic deformations of organs and soft tissues are complex, repetitive disturbances for surgeons manipulating robotic interfaces in computer-assisted surgery. They are due to respiratory movements or heart beats, and they have to be manually compensated for by the surgeon whenever accurate gestures are needed, as it is the case in cardiac or robotized laparoscopic surgery. This work presents a repetitive model predictive control scheme for the cancellation of fast periodic motions by a robot arm, which is controlled by visual servoing at 500 Hz by means of a high-speed camera. The problem we address is to keep a constant distance in the camera images from a surgical tool's tip to the organ surface. Contributions of the control input to reference tracking and to the fast-disturbance rejection are split and computed separately to ensure that the surgeon's interaction on the robot bas no influence on the cancellation performance. The system is tested in a laboratory experiment with an experimental surgical arm and in in vivo conditions on a living pig with a standard surgical robot. Results show the effectiveness and the potential of the proposed control scheme.

Robust and accurate object recognition and tracking is a necessary pre-requisite for visual servoing tasks. This paper presents a new approach using disparity images provided by a stereo vision system from which 3D models are reconstructed. We use a segmentation procedure based on a simple region growing process to segment the non-dense 3D images, and compare it to a classical dense range image segmenter. The obtained regions are classified according to a simple model of the target object, and multiple candidates are handled over image sequences by application of the CONDENSATION algorithm. We then use a histogram based clusterization technique to identify the target object pose for visual servoing commands. Experimental results in localizing a door-handle show an accuracy of about 10 cm for the estimated position, while being robust to clutter and noise in the sensor images.