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Item Open Access PERCEIVED SAFETY IN PHYSICAL HUMAN ROBOT INTERACTION - A SURVEY(arxiv, 2021) Rubagotti, Matteo; Tusseyeva, Inara; Baltabayeva, Sara; Summers, Danna; Sandygulova, AnaraThis review paper focuses on different aspects of perceived safety for a number of autonomous physical systems. This is a major aspect of robotics research, as more and more applications allow human and autonomous systems to share their space, with crucial implications both on safety and on its perception. The alternative terms used to express related concepts (e.g., psychological safety, trust, comfort, stress, fear, and anxiety) are listed and explained. Then, the available methods to assess perceived safety (i.e., questionnaires, physiological measurements, behavioral assessment, and direct input devices) are described. Six categories of autonomous systems are considered (industrial manipulators, mobile robots, mobile manipulators, humanoid robots, drones, and autonomous vehicles), providing an overview of the main themes related to perceived safety in the specific domain, a description of selected works, and an analysis of how motion and characteristics of the system influence the perception of safety. The survey also discusses experimental duration and location of the reviewed papers as well as identified trends over time.Item Open Access A Generalized Observer for Estimating Fast–Varying Disturbances(IEEE, 2018-04) Ton, Duc Do; Nguyen, Hoach TheIn this paper, a generalized disturbance observer (GDO) is proposed for estimating a broad range of disturbances including fast-varying ones. The estimation error of the proposed GDO is proven to be ultimately bounded provided that an arbitrary r th time derivative of disturbance is bounded. A broader range of disturbances can be estimated by the proposed GDO in comparison with the conventional disturbance observers (DO) or even recent fast-varying disturbance observers (FVDO) because conservative assumptions such as zero time-derivatives of disturbances are avoided. Furthermore, intuitive rules for gain-tuning and selecting the weighting matrices in the observer design are systematically presented. To validate the superiority of the proposed GDO to conventional FVDOs, comprehensive studies using the linear and nonlinear systems with different types of disturbances are conducted in the MATLAB/Simulink platform. In a specific application of wind energy conversion systems, the proposed GDO is employed to precisely estimate the aerodynamic torque. Then, a completed control system with a linear quadratic regulator (LQR) is designed and implemented to verify the final performance with the proposed GDO. The proposed observer-based LQR is proved to ultimately be bounded stable with superior performances to further validate the proposed GDO.Item Open Access Generalized Dynamics of Stacked Tensegrity Manipulators(Institute of Electrical and Electronics Engineers, 2019-05-14) Fadeyev, Denis; Zhakatayev, Altay; Kuzdeuov, Aksat; Varol, Huseyin AtakanTensegrity structures emerged initially as an art form, have recently gained substantial interest among engineering researchers. The distinctive attribute of these structures is using pretensioned tensile elements connected to rigid bars to establish an equilibrium of the whole structure. Thanks to these elements, tensegrity structures are lightweight and yet robust. The main challenge impeding their widespread use is the intricate constrained nonlinear dynamics caused by the tensegrity topology. In this paper, we extend the dynamics of tensegrities by adding damping forces and incorporating forces along the connected strings passing through several nodes. As an experimental platform, a two-stage stacked tensegrity manipulator was constructed. The system was actuated using six actuators and the kinematic information of the system was acquired by measuring the node coordinates using optical motion capture. Afterward, we compared the structure behavior to the simulated one using our dynamics formulation. The results of these experiments show that our dynamics formulation is capable of representing the rich nonlinear dynamics of stacked tensegrity manipulators effectively.Item Restricted A Series Elastic Tactile Sensing Array for Tactile Exploration of Deformable and Rigid Objects(2018) Kappassov, Zhanat; Baimukashev, Daulet; Adiyatov, Olzhas; Atakan Varol, HuseyinTactile sensing arrays are used to detect contacts of robotic systems with the environment. They are particularly useful for scenarios in which vision-based sensors cannot be used. Thanks to the presence of multiple sensing elements, tactile arrays also provide spatial information about the contact location. In this work, we present our series elastic tactile array to enable tactile exploration for position-controlled robot manipulators. Sixteen compliant sensing elements are arranged as a 4 4 array. This allows the position-controlled robot to explore objects via palpation. Tactile sensing was accomplished by measuring the change of the magnetic field caused by neodymium magnets embedded into the