868 resultados para Intelligent Robotics


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The development of an intelligent wheelchair (IW) platform that may be easily adapted to any commercial electric powered wheelchair and aid any person with special mobility needs is the main objective of this project. To be able to achieve this main objective, three distinct control methods were implemented in the IW: manual, shared and automatic. Several algorithms were developed for each of these control methods. This paper presents three of the most significant of those algorithms with emphasis on the shared control method. Experiments were performed by users suffering from cerebral palsy, using a realistic simulator, in order to validate the approach. The experiments revealed the importance of using shared (aided) controls for users with severe disabilities. The patients still felt having complete control over the wheelchair movement when using a shared control at a 50% level and thus this control type was very well accepted. Thus it may be used in intelligent wheelchairs since it is able to correct the direction in case of involuntary movements of the user but still gives him a sense of complete control over the IW movement.

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These are the full proceedings of the conference.

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In this paper a new method for self-localization of mobile robots, based on a PCA positioning sensor to operate in unstructured environments, is proposed and experimentally validated. The proposed PCA extension is able to perform the eigenvectors computation from a set of signals corrupted by missing data. The sensor package considered in this work contains a 2D depth sensor pointed upwards to the ceiling, providing depth images with missing data. The positioning sensor obtained is then integrated in a Linear Parameter Varying mobile robot model to obtain a self-localization system, based on linear Kalman filters, with globally stable position error estimates. A study consisting in adding synthetic random corrupted data to the captured depth images revealed that this extended PCA technique is able to reconstruct the signals, with improved accuracy. The self-localization system obtained is assessed in unstructured environments and the methodologies are validated even in the case of varying illumination conditions.

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In the last decade, local image features have been widely used in robot visual localization. In order to assess image similarity, a strategy exploiting these features compares raw descriptors extracted from the current image with those in the models of places. This paper addresses the ensuing step in this process, where a combining function must be used to aggregate results and assign each place a score. Casting the problem in the multiple classifier systems framework, in this paper we compare several candidate combiners with respect to their performance in the visual localization task. For this evaluation, we selected the most popular methods in the class of non-trained combiners, namely the sum rule and product rule. A deeper insight into the potential of these combiners is provided through a discriminativity analysis involving the algebraic rules and two extensions of these methods: the threshold, as well as the weighted modifications. In addition, a voting method, previously used in robot visual localization, is assessed. Furthermore, we address the process of constructing a model of the environment by describing how the model granularity impacts upon performance. All combiners are tested on a visual localization task, carried out on a public dataset. It is experimentally demonstrated that the sum rule extensions globally achieve the best performance, confirming the general agreement on the robustness of this rule in other classification problems. The voting method, whilst competitive with the product rule in its standard form, is shown to be outperformed by its modified versions.

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Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

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Humanoid robots are an extremely complex interdisciplinary research field. Particularly, the development of high size humanoid robots usually requires joint efforts and skills from groups that are in many different research centers around the world. However, there are serious constraints in this kind of collaborative development. Some efforts have been made in order to propose new software frameworks that can allow distributed development with also some degree of hardware abstraction, allowing software reuse in successive projects. However, computation represents only one of the dimensions in robotics tasks, and the need for reuse and exchange of full robot modules between groups are growing. Large advances could be reached if physical parts of a robot could be reused in a different robot constructed with other technologies by other researcher or group. This paper proposes a new robot framework, from now on called TORP (The Open Robot Project), that aims to provide a standard architecture in all dimensions (electrical, mechanical and computational) for this collaborative development. This methodology also represents an open project that is fully shared. In this paper, the first robot constructed following the TORP specification set is presented as well as the advances proposed for its improvement. © 2010 IEEE.

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En esta memoria se describe el trabajo de construcción de una arquitectura software diseñada para facilitar el desarrollo un planificador de misión de un vehículo aéreo no tripulado (UAV), con el fin de que éste alcance los objetivos marcados en la competición internacional de robótica IARC (séptima edición). A lo largo de la memoria, se describe en primer lugar, una revisión de técnicas de robótica inteligente aplicadas a la construcción de vehículos aéreos no tripulados, en el que se ven los diferentes paradigmas de programación de la robótica inteligente y la clasificación de dichos robots aéreos, dependiendo de su autonomía. Este descripción finaliza con la presentación del problema correspondiente a la competición IARC. A continuación se describe el diseño realizado para soporte al desarrollo de un planificador de misiones de UAVs, con simulación de comportamiento de vehículos robóticos y visualización 3D con movimiento. Finalmente, se muestran las pruebas que se han realizado para validar la construcción de dicha arquitectura software. ---ABSTRACT---In this report it is presented the construction of a software architecture, designed to facilitate the development of a mission planner for an unmanned aerial vehicle (UAV), so that it reaches the goals set in the International Aerial Robotics Competition - IARC (seventh edition). Throughout this report, it is described first, a review of intelligent robotics techniques applied to the construction of unmanned aerial vehicles, where different paradigms of intelligent robotics are seen, along with a classification of such aerial robots, depending on their autonomy. Description ends with the presentation of the problem corresponding to the IARC competition. Following, it is described the design made to satisfy the support to the development of a mission planner for UAV´s, with a simulation of the robotics vehicles’ behaviours and a 3D display with motion. Finally, we will deal with the tests that have been conducted to validate the construction of the software architecture.

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This paper presents the implementation of a modified particle filter for vision-based simultaneous localization and mapping of an autonomous robot in a structured indoor environment. Through this method, artificial landmarks such as multi-coloured cylinders can be tracked with a camera mounted on the robot, and the position of the robot can be estimated at the same time. Experimental results in simulation and in real environments show that this approach has advantages over the extended Kalman filter with ambiguous data association and various levels of odometric noise.

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