Event based localization in Ackermann steering limited resource mobile robots

“© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”This paper presents a local sensor fusion technique with an event-based global position correction to improve the localization of a mobile robot with limited computational resources. The proposed algorithms use a modified Kalman filter and a new local dynamic model of an Ackermann steering mobile robot. It has a similar performance but faster execution when compared to more complex fusion schemes, allowing its implementation inside the robot. As a global sensor, an event-based position correction is implemented using the Kalman filter error covariance and the position measurement obtained from a zenithal camera. The solution is tested during a long walk with different trajectories using a LEGO Mindstorm NXT robot.This work was supported by FEDER-CICYT projects with references DPI2011-28507-C02-01 and DPI2010-20814-C02-02, financed by the Ministerio de Ciencia e Innovacion (Spain). This work was also supported by the University of Costa Rica.Marín, L.; Vallés Miquel, M.; Soriano Vigueras, Á.; Valera Fernández, Á.; Albertos Pérez, P. (2014). Event based localization in Ackermann steering limited resource mobile robots. IEEE/ASME Transactions on Mechatronics. 19(4):1171-1182. doi:10.1109/TMECH.2013.2277271S1171118219

Mobile robot localization failure recovery

Mobile robot localization is one of the most important problems in robotics. Localization is the process of a robot finding out its location given a map of its environment. A number of successful localization solutions have been proposed, among them the well-known and popular Monte Carlo localization method, which is based on particle filters. This thesis proposes a localization approach based on particle filters, using a different way of initializing and resampling of the particles, that reduces the cost of localization. Ultrasonic and light sensors are used in order to perform the experiments. Monte Carlo Localization may fail to localize the robot properly because of the premature convergence of the particles. Using more number of particles increases the computational cost of localization process. Experimental results show that, applying the proposed method robot can successfully localize itself using less number of particles; therefore the cost of localization is decreased

Multi sensor fusion framework for indoor-outdoor localization of limited resource mobile robots

This paper presents a sensor fusion framework that improves the localization of mobile robots with limited computational resources. It employs an event based Kalman Filter to combine the measurements of a global sensor and an inertial measurement unit (IMU) on an event based schedule, using fewer resources (execution time and bandwidth) but with similar performance when compared to the traditional methods. The event is defined to reflect the necessity of the global information, when the estimation error covariance exceeds a predefined limit. The proposed experimental platforms are based on the LEGO Mindstorm NXT, and consist of a differential wheel mobile robot navigating indoors with a zenithal camera as global sensor, and an Ackermann steering mobile robot navigating outdoors with a SBG Systems GPS accessed through an IGEP board that also serves as datalogger. The IMU in both robots is built using the NXT motor encoders along with one gyroscope, one compass and two accelerometers from Hitecnic, placed according to a particle based dynamic model of the robots. The tests performed reflect the correct performance and low execution time of the proposed framework. The robustness and stability is observed during a long walk test in both indoors and outdoors environments.This work has been partially funded by FEDER-CICYT projects with references DPI2011-28507-C02-01 and DPI2010-20814-C02-02, financed by Ministerio de Ciencia e Innovacion (Spain). Also, the financial support from the University of Costa Rica is greatly appreciated.Marín, L.; Vallés Miquel, M.; Soriano Vigueras, Á.; Valera Fernández, Á.; Albertos Pérez, P. (2013). Multi sensor fusion framework for indoor-outdoor localization of limited resource mobile robots. 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Two improved methods for mobile robot localization

Mobile robot localization is the problem of determining the robot\u27s pose given the map of its environment, based on the sensor reading and its movement. It is a fundamental and very important problem in the research of mobile robotics. Grid localization and Monte Carlo localization (MCL) are two of the most widely used approaches for localization, especially the MCL. However each of these two popular methods has its own problems. How to reduce the computation cost and better the accuracy is our main concern. In order to improve the performance of localization, we propose two improved localization algorithms. The first algorithm is called moving grid cell based MCL, which takes advantages of both grid localization and MCL and overcomes their respective shortcomings. The second algorithm is dynamic MCL based on clustering, which uses a cluster analysis component to reduce the computation cost

Implementation of Kalman filter for the indoor location system of a Lego NXT mobile robot

This paper shows the implementation of an estimation technique based on Kalman filter to correct accumulated errors that occur along a trajectory when tracking location over a mobile platform (Lego NXT 2.0 type) in a known environment. The implementation begins with kinematic models and odometers to subsequently construct the filter and conduct the corresponding experimentation