series elastic elements. We demonstrate the efficacy of our sensor with two sets of experiments involving physical interaction scenarios. Firstly, we show that the sensor can be used to differentiate between rigid and deformable objects. Secondly, we show that point clouds of objects can be generated quickly with our sensor module attached to a position-controlled robot manipulator as an endeffectorItem Restricted Tactile sensing in dexterous robot hands – review(2016-05-06) Kappassov, Zhanat; Corrales, Juan-Antonio; Perdereau, VéroniqueTactile sensing is an essential element of autonomous dexterous robot hand manipulation. It provides information about forces of interaction and surface properties at points of contact between the robot fingers and the objects. Recent advancements in robot tactile sensing led to development of many computational techniques that exploit this important sensory channel. This paper reviews current state-of-the-art of manipulation and grasping applications that involve artificial sense of touch and discusses pros and cons of each technique. The main issues of artificial tactile sensing are addressed. General requirements of a tactile sensor are briefly discussed and the main transduction technologies are analyzed. Twenty eight various tactile sensors, each integrated into a robot hand, are classified in accordance with their transduction types and applications. Previously issued reviews are focused on hardware part of tactile sensors, whereas we present an overview of algorithms and tactile feedback-based control systems that exploit signals from the sensors. The applications of these algorithms include grasp stability estimation, tactile object recognition, tactile servoing and force control. Drawing from advancements in tactile sensing technology and taking into consideration its drawbacks, this paper outlines possible new directions of research in dexterous manipulationItem Metadata only An intelligent system for quality measurement of Golden Bleached raisins using two comparative machine learning algorithms(Measurement, 2017-09-01) Karimi, Navab; Ranjbarzadeh Kondrood, Ramin; Alizadeh, Tohid; Navab, KarimiAbstract In this research, an expert system is provided for measuring and recognizing the quality and purity of mixed (pure-impure) raisins using bulk raisins’ images. For this purpose, by utilizing a machine vision setup 1400 images of the raisins were captured in the several ranges of mixture (from 5 to 50%). Then, totally 146 textural features were obtained using four methods of gray-level histograms, gray level co-occurrence matrix (GLCM), gray level run-length (GLRM) matrix, and local binary pattern (LBP). Principal Components Analysis (PCA) was used in order to find the optimum features from the extracted features. Accordingly, Artificial Neural Network (ANN) and Support Vector Machine (SVM) were used for classifying the mixtures. In comparison to ANN, using top 50 features, SVM classifier had more efficient and accurate classification results (averagely 92.71%). The results of the proposed approach can be used in designing a system for purity and quality measuring of raisins.Item Metadata only Design and evaluation of action observation and motor imagery based BCIs using Near-Infrared Spectroscopy(Measurement, 2017-02-01) Abibullaev, Berdakh; An, Jinung; Lee, Seung Hyun; Moon, Jeon Il; Berdakh, AbibullaevAbstract The integration of Brain-Computer-Interfaces (BCI) into rehabilitation research is a promising approach that may substantially impact the rehabilitation success. Yet, there is still significant challenges that needs to be addressed before the BCI technology can be fully used effectively in a clinical setting as a neural prosthesis for motor impaired users. As it is still unknown whether the conventional BCI induction strategies that use different the types of stimuli and/or mental tasks induce cortical reorganization for disabled users. This paper presents a design and evaluation of a real-time Near-Infrared Spectroscopy (NIRS) based BCI protocol to control an external haptic device, and an interesting source of brain signals that may convey complementary information for inducing neuroplasticity. The protocol is based on the ideas derived from Mirror-based Therapy (MT) in which subjects not only perform literal motor imagery tasks but also combine their intents with visual action observation of a related motor imagery task. The NIRS-BCI system then commands a haptic device in real-time to move in opposing directions of leftward and rightward movement. We also compare the proposed protocol to the conventional limb motor imagery task and verify its efficacy with online decoding accuracies up to 94.99%. The initial validation of the experimental setup was done with seven healthy subjects. Nonetheless we contend that the design of the current NIRS-BCI method hold promise with patient populations for effective stroke rehabilitation therapy, because the beneficial effects of MT alone in post-stroke recovery has already been manifested in the literature.Item Metadata only Minimizing inter-subject variability in fNIRS-based