Alicia Casals

Automatic guidance of an assistant robot in laparoscopic surgery

A robotic arm which automatically guides the camera in the laparoscopic surgery is presented. The goal of this work is to generate adequate camera control strategies to track the working scene during a surgical procedure. The system is based on the computer vision analysis of the laparoscopic image that allows the surgeon to identify the scenes relevant point from the surgical instruments.

A review on strategies for recognizing natural objects in colour images of outdoor scenes

Abstract This paper surveys some significant vision systems dealing with the recognition of natural objects in outdoor environments. The main goal of the paper is to discuss the way in which the segmentation and recognition processes are performed: the classical bottom–up, top–down and hybrid approaches are discussed by reviewing the strategies of some key outdoor scene understanding systems. Advantages and drawbacks of the three strategies are presented. Considering that outdoor scenes are especially complex to treat in terms of lighting conditions, emphasis is placed on the way systems use colour for segmentation and characterization proposals. After this study of state-of-the-art strategies, the lack of a consolidated colour space is noted, as well as the suitability of the hybrid approach for handling particular problems of outdoor scene understanding.

Auto-adaptive robot-aided therapy using machine learning techniques

This paper presents an application of a classification method to adaptively and dynamically modify the therapy and real-time displays of a virtual reality system in accordance with the specific state of each patient using his/her physiological reactions. First, a theoretical background about several machine learning techniques for classification is presented. Then, nine machine learning techniques are compared in order to select the best candidate in terms of accuracy. Finally, first experimental results are presented to show that the therapy can be modulated in function of the patient state using machine learning classification techniques.

Autonomous navigation in ill-structured outdoor environment

Presents a methodology for autonomous navigation in weakly structured outdoor environments such as dirt roads or mountain ways. The main problem to solve is the detection of an ill-defined structure-the way-and the obstacles in the scene, when working in variable lighting conditions. First, we discuss the road description requirements to perform autonomous navigation in this kind of environment and propose a simple sensors configuration based on vision. A simplified road description is generated from the analysis of a sequence of color images, considering the constraints imposed by the model of ill-structured roads. This environment description is done in three steps: region segmentation, obstacle detection and coherence evaluation.

A new approach to outdoor scene description based on learning and top-down segmentation

Abstract This paper presents a new approach to outdoor scene description, by providing a methodology for labelling image objects in a top-down way. The systems strategy is based on a cooperative set of distributed tasks, composed of several segmentation processes devoted to the recognition of the objects of interest, and a coordinator process, used to control the segmentation tasks. The approach includes a learning process to generate flexible models that can fit with the relevant objects of outdoor scenes, which can vary significantly under different environment conditions. The results of a series of tests carried out under different weather and seasonal conditions demonstrate the feasibility of the approach.

Stereoscopic system for human body tracking in natural scenes

Human body detection and tracking in a scene constitutes a very active working field clue to their applicability to many areas, specially as a man-machine interface (MMI) means. The system presented aims to improve the reliability and efficiency of teleoperation. The system is of application to teleoperated manipulation in civil applications such as big robots in shipyards, mines, public works or even cranes. Image segmentation is performed from movement detection. The recognition of moving bodies is verified by means of a simplified articulated cylindrical model, thus allowing to operate with a low computational cost.

Human-Robot Interaction Based on a Sensitive Bumper Skin

In order to enable robots to work in close contact with humans or in environments with unknown obstacles, new reactive control strategies based on sensitive bumper skins are proposed. The aim of this work is to provide a robot with contact and force control based strategies that make it dependable, safe and with foreseeable behaviours. The sensory skin is composed of rigid-bumpers provided with deformation sensors incorporated in a flexible substrate. This solution is a compromise between a simple bumper skin and current array-based robot skins, with better potential performances but, more costly and with usability problems. Such sensitive bumpers cover all moveable links of the robot allowing the detection of collisions, measuring the force involved and locating the point of contact with reasonable precision and cost

Open laboratory for robotics education

Laboratories are key components in the learning process of applied matters. The laboratory enables students to acquire methodologies, work habitude, knowledge on equipment operation and experience, in conditions as near as possible to their future professional activity. The evolution of communication and information technologies opens new possibilities in educational methods. The purpose of this paper is to present a Web based system for the implementation of a robotics laboratory with didactic finalities. The laboratory is to be accessible indifferently and simultaneously, in situ or via Internet. The system presented aims at providing access to the laboratory at any time, from everywhere, without space problems, paying special attention to safety requirements. For these reasons we call it an Open laboratory.

Selection of the best stereo pair in a multi-camera configuration

The analysis of the error of stereo measurements by triangulation is revisited from three points of view: geometrical, statistical and visual quality. When the target is visible by a set of distributed cameras in the workspace, there are multiple combinations of camera pairs adequate to be considered for the location, by triangulation, of the target position. Three-camera placements are analysed evaluating their precision in a short-medium distance. The work presented analyses which combination of stereo measurements gives the best results, and proposes a method for the automatic selection of the most adequate cameras pair.

A recurrent neural network approach for 3D vision-based force estimation

Robotic-assisted minimally invasive surgery has demonstrated its benefits in comparison with traditional procedures. However, one of the major drawbacks of current robotic system approaches is the lack of force feedback. Apart from space restrictions, the main problems of using force sensors are their high cost and the biocompatibility. In this work a proposal based on Vision Based Force Measurement is presented, in which the deformation mapping of the tissue is obtained using the ℓ2 - Regularized Optimization class, and the force is estimated via a recurrent neural network that has as inputs the kinematic variables and the deformation mapping. Moreover, the capability of RNN for predicting time series is used in order to deal with tool occlusions. The highlights of this proposal, according to the results, are: knowledge of material properties are not necessary, there is no need of adding extra sensors and a good trade-off between accuracy and efficiency has been achieved.