Evaluating a human-robot interface for exploration missions

The research reported in this paper concerns the design, implementation, and experimental evaluation of a Human-Robot Interface for stationary remote operators, implemented for a PC computer. The GUI design and functionality is described. An Autonomy Management Model has been implemented and explained. We have conducted user evaluation, making two set of experiments, that will be described and the resulting data analyzed. The conclusions give an insight on the most important usability concerns, regarding the operator situational awareness. The scalability of the interface is also experimentally studied

Application of computer vision techniques for laser-based global localization of a mobile robot in a known, static environment

El problema de la localización global, es decir, la localización de un robot en un entorno conocido sin información sobre sus estados previos, ha sido ampliamente estudiado mediante diferentes métodos en la literatura. Un gran número de algoritmos han sido presentados y probados considerando varias condiciones. El objetivo de este trabajo es comparar diferentes métodos de representación de ocupación de celdillas construidos a partir de datos provistos por un láser utilizando técnicas de visión por computador. El enfoque seguido usa comparación visual de un mapa global con uno local, creado gracias al sensor láser. Esta elección permite una fácil integración con sistemas robóticos existentes al mismo tiempo que evita problemas típicos de soluciones puramente visuales, como la influencia de cambios de iluminación. Los dos algoritmos comparados—basados en área y en puntos característicos, respectivamente—han sido evaluados en un entorno interior simulado. Se ha asumido una situación estática, sin presencia de obstáculos móviles. El trabajo ha sido realizado durante un programa de movilidad en el extranjero, por lo que el documento se adapta en fondo y forma a las especificaciones de la Universidad de destino

Sistema de navegación de un robot móvil

El objetivo del proyecto consiste en la realización de un sistema de navegación para un robot móvil. La eficiencia de este sistema y el grado de conocimiento del entorno que proporcione han de permitir que el robot lleve a cabo sus tareas con precisión y flexibilidad. Se ha trabajado en control de movimiento, planificación de trayectorias, control reactivo, localización y construcción de mapas

Artificial Spatial Cognition for Robotics and Mobile Systems: brief survey and current open challenges

Remarkable and impressive advancements in the areas of perception, mapping and navigation of artificial mobile systems have been witnessed in the last decades. However, it is clear that important limitations remain regarding the spatial cognition capabilities of existing available implementations and the current practical functionality of high level cognitive models [1, 2]. For enhanced robustness and flexibility in different kinds of real world scenarios, a deeper understanding of the environment, the system, and their interactions -in general terms- is desired. This long abstract aims at outlining connections between recent contributions in the above mentioned areas and research in cognitive architectures and biological systems. We try to summarize, integrate and update previous reviews, highlighting the main open issues and aspects not yet unified or integrated in a common architectural framework

Artificial Spatial Cognition for Robotics and Mobile Systems: very brief survey and current open challenges

Remarkable and impressive advancements in the areas of perception, mapping and navigation of artificial mobile systems have been witnessed in the last decades. However, it is clear that important limitations remain regarding the spatial cognition capabilities of existing available implementations and the current practical functionality of high level cognitive models [1, 2]. For enhanced robustness and flexibility in different kinds of real world scenarios, a deeper understanding of the environment, the system, and their interactions -in general terms- is desired. This long abstract aims at outlining connections between recent contributions in the above mentioned areas and research in cognitive architectures and biological systems. We try to summarize, integrate and update previous reviews, highlighting the main open issues and aspects not yet unified or integrated in a common architectural framework

Computation of the optimal relative pose between overlapping grid maps through discrepancy minimization

Grid maps are a common environment representation in mobile robotics. Many Simultaneous Localization and Mapping (SLAM) solutions divide the global map into submaps, forming some kind of graph or tree to represent the structure of the environment, while the metric details are captured in the submaps. This work presents a novel algorithm that is able to compute a physically feasible relative pose between two overlapping grid maps. Our algorithm can be used for correspondence search (data association), but also for integrating negative information in a unified way. This paper proposes a discrepancy measure between two overlapping grid maps and its application in a quasi Newton optimization algorithm, with the hypothesis that minimizing such discrepancy could provide useful information for SLAM. Experimental evidence is provided showing the high potential of the algorithm

Fast processing of grid maps using graphical multiprocessors

Grid mapping is a very common technique used in mobile robotics to build a continuous 2D representation of the environment useful for navigation purposes. Although its computation is quite simple and fast, this algorithm uses the hypothesis of a known robot pose. In practice, this can require the re-computation of the map when the estimated robot poses change, as when a loop closure is detected. This paper presents a parallelization of a reference implementation of the grid mapping algorithm, which is suitable to be fully run on a graphics card showing huge processing speedups (up to 50×) while fully releasing the main processor, which can be very useful for many Simultaneous Localization and Mapping algorithms

Robot Goes Back Home Despite All the People

We have developed a navigation system for a mobile robot that enables it to autonomously return to a start point after completing a route. It works efficiently even in complex, low structured and populated indoor environments. A point-based map of the environment is built as the robot explores new areas; it is employed for localization and obstacle avoidance. Points corresponding to dynamical objects are removed from the map so that they do not affect navigation in a wrong way. The algorithms and results we deem more relevant are explained in the paper

Extraction of Geometrical Features in 3D Environments for Service Robotic Applications

Modeling environments with 3D feature based representations is a challenging issue in current mobile robotics. Fast and robust algorithms are required for applicability to navigation. We present an original and effective segmentation method that uses computer vision techniques and the residuals from plane fitting as measurements to generate a range image from 3D data acquired by a laser scanner. The extracted points of each region are converted into plane patches, spheres and cylinders by means of least-squares fitting

A Vision-based Quadrotor Swarm for the participation in the 2013 International Micro Air Vehicle Competition

This paper presents a completely autonomous solution to participate in the 2013 International Micro Air Vehicle Indoor Flight Competition (IMAV2013). Our proposal is a modular multi-robot swarm architecture, based on the Robot Operating System (ROS) software framework, where the only information shared among swarm agents is each robot's position. Each swarm agent consists of an AR Drone 2.0 quadrotor connected to a laptop which runs the software architecture. In order to present a completely visual-based solution the localization problem is simplified by the usage of ArUco visual markers. These visual markers are used to sense and map obstacles and to improve the pose estimation based on the IMU and optical data flow by means of an Extended Kalman Filter localization and mapping method. The presented solution and the performance of the CVG UPM team were awarded with the First Prize in the Indoors Autonomy Challenge of the IMAV2013 competition