5,469 research outputs found

    Q-Learning Adjusted Bio-Inspired Multi-Robot Coordination

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    Spatial Awareness in Locative Media Projects

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    This MRP focuses on the different applications that location technologies take part of such as, free based navigational systems like Google Maps, and Locative Media art projects; and analyzes their impact on people and their experience of space. By determining that Google Maps and Locative Media are on different sides of the spectrum of location technologies, I suggest that they are developing different territorial discourses through the use of digital mapping. I suggest that Google Maps is developing a territorialization of space by modifying the way in which the body recognizes space, by creating an image of the world that is designed towards a single user—the Google user—and by imposing itself as a map that is able to represent space. Using a theoretical approach towards understanding these effects, I then analyze three Locative Media project that challenge these ideas, they are: Cary Peppermint’s project, “Indeterminate Hikes,”“Megafone/Montreal in*accessible” by Antoni Abad, and “Amsterdam Real Time” by the Waag Society

    A Framework for Automatic Behavior Generation in Multi-Function Swarms

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    17 USC 105 interim-entered record; under review.Multi-function swarms are swarms that solve multiple tasks at once. For example, a quadcopter swarm could be tasked with exploring an area of interest while simultaneously functioning as ad-hoc relays. With this type of multi-function comes the challenge of handling potentially conflicting requirements simultaneously. Using the Quality-Diversity algorithm MAP-elites in combination with a suitable controller structure, a framework for automatic behavior generation in multi-function swarms is proposed. The framework is tested on a scenario with three simultaneous tasks: exploration, communication network creation and geolocation of Radio Frequency (RF) emitters. A repertoire is evolved, consisting of a wide range of controllers, or behavior primitives, with different characteristics and trade-offs in the different tasks. This repertoire enables the swarm to online transition between behaviors featuring different trade-offs of applications depending on the situational requirements. Furthermore, the effect of noise on the behavior characteristics in MAP-elites is investigated. A moderate number of re-evaluations is found to increase the robustness while keeping the computational requirements relatively low. A few selected controllers are examined, and the dynamics of transitioning between these controllers are explored. Finally, the study investigates the importance of individual sensor or controller inputs. This is done through ablation, where individual inputs are disabled and their impact on the performance of the swarm controllers is assessed and analyzed

    A Framework for Automatic Behavior Generation in Multi-Function Swarms

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    Multi-function swarms are swarms that solve multiple tasks at once. For example, a quadcopter swarm could be tasked with exploring an area of interest while simultaneously functioning as ad-hoc relays. With this type of multi-function comes the challenge of handling potentially conflicting requirements simultaneously. Using the Quality-Diversity algorithm MAP-elites in combination with a suitable controller structure, a framework for automatic behavior generation in multi-function swarms is proposed. The framework is tested on a scenario with three simultaneous tasks: exploration, communication network creation and geolocation of RF emitters. A repertoire is evolved, consisting of a wide range of controllers, or behavior primitives, with different characteristics and trade-offs in the different tasks. This repertoire would enable the swarm to transition between behavior trade-offs online, according to the situational requirements. Furthermore, the effect of noise on the behavior characteristics in MAP-elites is investigated. A moderate number of re-evaluations is found to increase the robustness while keeping the computational requirements relatively low. A few selected controllers are examined, and the dynamics of transitioning between these controllers are explored. Finally, the study develops a methodology for analyzing the makeup of the resulting controllers. This is done through a parameter variation study where the importance of individual inputs to the swarm controllers is assessed and analyzed

    ARTiVIS Arts, real-time video and interactivity for sustainability

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    Dissertação para obtenção do Grau de Doutor em Media DigitaisPortuguese Foundation for Science and Technology (SFRH/BD/42555/2007

    The Evolution of First Person Vision Methods: A Survey

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    The emergence of new wearable technologies such as action cameras and smart-glasses has increased the interest of computer vision scientists in the First Person perspective. Nowadays, this field is attracting attention and investments of companies aiming to develop commercial devices with First Person Vision recording capabilities. Due to this interest, an increasing demand of methods to process these videos, possibly in real-time, is expected. Current approaches present a particular combinations of different image features and quantitative methods to accomplish specific objectives like object detection, activity recognition, user machine interaction and so on. This paper summarizes the evolution of the state of the art in First Person Vision video analysis between 1997 and 2014, highlighting, among others, most commonly used features, methods, challenges and opportunities within the field.Comment: First Person Vision, Egocentric Vision, Wearable Devices, Smart Glasses, Computer Vision, Video Analytics, Human-machine Interactio
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