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Robotics, Volume 2, Issue 2 (June 2013), Pages 36-121

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Research

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Open AccessArticle Moving Object Localization Using Sound-Based Positioning System with Doppler Shift Compensation
Robotics 2013, 2(2), 36-53; doi:10.3390/robotics2020036
Received: 24 February 2013 / Revised: 26 March 2013 / Accepted: 27 March 2013 / Published: 10 April 2013
Cited by 6 | PDF Full-text (658 KB) | HTML Full-text | XML Full-text
Abstract
Sound-based positioning systems are a potential alternative low-cost navigation system. Recently, we developed such an audible sound-based positioning system, based on a spread spectrum approach. It was shown to accurately localize a stationary object. Here, we extend this localization to a moving object
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Sound-based positioning systems are a potential alternative low-cost navigation system. Recently, we developed such an audible sound-based positioning system, based on a spread spectrum approach. It was shown to accurately localize a stationary object. Here, we extend this localization to a moving object by compensating for the Doppler shift associated with the object movement. Numerical simulations and experiments indicate that by compensating for the Doppler shift, the system can accurately determine the position of an object moving along a non-linear path. When the object moved in a circular path with an angular velocity of 0 to 1.3 rad/s, it could be localized to within 25 mm of the actual position. Experiments also showed the proposed system has a high noise tolerance of up to −25 dB signal-to-noise ratio (SNR) without compromising accuracy. Full article
Open AccessArticle An Artificial Neural Network Based Robot Controller that Uses Rat’s Brain Signals
Robotics 2013, 2(2), 54-65; doi:10.3390/robotics2020054
Received: 27 March 2013 / Revised: 16 April 2013 / Accepted: 19 April 2013 / Published: 29 April 2013
Cited by 2 | PDF Full-text (723 KB) | HTML Full-text | XML Full-text
Abstract
Brain machine interface (BMI) has been proposed as a novel technique to control prosthetic devices aimed at restoring motor functions in paralyzed patients. In this paper, we propose a neural network based controller that maps rat’s brain signals and transforms them into robot
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Brain machine interface (BMI) has been proposed as a novel technique to control prosthetic devices aimed at restoring motor functions in paralyzed patients. In this paper, we propose a neural network based controller that maps rat’s brain signals and transforms them into robot movement. First, the rat is trained to move the robot by pressing the right and left lever in order to get food. Next, we collect brain signals with four implanted electrodes, two in the motor cortex and two in the somatosensory cortex area. The collected data are used to train and evaluate different artificial neural controllers. Trained neural controllers are employed online to map brain signals and transform them into robot motion. Offline and online classification results of rat’s brain signals show that the Radial Basis Function Neural Networks (RBFNN) outperforms other neural networks. In addition, online robot control results show that even with a limited number of electrodes, the robot motion generated by RBFNN matched the motion generated by the left and right lever position. Full article
(This article belongs to the Special Issue Intelligent Robots)
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Open AccessArticle Computationally Efficient Adaptive Type-2 Fuzzy Control of Flexible-Joint Manipulators
Robotics 2013, 2(2), 66-91; doi:10.3390/robotics2020066
Received: 1 April 2013 / Revised: 4 May 2013 / Accepted: 13 May 2013 / Published: 21 May 2013
Cited by 2 | PDF Full-text (635 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, we introduce an adaptive type-2 fuzzy logic controller (FLC) for flexible-joint manipulators with structured and unstructured dynamical uncertainties. Simplified interval fuzzy sets are used for real-time efficiency, and internal stability is enhanced by adopting a trade-off strategy between the manipulator’s
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In this paper, we introduce an adaptive type-2 fuzzy logic controller (FLC) for flexible-joint manipulators with structured and unstructured dynamical uncertainties. Simplified interval fuzzy sets are used for real-time efficiency, and internal stability is enhanced by adopting a trade-off strategy between the manipulator’s and the actuators’ velocities. Furthermore, the control scheme is independent of the computationally expensive noisy torque and acceleration signals. The controller is validated through a set of numerical simulations and by comparing it against its type-1 counterpart. The ability of the adaptive type-2 FLC in coping with large magnitudes of uncertainties yields an improved performance. The stability of the proposed control system is guaranteed using Lyapunov stability theory. Full article
(This article belongs to the Special Issue Intelligent Robots)
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Review

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Open AccessReview Psychophysiological Methods to Evaluate User’s Response in Human Robot Interaction: A Review and Feasibility Study
Robotics 2013, 2(2), 92-121; doi:10.3390/robotics2020092
Received: 24 March 2013 / Revised: 13 May 2013 / Accepted: 14 May 2013 / Published: 10 June 2013
Cited by 3 | PDF Full-text (476 KB) | HTML Full-text | XML Full-text
Abstract
Implementing psychophysiological measures is a worthwhile approach for understanding human reaction to robot presence in terms of individual emotional state. This paper reviews the suitability of using psychophysiological assessment in human-robot interaction (HRI) research. A review of most common psychophysiological parameters used in
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Implementing psychophysiological measures is a worthwhile approach for understanding human reaction to robot presence in terms of individual emotional state. This paper reviews the suitability of using psychophysiological assessment in human-robot interaction (HRI) research. A review of most common psychophysiological parameters used in a controlled laboratory setting is provided and advantages and challenges of their utilization in HRI experiments are described. Exemplar studies focused on the implementation of psychophysiological measures for the evaluation of the emotional responses of the participants to the robots’ presence are described. Based on the reviewed literature, the paper also describes the results of our own research experience to make the most of the emerged recommendations. We planned and performed a study aimed at implementing psychophysiological measurements for assessing the human response of two groups of older adults (Healthy vs. Mild Cognitive Impairment subjects) towards a telepresence robot. Finally, the paper provides a summary of lessons learned across the field in using psychophysiological measures in HRI studies. Full article
(This article belongs to the Special Issue Human Centred Robotics)
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