brain–computer interfaces via multiple-kernel support vector learning(Medical Engineering & Physics, 2013-12-01) Abibullaev, Berdakh; An, Jinung; Jin, Sang-Hyeon; Lee, Seung Hyun; Moon, Jeon Il; Berdakh, AbibullaevAbstract Brain signal variation across different subjects and sessions significantly impairs the accuracy of most brain–computer interface (BCI) systems. Herein, we present a classification algorithm that minimizes such variation, using linear programming support-vector machines (LP-SVM) and their extension to multiple kernel learning methods. The minimization is based on the decision boundaries formed in classifiers’ feature spaces and their relation to BCI variation. Specifically, we estimate subject/session-invariant features in the reproducing kernel Hilbert spaces (RKHS) induced with Gaussian kernels. The idea is to construct multiple subject/session-dependent RKHS and to perform classification with LP-SVMs. To evaluate the performance of the algorithm, we applied it to oxy-hemoglobin data sets acquired from eight sessions and seven subjects as they performed two different mental tasks. Results show that our classifiers maintain good performance when applied to random patterns across varying sessions/subjects.Item Metadata only Cluster-head based feedback for simplified time reversal prefiltering in ultra-wideband systems(Physical Communication, 2017-12-01) Soleimani, Hossein; Tomasin, Stefano; Alizadeh, Tohid; Shojafar, Mohammad; Hossein, SoleimaniAbstract Time-reversal prefiltering (TRP) technique for impulse radio (IR) ultra wide-band (UWB) systems requires a large amount of feedback to transmit the channel impulse response from the receiver to the transmitter. In this paper, we propose a new feedback design based on vector quantization. We use a machine learning algorithm to cluster the estimated channels into several groups and to select the channel cluster heads (CCHs) for feedback. In particular, CCHs and their labels are recorded at both side of the UWB transceivers and the label of the most similar CCH to the estimated channel is fed back to the transmitter. Finally, the TRP is applied using the feedback CCH. The proposed digital feedback provides three main advantages: (1) it significantly reduces the dedicated bandwidth required for feedback; (2) it considerably improves the speed of transceivers; and, (3) it is robust to noise in the feedback channel since few bytes are required to send the codes that can be heavily error protected. Numerical results on standard UWB channel models are discussed, showing the advantage of the proposed solution.Item Open Access Disturbance Observer-Based Fuzzy SMC of WECSs Without Wind Speed Measurement(IEEE Access, 2017-02-25) Do, Ton DucThe main role of control system for wind turbines is tracking the optimal power via regulating the rotor speed of the generator. A high performance controller, which can deal with unmodeled dynamics, uncertainties, and external disturbance, can effectively increase the captured power from the wind. This paper focuses on designing an advanced sliding mode control (SMC) scheme for wind energy conversion systems (WECSs). As the proposed SMC scheme includes a nonlinear disturbance observer (DOB) for estimating aerodynamic torque and wind speed, there is no requirement to measure aerodynamic torque or wind speed. The proposed control scheme considers not only the uncertainties and disturbance but also the random nature of wind speed and intrinsic nonlinear behavior of the WESCs. Via designing sliding surface based on estimated information, the proposed control system can avoid disadvantages associated with the robust control techniques. To totally remove chattering as well as improving other control criteria, a fuzzybased variable switching gain scheme is introduced. Comparative simulation results are shown to verify the effectiveness and superior performance of the proposed DOB-based fuzzy SMC scheme.Item Open Access Closed-Loop Control of Variable Stiffness Actuated Robots via Nonlinear Model Predictive Control(IEEE Access, 2015-04-10) Zhakatayev, Altay; Rubagotti, Matteo; Varol, Huseyin AtakanVariable stiffness actuation has recently attracted great interest in robotics, especially in areas involving a high degree of human robot interaction. After investigating various design approaches for variable stiffness actuated (VSA) robots, currently the focus is shifting to the control of these systems. Control of VSA robots is challenging due to the intrinsic nonlinearity of their dynamics and the need to satisfy constraints on input and state variables.Contrary to the partially open-loop state-of-the-art approaches, in this paper, we present a close-loop control framework for VSA robots leveraging recent increases in computational resources and advances in optimization algorithms. In particular, we generate reference trajectories by means of open-loop optimal control, and track these trajectories via nonlinear model predictive control in a closed-loop manner. In order to show the advantages of our proposed scheme with respect to the previous (partially open-loop) ones, extensive