Fernando Auat Cheein | Universidad Técnica Federico Santa María (original) (raw)

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Papers by Fernando Auat Cheein

2015 4th International Conference on Systems and Control (ICSC), 2015

Recently, the use of assistive vehicles in industrial or daily day tasks started to grow rapidly.... more Recently, the use of assistive vehicles in industrial or daily day tasks started to grow rapidly. Therefore, it is important to guarantee safety to the robot and to any other moving element in the environment (either people, animals or other robots). In this work, we develop and implement a navigation assistive system based on collision risk estimation using depth sensors. Speed and steering constraints are applied to semi-autonomous assistance vehicles to avoid hazardous situations and to improve the users welfare. We calculate a collision risk indicator based on the tracking of moving elements from the scene, by means of a visual tracking approach and a proposed motion model. The performance of the system is tested in selected situations. Furthermore, the motion model associated with people is empirically validated. Finally, the simulation results included here, show the effectiveness of the system in reducing the imminent collision risk up to 90%, without imposing drastic decisions over the vehicle movement.

Robotica, 2013

SUMMARYIn this work, an optimal maneuverability strategy for car-like unmanned vehicles operating... more SUMMARYIn this work, an optimal maneuverability strategy for car-like unmanned vehicles operating in restricted environments is presented. The maneuverability strategy is based on a path planning algorithm that uses the environment information to plan a safe, feasible and optimum path for the unmanned mobile robot. The environment information is obtained by means of a simultaneous localization and mapping (SLAM) algorithm. The SLAM algorithm uses the sensors' information to build a map of the surrounding environment. A Monte Carlo sampling technique is used to find an optimal and safe path within the environment based on the SLAM information. The objective of the planning is to safely reach a desired orientation in a bounded space. Theoretical demonstrations and real-time experimental results (in indoor and outdoor environments) are also presented in this work.

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2015

Vehicle localization in large-scale urban environments has been commonly addressed as a map-match... more Vehicle localization in large-scale urban environments has been commonly addressed as a map-matching problem in the literature. Generally, the maps are 2D images of the world where each pixel covers a part of it. However, building maps for large-scale urban environments requires driving the vehicle along the desired path at least once. In order to simplify this task, in this work, we propose a new localization system that uses satellite aerial map-images available on the Internet to localize a vehicle in a complex urban environment. Satellite aerial map-images are compared against re-emission maps built from the infrared reflectance information of the vehicle's LiDAR. Normalized Mutual Information (NMI) is used to compare re-emission and aerial map images. A Particle Filter Localization strategy is applied for vehicle's localization. As a result, the system has an accuracy of 0.89m in a test course with 6.5km. Our system can be used continuously without losing track, and it works even in dark and partially occluded areas.

Expert Systems with Applications, 2015

We present a mapping system for large-scale environments with changing features.We describe in a ... more We present a mapping system for large-scale environments with changing features.We describe in a high level of detail a mapping algorithm for 3D-LiDAR.G-ICP was used for loop closure displacement c...

Resumen En este trabajo se presenta una arquitectura de planificación de caminos conmutada para l... more Resumen En este trabajo se presenta una arquitectura de planificación de caminos conmutada para la navegación en entornos agrícolas. Se demuestra además la convergencia de la arquitectura de planificación propuesta al implementar un criterio de conmutación basado en la probabilidad de éxito del camino propuesto por cada planificador. Los caminos que son seleccionados por el criterio de conmutación remiten sus referencias a un controlador de seguimiento de caminos que genera los comandos de control del robot móvil utilizado. La información tanto interna del robot como así también del ambiente, es manejada por un algoritmo de SLAM (por sus siglas en inglés de Simultaneous Localization and Mapping). El algoritmo de SLAM estima recursivamente la localización del vehículo dentro del ambiente y los parámetros que describen geométricamente los troncos de los árboles del entorno. Esta información es usada por los planificadores y por el controlador para ejecutar la navegación de una forma e...

Resumen En este trabajo se presenta la aplicación de un algoritmo de Mapeo Probabilístico y Loca... more Resumen En este trabajo se presenta la aplicación de un algoritmo de Mapeo Probabilístico y Localización Simultáneos (SLAM, por sus siglas en inglés de Simultaneous Localization and Mapping) en una Interface Cerebro-Computadora (ICC) que gobierna la navegación de un robot móvil. La ICC consta de un panel con lugares y funciones predefinidas dentro de un ambiente conocido. El paciente, mediante sus señales electroencefálicas, puede elegir a voluntad desde el panel de control, la función a ejecutar o el destino a alcanzar por el robot móvil. El algoritmo de SLAM permite generar mapas probabilísticos de nuevos entornos. Estos mapas, son segmentados y adicionados a la ICC, ampliando así las opciones del panel. Con los mapas obtenidos es posible generar trayectorias de navegación para el robot móvil. Acompañan este trabajo los resultados experimentales obtenidos. Palabras Clave SLAM, robot móvil, Interfaces Cerebro-Computadora. 1. INTRODUCCIÓN El uso de Interfaces Cerebro-Computadoras ...

IEEE Industrial Electronics Magazine, 2000

ABSTRACT The application of agricultural machinery in precision agriculture has experienced an in... more ABSTRACT The application of agricultural machinery in precision agriculture has experienced an increase in investment and research due to the use of robotics applications in the machinery design and task executions. Precision autonomous farming is the operation, guidance, and control of autonomous machines to carry out agricultural tasks. It motivates agricultural robotics. It is expected that, in the near future, autonomous vehicles will be at the heart of all precision agriculture applications [1]. The goal of agricultural robotics is more than just the application of robotics technologies to agriculture. Currently, most of the automatic agricultural vehicles used for weed detection, agrochemical dispersal, terrain leveling, irrigation, etc. are manned. An autonomous performance of such vehicles will allow for the continuous supervision of the field, since information regarding the environment can be autonomously acquired, and the vehicle can then perform its task accordingly.