Erol Özgür

Dynamic control of the Quattro robot by the leg edges

This paper discusses variable selection for the efficient dynamic control of the Quattro parallel robot through an inverse dynamic model expressed by means of leg orientations. A selection is made within a group of variables where each can imply the state of the robot. Besides, in this work, steering a parallel robot dynamically using its self-projection onto the image plane (where the edges of the lower-legs are exploited in control) is proposed and validated for the first time. In the light of the realistic control simulation, the formative points of better control of the Quattro robot are figured out.

A method for simplifying the analysis of leg-based visual servoing of parallel robots

As the end-effector pose is an external property of a parallel robot, it is natural to use exteroceptive sensors to measure it in order to suppress inaccuracies coming from modelling errors. Cameras offer this possibility. So, it is possible to obtain higher accuracy than in the case of classic control schemes (based on geometrical model). In some cases, it is impossible to directly observe the end-effector, but the leg directions can instead be used. In this case, however, unusual results were recorded, namely: (i) the possibility of controlling the robot by observing a number of legs less than the total number of legs, and that (ii) in some cases, the robot does not converge to the desired end-effector pose, even if the observed leg directions did. These results can be explained through the use of the hidden robot concept, which is a tangible visualisation of the mapping between the observed leg direction space (internal property) and Cartesian space (external property). This hidden robot has different assembly modes and singular configurations from the real robot, and it is a powerful tool to simplify the analysis of the aforementioned mapping. In this paper, the concept of hidden robot model is generalised for any type of parallel robot controlled through visual servoing based on observation of the leg directions. Validation has been accomplished through experiments on a Quattro robot with 4 dof.

Deformable Registration of a Preoperative 3D Liver Volume to a Laparoscopy Image Using Contour and Shading Cues.

The deformable registration of a preoperative organ volume to an intraoperative laparoscopy image is required to achieve augmented reality in laparoscopy. This is an extremely challenging objective for the liver. This is because the preoperative volume is textureless, and the liver is deformed and only partially visible in the laparoscopy image. We solve this problem by modeling the preoperative volume as a Neo-Hookean elastic model, which we evolve under shading and contour cues. The contour cues combine the organ’s silhouette and a few curvilinear anatomical landmarks. The problem is difficult because the shading cue is highly nonconvex and the contour cues give curve-level (and not point-level) correspondences. We propose a convergent alternating projections algorithm, which achieves a \(4\%\) registration error.

Vector-based dynamic modeling and control of the Quattro parallel robot by means of leg orientations

One of the key steps in high-speed control of a parallel robot is to define an efficient dynamic model. It is usually not easy to have such a model for parallel robots, since many of them have complex structures. Here, we propose a vector-based approach, which employs the robot leg orientations, to obtain a simplified inverse dynamic model. At the least, this vector-based methodology is pioneering, when combined with the observation of orientations by a calibrated camera, in the sense of solving the entire control-oriented (hard) modeling problem, both kinematics and dynamics, in an almost algebraic manner through the knowledge of only a nominal set of image features: the edges of the robot legs and their time derivatives. Proposed method is verified on a simulator of the Quattro robot with a computed torque control where the leg orientations are steered.

[POSTER] Visualizing In-Organ Tumors in Augmented Monocular Laparoscopy

One of the important goals of medical augmented reality is to reveal the hidden anatomy, such as a tumor in an organ. However, conveying a hidden tumors depth to the user effortlessly and precisely is still an unsolved problem. This is especially difficult in monocular laparoscopy. First, the number of available depth cues is in practice limited to only two: occlusion and relative size. Second, exploiting these cues is not an easy task either. We propose a specific visualization consisting of auxiliary orthographic tumor silhouettes on the front and back surfaces of the organ and a semi-transparent tumor in between. This creates two depth planes forming a perceivable ratio-scaled metric space for the tumor. We conducted a user study to evaluate the proposed visualization. The results show that subsurface tumor depth perception is improved dramatically compared to the conventional transparent overlay.

Particle-SfT: A Provably-Convergent, Fast Shape-from-Template Algorithm

The Shape-from-Template (SfT) problem is to recover the 3D shape of a deformable object from a single image, given a 3D template and a deformation constraint. We propose Particle-SfT, a new SfT algorithm which handles isometric and non-isometric deformations. We build Particle-SfT upon a particle system guided by deformation and reprojection constraint projections. Reconstruction is achieved by evolving particles to a globally attractive equilibrium, while taking observable external forces such as gravity into account, if any. Particle-SfT may be used to refine an existing initial shape. However, in practice we simply use the template as initial guess. This is because, as opposed to the existing refining methods, Particle-SfT has an extremely wide convergence basin. Particle-SfT is also faster than the existing refining methods. This is because it moves pieces of the shape’s mesh independently to achieve larger step size by optimal constraint projection. We proved its convergence to a fixed-point. We experimented it with synthetic and real data. It has the same accuracy as the best performing isometric method and consistently outperforms all existing elastic methods in almost all cases, while being much faster.

A Perspective on Non-Isometric Shape-from-Template

Shape-from-Template (SfT) uses an objects shape template and a deformation law to achieve single-image reconstruction. SfT is a fundamental tool to retexture or augment a deformable object in a monocular video. It has matured for isometric deformations, but the non-isometric case is yet largely open. This is because modeling is generally more complicated and the constraints certainly weaker. Existing algorithms use, for instance, linear elasticity, require one to provide boundary conditions represented by known deformed shape parts and need nonconvex optimization. We use a very simple and generic model to show that non-isometric SfT has a unique solution up to scale under strong perspective imaging and mild deformation curvature. Our model uses a novel type of homography interpretation that we call Perspective-Projection-Affine-Embedding. It may use boundary conditions if available and can be estimated with Linear Least Squares optimization. We provide experimental results on synthetic and real data.