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Thermally activated shape memory alloy (SMA) actuators are direct-driven and produce high power density with design versatility. However, beyond their linear actuation in spring forms there is little variety and their application in robotics is limited by the challenging characterization of the actuator. In this paper we introduce a novel low-profile torsional SMA actuator design, and its comprehensive mechanical performance characterization for centimeter-scale robotic applications. We determine the thermo-mechanical model of the actuator with full characterization experiments with load, without load, and in blocked conditions to analyze actuator performance in robotic applications. We also illustrate its application in an origami robot with closed-loop control of the actuators. From the performance tests we have modeled and demonstrated the functional capacity of this low weight torsional actuator and have possibly shown the maximum physical and material limits of an SMA that produces 34.1 mNm torque and has a torque-to-weight ratio of 486 mNm/g.
Posted on: July 8, 2016
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Posted on: June 25, 2016
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Posted on: June 25, 2016
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Posted on: June 25, 2016
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Posted on: June 25, 2016
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Movement Related Cortical Potentials (MRCP) have been the subject of numerous studies. They accompany many self-initiated movements and this makes them a good candidate for incorporation in BCI paradigms. In this work we propose a novel experimental protocol involving natural controlling of a computer mouse and based on EEG recordings from 5 subjects, show that it elicits MRCP. We also show the feasibility of online detection of MRCP by implementing a classification based detection framework. Additionally, we discuss the adverse effects of causality restriction on detection performance by implementing an additional offline approach relaxing those restrictions and comparing the results. The best MRCP detection performance achieved on the recorded data with the offline approach has an average maximum accuracy of 0.76 and with the online approach an average AUC of 0.953.
Posted on: June 20, 2016
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In this paper, we present an experiment in the context of a child-robot interaction where we study the influence of the child-robot spatial arrangement on the child’s focus of attention and the perception of the robot’s performance. In the “Co-Writer learning by teaching” activity, the child teaches a Nao robot how to handwrite. Usually only face-to-face spatial arrangements are tested in educational child robot interactions, but we explored two spatial conditions from Kendon’s F-formation, the side-by-side and the face-to-face formations in a within subject experiment. We estimated the gaze behavior of the child and their consistency in grading the robot with regard to the robot’s progress in writing. Even-though the demonstrations provided by children were not different between the two conditions (i.e. the robot’s learning didn’t differ), the results showed that in the side-by-side condition children tended to be more indulgent with the robot’s mistakes and to give it better feedback. These results highlight the influence of experimental choices in child-robot interaction.
Posted on: June 15, 2016
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Posted on: June 10, 2016
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This paper presents the first cross-continental collaborative robotic event based around education. It was entitled R2T2 and it involved more than 100 children from Europe and Africa. Based on remote programming, video streaming feedback, and a scenario of collaborative space rescue, R2T2 focused on pedagogical elements that are fundamentally different than those characterizing classic robotic competitions. The value of these educational actions is shown through the results of a survey conducted among the participants; the working methodologies by the African students were significantly enhanced and there was a broad inclusion in general, despite the fact that some gender issues lingered. This paper’s contribution is to demonstrate an approach to implementing a north-south collaboration to get school students excited about robotics and the problem-solving skills required in engineering.
Posted on: June 10, 2016
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We study the problem of perceiving forest or mountain trails from a single monocular image acquired from the viewpoint of a robot traveling on the trail itself. Previous literature focused on trail segmentation, and used low-level features such as image saliency or appearance contrast; we propose a different approach based on a deep neural network used as a supervised image classifier. By operating on the whole image at once, our system outputs the main direction of the trail compared to the viewing direction. Qualitative and quantitative results computed on a large real-world dataset (which we provide for download) show that our approach outperforms alternatives, and yields an accuracy comparable to the accuracy of humans that are tested on the same image classification task. Preliminary results on using this information for quadrotor control in unseen trails are reported. To the best of our knowledge, this is the first letter that describes an approach to perceive forest trials, which is demonstrated on a quadrotor micro aerial vehicle.
Posted on: June 2, 2016