simulation and real-world experiments were conducted using a two link planar manipulator for a ball throwing task. The results of these experiments indicate that the closed-loop scheme outperforms the partially open loop one due to its ability to compensate for model uncertainties and external disturbances, while satisfying the imposed constraints.Item Open Access Locomotion Strategy Selection for a Hybrid Mobile Robot Using Time of Flight Depth Sensor(Journal of Sensors, 2015-03-22) Saudabayev, Artur; Kungozhin, Farabi; Nurseitov, Damir; Varol, Huseyin AtakanThe performance of a mobile robot can be improved by utilizing different locomotion modes in various terrain conditions. This creates the necessity of having a supervisory controller capable of recognizing different terrain types and changing the locomotion mode of the robot accordingly. This work focuses on the locomotion strategy selection problem for a hybrid legged wheeled mobile robot. Supervisory control of the robot is accomplished by the terrain recognizer, which classifies depth images obtained from a commercial time of flight depth sensor and selects different locomotion mode subcontrollers based on the recognized terrain type. For the terrain recognizer, a database is generated consisting of five terrain classes (Uneven, LevelGround, StairUp, StairDown, and Nontraversable). Depth images are enhanced using confidence map based filtering. The accuracy of the terrain classification using Support VectorMachine classifier for the testing database in five-class terrain recognition problem is 97%. Real-world experiments assess the locomotion abilities of the quadruped and the capability of the terrain recognizer in real-time settings. The results of these experiments show depth images processed in real time using machine learning algorithms can be used for the supervisory control of hybrid robots with legged andwheeled locomotion capabilities.Item Open Access Sensors for Robotic Hands: A Survey of State of the Art(IEEE Access, 2015-10-12) Saudabayev, Artur; Varol, Huseyin AtakanRecent decades have seen significant progress in the field of artificial hands. Most of the surveys, which try to capture the latest developments in this field, focused on actuation and control systems of these devices. In this paper, our goal is to provide a comprehensive survey of the sensors for artificial hands. In order to present the evolution of the field, we cover five year periods starting at the turn of the millennium. At each period, we present the robot hands with a focus on their sensor systems dividing them into categories, such as prosthetics, research devices, and industrial end-effectors.We also cover the sensors developed for robot hand usage in each era. Finally, the period between 2010 and 2015 introduces the reader to the state of the art and also hints to the future directions in the sensor development for artificial hands.Item Open Access A Chaotic Neural Network as Motor Path Generator for Mobile Robotics(IEEE International Conference on Robotics and Biomimetics, 2014) Folgheraiter, Michele; Gini, GiuseppinaThis work aims at developing a motor path generator for applications in mobile robotics based on a chaotic neural network. The computational paradigm inspired by the neural structure of microcircuits located in the human prefrontal cortex is adapted to work in real-time and used to generate the joints trajectories of a lightweight quadruped robot. The recurrent neural network was implemented in Matlab and a software framework was developed to test the performances of the system with the robot dynamic model. Preliminary results demonstrate the capability of the neural controller to learn period signals in a short period of time allowing adaptation during the robot operationItem Open Access Actuation Design Methodology for Haptic Interfaces and Rehabilitation Systems(IEEE 8th International Conference on Application of Information and Communication Technologies, 2014) Folgheraiter, MicheleThis paper introduces a methodology and a software framework intended to optimize and speed up the design process of a haptic interface or a rehabilitation system. Starting from an initial mechanical design the procedure allows to export the kinematic and dynamic properties of the robotic system in a simulation environment. The software receives as additional input the Cartesian or joints trajectories and generates as output the required torques at the joints. From the recorded measurements the program extracts the torque ranges necessary to choose a suitable actuation system for the robot. The possibility to run the simulation in batch modality allows also to define different optimization techniques that may be used to reduce the overall system weight or increase its payloadItem Open Access A Neuromorphic Motion Controller for a Biped Robot(Human Performance and Robotics, Satellite Workshop of IEEE Humanoid 2016, 2016) Folgheraiter, Michele; Keldibek, Amina; Aubakir, Bauyrzhan; Salakchinov, Shyngys; Gini, Giuseppina; Mauro Franchi, AlessioHere we propose a neuromorphic control system for a medium size humanoid robot under