1,124 research outputs found

    Swarm Robotics

    Get PDF
    Collectively working robot teams can solve a problem more efficiently than a single robot, while also providing robustness and flexibility to the group. Swarm robotics model is a key component of a cooperative algorithm that controls the behaviors and interactions of all individuals. The robots in the swarm should have some basic functions, such as sensing, communicating, and monitoring, and satisfy the following properties

    Combining Differential Kinematics and Optical Flow for Automatic Labeling of Continuum Robots in Minimally Invasive Surgery

    Get PDF
    International audienceThe segmentation of continuum robots in medical images can be of interest for analyzing surgical procedures or for controlling them. However, the automatic segmentation of continuous and flexible shapes is not an easy task. On one hand conventional approaches are not adapted to the specificities of these instruments, such as imprecise kinematic models, and on the other hand techniques based on deep-learning showed interesting capabilities but need many manually labeled images. In this article we propose a novel approach for segmenting continuum robots on endoscopic images, which requires no prior on the instrument visual appearance and no manual annotation of images. The method relies on the use of the combination of kinematic models and differential kinematic models of the robot and the analysis of optical flow in the images. A cost function aggregating information from the acquired image, from optical flow and from robot encoders is optimized using particle swarm optimization and provides estimated parameters of the pose of the continuum instrument and a mask defining the instrument in the image. In addition a temporal consistency is assessed in order to improve stochastic optimization and reject outliers. The proposed approach has been tested for the robotic instruments of a flexible endoscopy platform both for benchtop acquisitions and an in vivo video. The results show the ability of the technique to correctly segment the instruments without a prior, and in challenging conditions. The obtained segmentation can be used for several applications, for instance for providing automatic labels for machine learning techniques

    Real-time performance-focused on localisation techniques for autonomous vehicle: a review

    Get PDF

    A multi-modal perception based assistive robotic system for the elderly

    Get PDF
    Edited by Giovanni Maria Farinella, Takeo Kanade, Marco Leo, Gerard G. Medioni, Mohan TrivediInternational audienceIn this paper, we present a multi-modal perception based framework to realize a non-intrusive domestic assistive robotic system. It is non-intrusive in that it only starts interaction with a user when it detects the user's intention to do so. All the robot's actions are based on multi-modal perceptions which include user detection based on RGB-D data, user's intention-for-interaction detection with RGB-D and audio data, and communication via user distance mediated speech recognition. The utilization of multi-modal cues in different parts of the robotic activity paves the way to successful robotic runs (94% success rate). Each presented perceptual component is systematically evaluated using appropriate dataset and evaluation metrics. Finally the complete system is fully integrated on the PR2 robotic platform and validated through system sanity check runs and user studies with the help of 17 volunteer elderly participants

    Cooperative strategies for the detection and localization of odorants with robots and artificial noses

    Full text link
    En este trabajo de investigación se aborda el diseño de una plataforma robótica orientada a la implementación de estrategias de búsqueda cooperativa bioinspiradas. En particular, tanto el proceso de diseño de la parte electrónica como hardware se han enfocado hacia la validación en entornos reales de algoritmos capaces de afrontar problemas de búsqueda con incertidumbre, como lo es la búsqueda de fuentes de olor que presentan variación espacial y temporal. Este tipo de problemas pueden ser resueltos de forma más eficiente con el empleo de enjambres con una cantidad razonable de robots, y por tanto la plataforma ha sido desarrollada utilizando componentes de bajo coste. Esto ha sido posible por la combinación de elementos estandarizados -como la placa controladora Arduino y otros sensores integrados- con piezas que pueden ser fabricadas mediante una impresora 3D atendiendo a la filosofía del hardware libre (open-source). Entre los requisitos de diseño se encuentran además la eficiencia energética -para maximizar el tiempo de funcionamiento de los robots-, su capacidad de posicionamiento en el entorno de búsqueda, y la integración multisensorial -con la inclusión de una nariz electrónica, sensores de luminosidad, distancia, humedad y temperatura, así como una brújula digital-. También se aborda el uso de una estrategia de comunicación adecuada basada en ZigBee. El sistema desarrollado, denominado GNBot, se ha validado tanto en los aspectos de eficiencia energética como en sus capacidades combinadas de posicionamiento espacial y de detección de fuentes de olor basadas en disoluciones de etanol. La plataforma presentada -formada por el GNBot, su placa electrónica GNBoard y la capa de abstracción software realizada en Python- simplificará por tanto el proceso de implementación y evaluación de diversas estrategias de detección, búsqueda y monitorización de odorantes, con la estandarización de enjambres de robots provistos de narices artificiales y otros sensores multimodales.This research work addresses the design of a robotic platform oriented towards the implementation of bio-inspired cooperative search strategies. In particular, the design processes of both the electronics and hardware have been focused towards the real-world validation of algorithms that are capable of tackling search problems that have uncertainty, such as the search of odor sources that have spatio-temporal variability. These kind of problems can be solved more efficiently with the use of swarms formed by a considerable amount of robots, and thus the proposed platform makes use of low cost components. This has been possible with the combination of standardized elements -as the Arduino controller board and other integrated sensors- with custom parts that can be manufactured with a 3D printer attending to the open-source hardware philosophy. Among the design requirements is the energy efficiency -in order to maximize the working range of the robots-, their positioning capability within the search environment, and multiple sensor integration -with the incorporation of an artificial nose, luminosity, distance, humidity and temperature sensors, as well as an electronic compass-. Another subject that is tackled is the use of an efficient wireless communication strategy based on ZigBee. The developed system, named GNBot, has also been validated in the aspects of energy efficiency and for its combined capabilities for autonomous spatial positioning and detection of ethanol-based odor sources. The presented platform -formed by the GNBot, the GNBoard electronics and the abstraction layer built in Python- will thus simplify the processes of implementation and evaluation of various strategies for the detection, search and monitoring of odorants with conveniently standardized robot swarms provided with artificial noses and other multimodal sensors

    Autonomous Optimization of Swimming Gait in a Fish Robot With Multiple Onboard Sensors

    Get PDF
    Autonomous gait optimization is an essential survival ability for mobile robots. However, it remains a challenging task for underwater robots. This paper addresses this problem for the locomotion of a bio-inspired robotic fish and aims at identifying fast swimming gait autonomously by the robot. Our approach for learning locomotion controllers mainly uses three components: 1) a biological concept of central pattern generator to obtain specific gaits; 2) an onboard sensory processing center to discover the environment and to evaluate the swimming gait; and 3) an evolutionary algorithm referred to as particle swarm optimization. A key aspect of our approach is the swimming gait of the robot is optimized autonomously, equivalent to that the robot is able to navigate and evaluate its swimming gait in the environment by the onboard sensors, and simultaneously run a built-in evolutionary algorithm to optimize its locomotion all by itself. Forward speed optimization experiments conducted on the robotic fish demonstrate the effectiveness of the developed autonomous optimization system. The latest results show that our robotic fish attained a maximum swimming speed of 1.011 BL/s (40.42 cm/s) through autonomous gait optimization, faster than any of the robot's previously recorded speeds
    corecore