3D Real-Time Reconstruction using the Cloud

This work presents a 3D real-time reconstruction based on a visual SLAM (Simultaneous Localization and Mapping) approach using a RGBD camera. The proposed system aims to create a 3D model of an environment using a low-power computer and Amazon EC2 (Elastic Compute Cloud) server for computation offloading

RL-DOVS: Reinforcement Learning for Autonomous Robot Navigation in Dynamic Environments

Autonomous navigation in dynamic environments where people move unpredictably is an essential task for service robots in real-world populated scenarios. Recent works in reinforcement learning (RL) have been applied to autonomous vehicle driving and to navigation around pedestrians. In this paper, we present a novel planner (reinforcement learning dynamic object velocity space, RL-DOVS) based on an RL technique for dynamic environments. The method explicitly considers the robot kinodynamic constraints for selecting the actions in every control period. The main contribution of our work is to use an environment model where the dynamism is represented in the robocentric velocity space as input to the learning system. The use of this dynamic information speeds the training process with respect to other techniques that learn directly either from raw sensors (vision, lidar) or from basic information about obstacle location and kinematics. We propose two approaches using RL and dynamic obstacle velocity (DOVS), RL-DOVS-A, which automatically learns the actions having the maximum utility, and RL-DOVS-D, in which the actions are selected by a human driver. Simulation results and evaluation are presented using different numbers of active agents and static and moving passive agents with random motion directions and velocities in many different scenarios. The performance of the technique is compared with other state-of-the-art techniques for solving navigation problems in environments such as ours

On the detectability of non-trivial topologies

We explore the main physical processes which potentially affect the topological signal in the Cosmic Microwave Background (CMB) for a range of toroidal universes. We consider specifically reionisation, the integrated Sachs-Wolfe (ISW) effect, the size of the causal horizon, topological defects and primordial gravitational waves. We use three estimators: the information content, the S/N statistic and the Bayesian evidence. While reionisation has nearly no effect on the estimators, we show that taking into account the ISW strongly decreases our ability to detect the topological signal. We also study the impact of varying the relevant cosmological parameters within the 2 sigma ranges allowed by present data. We find that only Omega_Lambda, which influences both ISW and the size of the causal horizon, significantly alters the detection for all three estimators considered here.Comment: 11 pages, 9 figure

A robotized dumper for debris removal in tunnels under construction

Tunnels in construction exhibit many challenges for automation. In this work we address the robotization of a conventional dumper for debris removal during the construction of tunnels, in the framework of a technological transfer project. The goal is to convert a dumper into an autonomous vehicle capable of planning, navigate and localize itself. Planning and navigation techniques have been adapted to the special kinodynamic characteristics of the vehicle. The difficulties for having a precise continuous localization in this kind of scenarios, due to the irregularities of the terrain, the changing illumination and the own scenario, have driven to develop hybrid localization techniques to integrate continuous and discrete information, coming from the navigation sensors, some semantic geometric features, and the signal strength propagation in tunnel scenarios. Simulation and real-world experiments are described, and some preliminary results are discussed

Ground robotics in tunnels: Keys and lessons learned after 10 years of research and experiments

The work reported in this article describes the research advances and the lessons learned by the Robotics, Perception and Real-Time group over a decade of research in the field of ground robotics in confined environments. This study has primarily focused on localization, navigation, and communications in tunnel-like environments. As will be discussed, this type of environment presents several special characteristics that often make well-established techniques fail. The aim is to share, in an open way, the experience, errors, and successes of this group with the robotics community so that those that work in such environments can avoid (some of) the errors made. At the very least, these findings can be readily taken into account when designing a solution, without needing to sift through the technical details found in the papers cited within this text

Integrating an autonomous robot on a dance and new technologies festival

This paper presents the results of a project to integrate an autonomous mobile robot into a modern dance performance at a dance and new technologies festival. The main goal is to integrate a simple low cost mobile robot into the dance performance, in order to study the possibilities that this kind of platforms can offer to the artists. First, this work explains the process and design to embed the robotic platform into the choreography theme. Another contribution described in this work is the system architecture proposed and built to make the robot behaviours match the artists requirements: precise, synchronized and robust robot movements. Finally, we discuss the main issues and lessons learned for this kind of robotics and arts applications and summarize the results obtained, including the successful final live performance results

RoboEarth Semantic Mapping: A Cloud Enabled Knowledge-Based Approach

The vision of the RoboEarth project is to design a knowledge-based system to provide web and cloud services that can transform a simple robot into an intelligent one. In this work, we describe the RoboEarth semantic mapping system. The semantic map is composed of: 1) an ontology to code the concepts and relations in maps and objects and 2) a SLAM map providing the scene geometry and the object locations with respect to the robot. We propose to ground the terminological knowledge in the robot perceptions by means of the SLAM map of objects. RoboEarth boosts mapping by providing: 1) a subdatabase of object models relevant for the task at hand, obtained by semantic reasoning, which improves recognition by reducing computation and the false positive rate; 2) the sharing of semantic maps between robots; and 3) software as a service to externalize in the cloud the more intensive mapping computations, while meeting the mandatory hard real time constraints of the robot. To demonstrate the RoboEarth cloud mapping system, we investigate two action recipes that embody semantic map building in a simple mobile robot. The first recipe enables semantic map building for a novel environment while exploiting available prior information about the environment. The second recipe searches for a novel object, with the efficiency boosted thanks to the reasoning on a semantically annotated map. Our experimental results demonstrate that, by using RoboEarth cloud services, a simple robot can reliably and efficiently build the semantic maps needed to perform its quotidian tasks. In addition, we show the synergetic relation of the SLAM map of objects that grounds the terminological knowledge coded in the ontology

Evidence against or for topological defects in the BOOMERanG data ?

The recently released BOOMERanG data was taken as ``contradicting topological defect predictions''. We show that such a statement is partly misleading. Indeed, the presence of a series of acoustic peaks is perfectly compatible with a non-negligible topological defects contribution. In such a mixed perturbation model (inflation and topological defects) for the source of primordial fluctuations, the natural prediction is a slightly lower amplitude for the Doppler peaks, a feature shared by many other purely inflationary models. Thus, for the moment, it seems difficult to rule out these models with the current data.Comment: 4 pages, 1 figure. Some changes following extraordinarily slow referee Reports and new data. Main results unchanged (sorry

Weak lensing in scalar-tensor theories of gravity

This article investigates the signatures of various models of dark energy on weak gravitational lensing, including the complementarity of the linear and non-linear regimes. It investigates quintessence models and their extension to scalar-tensor gravity. The various effects induced by this simplest extension of general relativity are discussed. It is shown that, given the constraints in the Solar System, models such as a quadratic nonminimal coupling do not leave any signatures that can be detected while other models, such as a runaway dilaton, which include attraction toward general relativity can let an imprint of about 10%.Comment: 25 pages, 29 figure

Cosmological observations in scalar-tensor quintessence

The framework for considering the astronomical and cosmological observations in the context of scalar-tensor quintessence in which the quintessence field also accounts for a time dependence of the gravitational constant is developed. The constraints arising from nucleosynthesis, the variation of the constant, and the post-Newtonian measurements are taken into account. A simple model of supernovae is presented in order to extract the dependence of their light curves with the gravitational constant; this implies a correction when fitting the luminosity distance. The properties of perturbations as well as CMB anisotropies are also investigated.Comment: 26 pages, 22 figures, to appear in PR