A practical multirobot localization system

We present a fast and precise vision-based software intended for multiple robot localization. The core component of the software is a novel and efficient algorithm for black and white pattern detection. The method is robust to variable lighting conditions, achieves sub-pixel precision and its computational complexity is independent of the processed image size. With off-the-shelf computational equipment and low-cost cameras, the core algorithm is able to process hundreds of images per second while tracking hundreds of objects with a millimeter precision. In addition, we present the method's mathematical model, which allows to estimate the expected localization precision, area of coverage, and processing speed from the camera's intrinsic parameters and hardware's processing capacity. The correctness of the presented model and performance of the algorithm in real-world conditions is verified in several experiments. Apart from the method description, we also make its source code public at \emph{http://purl.org/robotics/whycon}; so, it can be used as an enabling technology for various mobile robotic problems

Simple yet stable bearing-only navigation

This article describes a simple monocular navigation system for a mobile robot based on the map-and-replay technique. The presented method is robust and easy to implement and does not require sensor calibration or structured environment, and its computational complexity is independent of the environment size. The method can navigate a robot while sensing only one landmark at a time, making it more robust than other monocular approaches. The aforementioned properties of the method allow even low-cost robots to effectively act in large outdoor and indoor environments with natural landmarks only. The basic idea is to utilize a monocular vision to correct only the robot's heading, leaving distance measurements to the odometry. The heading correction itself can suppress the odometric error and prevent the overall position error from diverging. The influence of a map-based heading estimation and odometric errors on the overall position uncertainty is examined. A claim is stated that for closed polygonal trajectories, the position error of this type of navigation does not diverge. The claim is defended mathematically and experimentally. The method has been experimentally tested in a set of indoor and outdoor experiments, during which the average position errors have been lower than 0.3 m for paths more than 1 km long

Calibration and Sensitivity Analysis of a Stereo Vision-Based Driver Assistance System

Az http://intechweb.org/ alatti "Books" fül alatt kell rákeresni a "Stereo Vision" címre és az 1. fejezetre

Theoretical and Experimental Collaborative Area Coverage Schemes Using Mobile Agents

This chapter is concerned with the development of collaborative control schemes for mobile ground robots for area coverage purposes. The simplest scheme assumes point omnidirectional robots with heterogeneous circular sensing patterns. Using information from their spatial neighbors, each robot (agent) computes its cell relying on the power diagram partitioning. If there is uncertainty in inferring the locations of these robots, the Additively Weighted Guaranteed Voronoi scheme is employed resulting in a rather conservative performance. The aforementioned schemes are enhanced by using a Voronoi-free coverage scheme that relies on the knowledge of any arbitrary sensing pattern employed by the agents. Experimental results are offered to highlight the efficiency of the suggested control laws

Evaluación y comparación de sistemas de planificación de navegación de robots en entornos dinámicos

Este trabajo aborda un análisis comparativo de diferentes técnicas de planificación de movimientos en entornos dinámicos. Se basa en trabajos anteriores, en los que se desarrollaron dos técnicas de planificación de movimientos para un robot que se mueve en un entorno dinámico. Se trata de técnicas de navegación robocéntricas en las que el modelo del entorno dinámico se basa en el espacio de velocidad-tiempo del robot, donde se representan tanto los objetos estáticos como dinámicos. La primera técnica trabaja sobre un espacio de velocidades bidimensional (velocidad lineal-velocidad angular). Explota la idea de identificar la mejor estrategia en función de la situación en la que se encuentra el robot. La segunda técnica optimiza una función objetivo en el espacio de velocidad-tiempo para obtener comandos óptimos y trayectorias seguras. Además, incorpora la técnica desarrollada en el primer trabajo como heurística para mejorar la toma de decisiones, dando lugar a Strategies-Optimization. Para evaluar el rendimiento de la navegación con dichas técnicas se define una serie de métricas, que permiten seleccionar los mejores parámetros de optimización para cada tipo de escenario. Estas métricas evalúan y comparan los comportamientos en diferentes escenarios, lo que permite tener una evaluación completa de todas las técnicas. Además, en aplicaciones reales los robots tienen que moverse en escenarios tanto de interior como de exterior. Sin embargo, para que los robots construyan un mapa del entorno, se localicen y naveguen utilizan diferentes sensores, debido al tipo de información disponible y a la incertidumbre de cada sensor en cada momento. Esto provoca discontinuidades en localización o incluso pérdida de ello, lo que debe evitarse. En este trabajo se presenta una técnica de localización unificada para entornos de interior-exterior que permite una transición continua entre una zona de la que se dispone un mapa construido con los sensores láser a bordo del robot y una zona que utiliza el GPS para la localización del robot

Coverage Protocols for Wireless Sensor Networks: Review and Future Directions

The coverage problem in wireless sensor networks (WSNs) can be generally defined as a measure of how effectively a network field is monitored by its sensor nodes. This problem has attracted a lot of interest over the years and as a result, many coverage protocols were proposed. In this survey, we first propose a taxonomy for classifying coverage protocols in WSNs. Then, we classify the coverage protocols into three categories (i.e. coverage aware deployment protocols, sleep scheduling protocols for flat networks, and cluster-based sleep scheduling protocols) based on the network stage where the coverage is optimized. For each category, relevant protocols are thoroughly reviewed and classified based on the adopted coverage techniques. Finally, we discuss open issues (and recommend future directions to resolve them) associated with the design of realistic coverage protocols. Issues such as realistic sensing models, realistic energy consumption models, realistic connectivity models and sensor localization are covered

Non-Linear Optimization Applied to Angle-of-Arrival Satellite-Based Geolocation with Correlated Measurements

A common remote sensing application is producing geolocation estimates for an object of interest from multiple sensor platforms. Geolocation estimates are desired to help improve situational awareness when dealing with space objects that do not actively broadcast their location. A depiction of the error parameters are calculated in conjunction with the positional estimates. Problems occur when multiple measurements from a single sensor are used to estimate a location due to correlations in sensor error. A non-linear optimization approach is presented for determining geolocation estimates and their associated error parameters. The error parameters directly reflect the error present on the individual measurements used to produce the position estimates. Correlations in errors are dealt with by augmenting the non-linear optimization with a covariance intersection algorithm. Finally, the ability to account for correlated errors within the optimization algorithm is analyzed using Monte-Carlo simulations. The ability to describe an objects location with a given confidence helps aid in the analysis of the system at large