development in the Robotics and Mechatronics Department at Nazarbayev University and in cooperation with Politecnico di Milano.Item Open Access ADVANCED STEPS IN BIPED ROBOTICS: INNOVATIVE DESIGN AND INTUITIVE CONTROL THROUGH SPRING-DAMPER ACTUATOR(4th IEEE/RAS International Conference on Humanoid Robots, 2004) Scarfogliero, Umberto; Folgheraiter, Michele; Gini, GiuseppinaThis paper focuses on the study and design of an anthropomorphical light biped robot. The robot presents a total of twelve degree of freedom that will permit it to act a walk in a three dimensional space, right now tested only in simulation. Each joint resemble the functionalities of the human articulation and is moved by tendon connected with actuator located in the robot’s pelvis. We implemented and tested an innovative actuator that permits to set the joint stiffness in real time maintaining a simple position control paradigm. The controller is able to estimate the external load measuring the spring deflection and demonstrated to be particularly robust respect to system uncertainties, such as inertia value changes. Comparing the resulting control law with existing models we found several similarities with the Equilibrium Point Theory.Item Open Access A Multi-Modal haptic interface for Virtual Reality and Robotics(Journal of Intelligent and Robotic Systems, 2008-08) Folgheraiter, Michele; Gini, Giuseppina; Vercesi, DarioIn this paper we present an innovative haptic device that combines the electro-tactile stimulation with the force and visual feedbacks in order to improve the perception of a virtual world. We discuss about the sensation evoked in a user by the haptic, force, and visual interface provided by this device, implemented as a special glove, equipped with sensors and actua- tors connected to a PC. The techniques used to recreate tactile and kines- thetic sensations are based on an innovative use of cutaneous stimulation integrated with actuators and 3D modelling techniques. We discuss about the specificity of haptic interfaces, their controllers, their open problems. We present results about generating the sensation of touching virtual ob- jects with our device. Experiments show also that, using a multi-modal sensorial pattern of stimulation, the subject perceives more realistically the virtual object. We discuss about possible use of the same technique as a way to interface intelligent robots.Item Open Access Development of a neuromorphic control system for a lightweight humanoid robot(The International Conference on Information Technology and Digital Applications, 2016-11) Folgheraiter, Michele; Keldibek, Amina; Aubakir, Bauyrzhan; Salakchinov, ShyngysA neuromorphic control system for a lightweight middle size humanoid biped robot built using 3D printing techniques is proposed. The control architecture consists of different modules capable to learn and autonomously reproduced complex periodic trajectories. Each modul is represented by a chaotic Recurrent Neural Network (RNN) with a core of dynamic neurons randomly and sparsely connected with fixed synapses . A set of read-out units with adaptable synapses realize a linear combination of the neurons output in order to reproduce the target signals. Different experiments were conducted to find out the optimal initialization for the RNN`s parameters. From simulation results, using normalized signals obtained from the robot model, it was proven that all the instances of the control module can learn and reproduce the target trajectories with an average RMS error of 1.63 and variance 0.74Item Open Access A combined B-Spline-Neural-Network and ARX Model for Online Identi cation of Nonlinear Dynamic Actuation Systems(Neurocomputing, 2016) Folgheraiter, MicheleThis paper presents a block oriented nonlinear dynamic model suitable for online identi cation.The model has the well known Hammerstein architecture where as a novelty the nonlinear static part is represented by a B-spline neural network (BSNN), and the linear static one is formalized by an auto regressive exogenous model (ARX). The model is suitable as a feed-forward control module in combination with a classical feedback controller to regulate velocity and position of pneumatic and hydraulic actuation systems which present non stationary nonlinear dynamics. The adaptation of both the linear and nonlinear parts is taking place simultaneously on a patterby- patter basis by applying a combination of error-driven learning rules and the recursive least squares method. This allows to decrease the amount of computation needed to identify the model's parameters and therefore makes the technique suitable for real time applications. The model was tested with a silver box benchmark and results show that the parameters are converging to a stable value after 1500 samples, equivalent to 7.5s of running time. The comparison with a pure ARX and BSNN model indicates a substantial improvement in terms of the RMS error, while the comparison with alternative non linear dynamic models like the NNOE and NNARX, having the same number of parameters but greater computational complexity, shows comparable performances.