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Posted on: November 28, 2011
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Anthropomorphism describes people’s tendency to ascribe humanlike qualities to non-human artifacts, such as robots. We investigated anthropomorphic language in 750 posts of online forums about the Roomba robotic vacuum cleaner, the AIBO robotic dog and the iPad tablet computer. Results of this content analysis suggest a significant difference for anthropomorphic language usage among the three technologies. In contrast to Roomba and iPad, the specific characteristics of the robotic dog enhanced a more social interaction and lead people to use considerably more anthropomorphic language.
Posted on: November 28, 2011
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Personal service robots, such as the iRobot Roomba vacuum cleaner provide a promising opportunity to study human-robot interaction (HRI) in domestic environments. Still rather little is known about long-term impacts of robotic home appliances on people’s daily routines and attitudes and how they evolve over time. We investigate these aspects through a longitudinal ethnographic study with nine households, to which we gave a Roomba cleaning robot. During six months, data is gathered through a combination of qualitative and quantitative methods.
Posted on: November 28, 2011
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This paper presents an affordable, fully automated and accurate mapping solutions based on ultra-light UAV imagery. Several datasets are analysed and their accuracy is estimated. We show that the accuracy highly depends on the ground resolution (flying height) of the input imagery. When chosen appropriately this mapping solution can compete with traditional mapping solutions that capture fewer high-resolution images from airplanes and that rely on highly accurate orientation and positioning sensors on board. Due to the careful integration with recent computer vision techniques, the post processing is robust and fully automatic and can deal with inaccurate position and orientation information which are typically problematic with traditional techniques.
Posted on: September 10, 2011
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To date, most modular robotic systems lack flexibility when increasing the number of modules due to their hard building blocks and rigid connection mechanisms. In order to improve adaptation to environmental changes, softness on the module level might be beneficial. However, coping with softness requires fundamental rethinking the way modules are built. A major challenge is to develop a connection mechanism that does not limit the softness of the modules, does not require precise alignment and allows for easy detachment. In this paper, we propose a soft active connection mechanism based on electroadhesion. The mechanism uses electrostatic forces to connect modules. The method is easy to implement and can be integrated in a wide range of soft module types. Based on our experimental results, we conclude that the mechanism is suitable as a connection principle for light-weight modules when efficiency in a wide range of softness, tolerance to alignment and easy detachment are desired. The main contributions of this article are (i) the qualitative comparison of different connector principles for soft modular robots, (ii) the integration of electroadhesion, featuring a novel electrode pattern design, into soft modules, and (iii) the demonstration and characterization of the performance of functional soft module mockups including the connection mechanism.
Posted on: September 6, 2011
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Vision Tape is a novel class of flexible compound-eye-like linear vision sensor dedicated to motion extraction and proximity estimation. This novel sensor possesses intrinsic mechanical flexibility that provides wide-range adaptive shape, allowing adjustable field of view as well as integration with numerous substrates and curvatures. Vision Tape extracts Optic Flow of the visual scene to calculate the motion vector, which allows proximity estimation based on the motion parallax principle.
Posted on: August 31, 2011
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For people with severe physical disabilities, low resolution input devices, such as buttons, sip and puff switches and brain–computer interfaces provide an opportunity to interact with the world. However, it can be difficult to control assistive technology, such as wheelchairs, tele–presence robots and robotic arms, when you have only a limited number of commands available and/or a lack of temporal precision in issuing such commands. These limitations can be overcome by employing shared control techniques, whereby the system assists the user in performing the desired task. In this study we compare the use of a simple discrete shared control policy with a more dynamic proportional shared control policy. We evaluate both approaches on a wheelchair that is only operated by two temporally– constrained discrete buttons. The experiments were performed in two different realistic indoor scenarios: an open–plan, spacious environment and a smaller, more cluttered office environment. A total of 10 healthy participants took part in this study.
Posted on: August 26, 2011
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In this paper, we introduce Vision Tape (VT), a novel class of flexible compound-eye-like linear vision sensor dedicated to motion extraction and proximity estimation. This novel sensor possesses intrinsic mechanical flexibility that provides wide-range adaptive shape, allowing adjustable field of view as well as integration with numerous substrates and curvatures. VT extracts Optic Flow (OF) of the visual scene to calculate the motion vector, which allows proximity estimation based on the motion parallax principle. In order to validate the functionality of VT, we have designed and fabricated an exemplary prototype consisting of an array of eight photodiodes attached to a flexible PCB that acts as mechanical and electrical support. This prototype performs image acquisition and processing with an integrated microcontroller at a frequency of 1000 fps, even during bending of the sensor. With this, the effect of VT shape on motion perception and proximity estimation is studied and, in particular, the effect of pixel-to-pixel angle is discussed. The results of these experiments allow estimating an optimal configuration of the sensor for OF extraction. Subsequently, a method that enhances the quality of extracted OF for non-optimal configurations is proposed. The experimental results show that, by applying the proposed method to VT in a suboptimal curvature, the quality of the OF can be increased by up to 176% and proximity estimation by 178%.
Posted on: August 24, 2011
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Technology is playing an increasing role in our society. Therefore it becomes important to educate the general public, and young generations in particular, about the most common technologies. In this context, robots are excellent education tools, for many reasons: (i) robots are fascinating and attract the attention of all population classes, (ii) because they move and react to their environment, robots are perceived as close to living beings, which make people attracted and attached to them, (iii) robots are multidisciplinary systems and can illustrate technological principles in electronics, mechanics, computer and communication sciences, and (iv) robots have many applications fields: medical, industrial, agricultural, safety … While several robots exist on the market and are used for education, entertainment or both, none fits with the dream educational tool: promoting creativity and learning, entertaining, cheap and powerful. We addressed this goal by developing the Thymio robot and distributing it during workshops over two years. This paper describes the design principles of the robot, the educational context, and the analysis made with 65 parents after two years of use. We conclude the paper by outlining the specifications of a new form of educational robot.
Posted on: August 22, 2011
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A current trend in robotics is to define robot tasks using a combination of superimposed motion patterns. For maximum versatility of such motion patterns, they should be easily and efficiently adaptable for situations beyond those for which the motion was originally designed. In this work, we show how a challenging minigolf-like task can be efficiently learned by the robot using a basic hitting motion model and a task-specific adaptation of the hitting parameters: hitting speed and hitting angle. We propose an approach to learn the hitting parameters for a minigolf field using a set of provided examples. This is a non- trivial problem since the successful choice of hitting parameters generally represent a highly non-linear, multi-valued map from the situation-representation to the hitting parameters. We show that by limiting the problem to learning one combination of hitting parameters for each input, a high-performance model of the hitting parameters can be learned using only a small set of training data. We compare two statistical methods, Gaussian Process Regression (GPR) and Gaussian Mixture Regression (GMR) in the context of inferring hitting parameters for the minigolf task. We validate our approach on the 7 degrees of freedom Barrett WAM robotic arm in both a simulated and real environment.
Posted on: August 16, 2011
