Abstract
This paper describes the sensor and actuator modeling of a realistic wheeled mobile robot simulator The motivation of developing such simulator is to produce a personalized versatile tool that allows production and validation of robot software reducing considerably the development time. The mobile robot simulator was developed in Object Pascal with its dynamics based on the ODE (Open Dynamics Engine), allowing to develop robot software for a three wheel omnidirectional robot equipped with Infra-Red distance sensors and brushless motors.

Abstract

Abstract
Omni-directional robots are becoming more and more common in recent robotic applications. They offer improved ease of maneuverability and effectiveness at the expense of increased complexity. Frequent applications include but are not limited to robotic competitions and service robotics. The goal of this work is to find a precise dynamical model in order to predict the robot behavior. Models were found for two real world omni-directional robot configurations and their parameters estimated using a prototype that can have 3 or 4 wheels. Simulations and experimental runs are presented in order to validate the presented work.

Abstract
VITRUVsim is a numerical tool with as much as possible physics included. Inputs are the source parameters (flux, morphology, position...) and outputs are sequences of observed fringes and/or reduced visibilities. VITRUVsim is written in a portable and free language Yorick(4).

Abstract

Abstract
Three-dimensional (3 D) objects' reconstruction using just bi-dimensional (21)) images has been a major research topic in Computer Vision. However, it is still a hard problem to solve, when automation, speed and precision are required and/or the objects present complex shapes and visual properties. In this paper, we compare two Active Computer Vision methods commonly used for the 3D reconstruction of objects from image sequences, acquired with a single off-the-shelf CCD camera: Structure From Motion (SFM) and Generalized Voxel Coloring (GVC) SFM recovers the 3D shape of an object using the camera(s)'s or object's movement, while VC is a volumetric method that uses photoconsistency measures to build a 31) model for the object. Both methods considered do not impose any kind of restrictions to the relative motion involved.