A Novel Real-Time MATLAB/Simulink/LEGO EV3 Platform for Academic Use in Robotics and Computer Science

Over the last years, mobile robot platforms are having a key role in education worldwide. Among others, LEGO Robots and MATLAB/Simulink are being used mainly in universities to improve the teaching experience. Most LEGO systems used in the literature are based on NXT, as the EV3 version is relatively recent. In contrast to the previous versions, the EV3 allows the development of real-time applications for teaching a wide variety of subjects as well as conducting research experiments. The goal of the research presented in this paper was to develop and validate a novel real-time educational platform based on the MATLAB/Simulink package and the LEGO EV3 brick for academic use in the fields of robotics and computer science. The proposed framework is tested here in different university teaching situations and several case studies are presented in the form of interactive projects developed by students. Without loss of generality, the platform is used for testing different robot path planning algorithms. Classical algorithms like rapidly-exploring random trees or artificial potential fields, developed by robotics researchers, are tested by bachelor students, since the code is freely available on the Internet. Furthermore, recent path planning algorithms developed by the authors are also tested in the platform with the aim of detecting the limits of its applicability. The restrictions and advantages of the proposed platform are discussed in order to enlighten future educational applications

Navegación de un robot móvil de configuración diferencial basada en fusión sensorial

Uno de los aspectos esenciales en la robótica móvil es la obtención y procesamiento de la información relativa a la localización del robot en el espacio de movimiento con el fin utilizarla para generar los movimientos deseados del robot. Para esto se busca utilizar la mayor cantidad posible de fuentes de información con el fin de corregir los errores de posición asociados a la presencia de ruido en las mediciones del robot. La fusión de toda esta información en una sola medida que pueda ser utilizada en el control de robot es tema central del presente trabajo en el cual se expone la implementación de una fusión de distintos datos provenientes de sensores para mejorar la navegación en robots móviles con recursos de computación limitados. Para ello, se hace una revisión de las técnicas existentes para la fusión de datos, poniendo especial interés en las correspondientes a filtros de Kalman. Se implementaron y probaron distintos esquemas de fusión de sensores utilizando información proveniente de sensores inerciales comunes de un robot en configuración diferencial (acelerómetros, giroscopios, brújula y encoders). Estas pruebas permitieron obtener el método de fusión de sensores propuesto, el que utiliza un filtro de Kalman junto con la información de un modelo local del robot móvil (modelo dinámico con descomposición por partículas inerciales junto con el modelo cinemático) de un robot móvil diferencial y la información de uno o varios sensores inerciales (según la plataforma). Este método propuesto es muy eficiente en términos de utilización de recursos lo cual permite su implementación en robots con recursos limitados. Además su desempeño es comparable a los esquemas de fusión más complejos que utilizan un modelo no lineal y los filtros de Kalman Extendidos y Unscented tal y como se muestra en los resultados obtenidos. El esquema propuesto se probó ampliamente en distintas plataformas como el robot e-puck, el sensor inercial industrial IG500 y principalmente utilizando el robot móvil LEGO NXT debido a su capacidad de utilizar distintos sensores inerciales, todo esto con el fin de comprobar el buen desempeño del método propuesto.Marin Paniagua, LJ. (2011). Navegación de un robot móvil de configuración diferencial basada en fusión sensorial. http://hdl.handle.net/10251/15617Archivo delegad

Experiences on a motivational learning approach for robotics in undergraduate courses

This paper presents an educational experience carried out in robotics undergraduate courses from two different degrees: Computer Science and Industrial Engineering, having students with diverse capabilities and motivations. The experience compares two learning strategies for the practical lessons of such courses: one relies on code snippets in Matlab to cope with typical robotic problems like robot motion, localization, and mapping, while the second strategy opts for using the ROS framework for the development of algorithms facing a competitive challenge, e.g. exploration algorithms. The obtained students’ opinions were instructive, reporting, for example, that although they consider harder to master ROS when compared to Matlab, it might be more useful in their (robotic related) professional careers, which enhanced their disposition to study it. They also considered that the challenge-exercises, in addition to motivate them, helped to develop their skills as engineers to a greater extent than the skeleton-code based ones. These and other conclusions will be useful in posterior courses to boost the interest and motivation of the students.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech