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Power, Control and Computing Technologies (ICPC2T), Conference on

作者： Rania Bouzid Jyotindra Narayan Hassène Gritli RISC Laboratory (LR16ES07) Borj Cedria Higher Institute of Information and Communication Technologies University of Carthage Tunis Tunisia Department of Mechanical Engineering Mechatronics and Robotics Laboratory Indian Institute of Technology Guwahati Guwahati Assam India Laboratory of Robotics Informatics and Complex Systems (RISC Lab - LR16ES07) National Engineering School of Tunis University of Tunis EI Manar Le Belvédère Tunis Tunisia

This research delves into the evaluation of Artificial Neural Networks (ANNs) in tackling the intricate domain of inverse kinematics problems within the context of robotic systems. Compared to direct kinematics, inverse kinematics presents significant challenges, especially when dealing with different arm lengths, which can impact the system's overall functionality. In this study, we conduct a comprehensive comparative analysis to assess the performance of ANNs across various robotic arm lengths. Following the forward kinematics of a simple 2-DoF robotic manipulator, three distinct datasets (fixed step size, random step size, and sinusoidal step size) are generated for training, testing, and validation phases. For every dataset, three optimizers, namely Levenberg Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient, are considered with different hyperparameters to investigate the performance of the ANN models. Based on the validation of MSE values, ANN models with different hyperparameters are found to be a promising approach in estimating the inverse kinematic solutions for different arm lengths with data complexities. Such solutions can be further extended to higher DoF robotic solutions and save the computational burden of evaluating analytical solutions.

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Artificial Neural Networks for the Forward Kinematics of a SCARA Manipulator: A Comparative Study with Two Datasets

Artificial Neural Networks for the Forward Kinematics of a S...

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Emerging Technologies for Sustainability and Intelligent systems (ICETSIS), ASU International Conference in

作者： Rania Bouzid Jyotindra Narayan Hassène Gritli RISC Laboratory (LR16ES07) Higher Institute of Information and Communication Technologies University of Carthage Borj Cedria Tunis Tunisia Department of Mechanical Engineering Mechatronics and Robotics Laboratory Indian Institute of Technology Guwahati Guwahati Assam India Laboratory of Robotics Informatics and Complex Systems (RISC Lab - LR16ES07) National Engineering School of Tunis University of Tunis El Manar Le Belvédère Tunis Tunisia

In the field of robotics and automation, the precise determination of manipulator positions is a fundamental aspect with widespread applications. Despite the significance of analytical methods, solving the Forward Kinematics (FK) for Selective Compliance Assembly Robot Arm (SCARA) manipulator robots can be complex and computationally intensive. Our research introduces a method to address the FK problem for the four degrees of freedom (4-DoF) SCARA manipulator through Artificial Neural Networks (ANNs). We utilize two different generated datasets, one with a fixed step size and another one based on a sinusoidal signal, for training the ANNs. This choice of datasets allows us to assess the ANN s' generalization capabilities across a range of operating conditions. Thereafter, the study is planned to investigate the ANN's performance while employing three distinct training algorithms: Levenberg-Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG). Through a comprehensive comparison of the performance of different ANN models, different training algorithms, and the two adopted generated datasets, this study provides valuable insights into selecting the most optimal training configurations for the SCARA robot's FK solutions. We employ the Mean Squared Error (MSE) and the error histogram as two performance metrics to assess the accuracy of various ANNs. The results reveal that optimal MSE outcomes were obtained when utilizing a balanced architecture with three hidden layers in the analysis of the sinusoidal-signal-based datasets.

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Multi-Class Gait Phase Recognition using Machine Learning Models with Two Training Methods

Multi-Class Gait Phase Recognition using Machine Learning Mo...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Amal Mekni Jyotindra Narayan Hassène Gritli RISC Laboratory (LR16ES07) Borj Cedria Higher Institute of Information and Communication Technologies University of Carthage Tunis Tunisia Department of Mechanical Engineering Mechtronics and Robotics Laboratory Indian Institute of Technology Guwahati Guwahati Assam India Laboratory of Robotics Informatics and Complex Systems RISC Lab - LR16ES07) National Engineering School of Tunis University of Tunis El Manar Le Belvédère Tunis Tunisia

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Walking is a fundamental human activity, yet understanding its intricacies is crucial for diagnosing and treating various gait abnormalities and musculoskeletal disorders. This study investigates the machine learning-based classification of gait phases into five subphases with two training methods. Using data from 100 individuals obtained from an open-source platform. The gait cycle was divided into the following subphases: Initial Contact, Loading Response, Mid Stance, Terminal Stance, and Pre-Swing. In the first approach, a stratified random sampling method was employed, allocating 80% of data from each subphase for training and 20% for testing. The second method involved training with data from 80% participants and testing with data from 20% participants. Out of different ML algorithms, the Random Forest (RF) achieves the highest performance (99.2%) with similar results across both methods. The k-Nearest Neighbors (k-NN), the Logistic Regression (LR), the Decision Tree (DT), and the Support Vector Machine (SVM) show comparable performance ($\sim 91 \%$) with marginal improvement with the second method. Naive Bayes (NB) has a significantly lower performance ($\sim 77.7 \%$). These findings suggest that RF is a robust and effective algorithm for this task. Furthermore, the study indicates that applying machine learning techniques to datasets divided by subjects presents promising opportunities for creating precise gait analysis systems. This can assist clinicians and researchers in enhancing patient care and gaining deeper insights into human locomotion.

关键词： Training Support vector machines Radio frequency Musculoskeletal system Logistic regression Machine learning algorithms Nearest neighbor methods Sampling methods Random forests Testing

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A distributed approach to the Optimal Power Flow problem for unbalanced and mesh networks ⁎

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IFAC-PapersOnLine 2020年第2期53卷 13287-13292页

作者： Giulio Ferro Michela Robba David D’Achiardi Rabab Haider Anuradha M. Annaswamy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge MA 02141 USA

In the present paper we introduce a new distributed optimization approach to the solution of general Optimal Power Flow problem for unbalanced and mesh networks. A new convex formulation, based on McCormick Envelopes, is proposed with a decomposition profile and a distributed approach based on proximal coordination. The resulting algorithm is shown to converge with a rate of o (1/τ), where τ is the number of iterations. The approach is validated on a modified IEEE 13 bus network, with added distributed energy resources including distributed generation and demand response.

关键词： Smart grids modeling of power systems optimal operation control of power systems optimization control of large-scale network systems decentralized distributed control

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Robust Stabilization of a Knee Exoskeleton Robot Using an LMI-Based Composite Controller

Robust Stabilization of a Knee Exoskeleton Robot Using an LM...

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Indian Control Conference (ICC)

作者： Sahar Jenhani Hassène Gritli Jyotindra Narayan Laboratory of Robotics Informatics and Complex Systems (RISC Lab - LR16ES07) National Engineering School of Tunis University of Tunis El Manar Tunis Tunisia RISC Lab. Higher Institute of Information and Communication Technologies University of Carthage Borj Cedria Tunis Tunisia Department of Mechanical Engineering Thapar Institute of Engineering and Technology Patiala Punjab India Department of Computing Imperial College London UK

ISBN: (数字)9798331517212

ISBN: (纸本)9798331517229

Rehabilitation exoskeleton robots are vital for restoring lower limb functionality in individuals with locomotor disorders. Extensive research has focused on optimizing the control of these robotic systems and make them more stable. However, there is still a considerable gap in developing robust controllers. This work introduces a robust control approach tailored for a one-degree-of-freedom (1-DoF) knee rehabilitation exoskeleton robot. Emphasizing position control, our method addresses challenges such as parameter uncertainties, solid and viscous frictions, external disturbances, and motion constraints. To effectively manage these issues, we propose a composite controller that integrates a linear state-feedback controller and a nonlinear control law. By employing a quadratic Lyapunov function, we derive the expression of the nonlinear controller and establish the LMI conditions required for calculating the matrix gain of the composite controller using two different design methods. To confirm the developed LMI conditions and demonstrate the proposed control method's ability to ensure robust position control of the knee exoskeleton robotic system, simulation results are finally provided.

关键词： Robust control Uncertain systems Limbs Friction Exoskeletons Position control Solids systems simulation Robots Lyapunov methods

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Preface

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2022年 13196 LNAI卷 v-vi页

作者： Bandini, Stefania Gasparini, Francesca Mascardi, Viviana Palmonari, Matteo Vizzari, Giuseppe Department of Informatics Systems and Communication University of Milano-Bicocca Milan Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genova Italy

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Observability Analysis and Reduced-Order Observer Design for a Super-Coiled Polymer-Driven Robotic Eye

Observability Analysis and Reduced-Order Observer Design for...

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IEEE Conference on Decision and Control

作者： Sunil Kumar Rajendran Qi Wei Ningshi Yao Feitian Zhang Department of Electrical and Computer Engineering George Mason University Fairfax VA USA Department of Bioengineering George Mason University Fairfax VA USA Department of Advanced Manufacturing and Robotics and the State Key Laboratory of Turbulence and Complex Systems College of Engineering Peking University Beijing China

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

With the aid of a robotic eye platform, several ocular motor disorders such as strabismus can be studied by ophthalmologists and biomedical researchers to better under-stand the biomechanisms of the human eye. Our previous work modeled a 2-DOF robotic eye driven by Super-Coiled Polymer (SCP) artificial muscles and presented a Deep Deterministic Policy Gradient (DDPG) learning-based controller. The control policy requires access to the full system states that include the orientation of the robotic eye and temperature changes of the SCPs. While the angular orientations of the robotic eye can be determined using embedded sensors or image-processing of the visual feed, it is quite laborious and expensive to measure the temperatures of the slender SCP muscles without affecting robot dynamics. To address this problem, this paper designs a linear reduced-order state observer based on the linearization of the nonlinear dynamics of the robotic eye. The linearized model is analytically shown to be fully observable along any trajectory within the operation range. To quantify the local observability of the dynamical system, local unobservability indices and local estimation condition numbers are determined along trajectories of the system states. The performance of the designed observer is tested through simulation in both open-loop and closed-loop foveation control of the robotic eye.

关键词： Temperature sensors Temperature measurement Visualization Observers Robot sensing systems Trajectory Nonlinear dynamical systems

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Crisis Resource Management in Emergency Medicine: an Italian medical students' experience 10

Crisis Resource Management in Emergency Medicine: an Italian...

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10th International Workshop on Innovative Simulation for Health Care, IWISH 2021

作者： Calandrino, Andrea Riitano, Alessio Ricci, Serena Dibenedetto, Silvia Vento, Fabio Torre, Giancarlo Chirico, Marco Borgonovo, Giacomo University of Genova Via Pastore 3 Genova16132 Italy IRCCS Giannina Gaslini Children's Hospital Via Gerolamo Gaslini 5 Genova16147 Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Via all'Opera Pia 13 Genova16145 Italy Department of Surgical Sciences and Integrated Diagnostics University of Genova Viale Benedetto XV 6 Genova16132 Italy

ISBN: (纸本)9788885741652

Crisis Resource Management (CRM) is a training technique which follows the procedures used for improving aviation safety. CRM can be used in many contexts, including emergency medicine training. This technique includes three steps: presentation of the problem, high fidelity simulation and debriefing. In particular, CRM carefully analyzes behavioral factors related to the management of critical events. This way, trainees and instructors can discuss and improve management and leadership. In fact, key factors of CRM are communication, roles management and priority definition. In order to apply CRM in Emergency Medicine, the Italian Medical Students' Secretariat organized the Emergency Medicine Simulation Seminary (EMSS). At the end of EMSS, we asked the 26 participants to give their feedback on the acceptance level of such medical training to assess the possibility of proposing CRM to other institutions as a method for emergency medicine training. Results show that Italian students have less simulation experience than international attendees. Also, CRM is a powerful training tool to rapidly improve knowledge on critical cases management;particularly if simulations are presented in high-fidelity settings by experienced instructors and with a proper training time to investigate, rehearse and fully understand the concepts. © 2021 The Authors.

关键词： Medical education

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Social Tasks in a Spatial Augmented Training for the Embodiment of Prosthetic Lower Limbs

Social Tasks in a Spatial Augmented Training for the Embodim...

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IEEE/RAS-EMBS International Conference on Biomedical robotics and Biomechatronics (BioRob)

作者： Giulia Mariani Chiara Dominici Federico Tessari Marco Freddolini Simone Traverso Samuele De Giuseppe Andrea Cherubini Emanuele Gruppioni Lorenzo De Michieli Carlo Ferraresi Matteo Laffranchi Giacinto Barresi Department of Informatics Bioengineering Robotics and Systems Engineering Università degli Studi di Genova Genoa Italy Rehab Technologies Lab at Istituto Italiano di Tecnologia Genova Italy Department of Mechanical and Aerospace Engineering Politecnico di Torino Turin Italy Newman Laboratory for Biomechanics Human Rehabilitation Massachusetts Institute of Technology Cambridge MA USA Open University Affiliated Research Center at Istituto Italiano di Tecnologia (ARC@IIT) Genova Italy Centro Protesi INAIL Istituto Nazionale per l'Assicurazione contro gli Infortuni sul Lavoro Bologna Italy

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Despite the proven advantages of prostheses, a significant number of amputees discontinue their usage for multiple reasons. Enhancing the prosthetic embodiment could be a strategy to improve the acceptance of such devices. Current literature suggests that virtual and augmented reality technologies offer promising solutions to enhance the embodiment of artificial limbs. This study delves into the potential of a Spatial Augmented Reality (SAR) system in fostering prosthesis integration and engaging individuals with transfemoral amputations. In this paper, participants use electromyography (EMG) to control a virtual leg. By contracting the quadriceps, they are tasked to break a virtual wall. Each collision generates vibrational feedback on the thigh. The study incorporates three conditions, featuring both social (cooperative and competitive) and non-social interaction. The underlying hypothesis posits that alternating between these conditions can concurrently stimulate embodiment through sustained engagement, particularly when social elements are introduced. Data collection encompasses EMG signals, heart rate variability, and skin conductance. Comprehensive examination, involving subjective questionnaires and assessments of proprioceptive drift, seeks to clarify the intricacies of embodiment and engagement throughout the tests. Results from tests alternating social and non-social conditions prove satisfactory, underscoring the approach's potential to enhance subject engagement and facilitate the prostheses' embodiment. This study thus provides insights for future investigations on prosthetic integration for amputees.

关键词： Training Legged locomotion Electric potential Spatial augmented reality Propioception Thigh Electromyography Skin Heart rate variability Robots

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Improving Obstacle Awareness to Enhance Interaction in Virtual Reality

Improving Obstacle Awareness to Enhance Interaction in Virtu...

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IEEE Annual International Symposium Virtual Reality

作者： Ivan Valentini Giorgio Ballestin Chiara Bassano Fabio Solari Manuela Chessa Dept. of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Italy

ISBN: (数字)9781728156088

ISBN: (纸本)9781728156095

Currently, immersive virtual reality is experienced through head-mounted displays while the user is physically present into a real, cluttered environment. This causes the problem of avoiding dangerous collisions with obstacles in the real environment that are invisible to the user, and also hampers the interaction with real objects. Following the augmented virtuality paradigm, these obstacles should be embedded into the virtual environment. Thus, there is the need of knowing the 3D structure of the real environment to align it with the virtual one. In this paper, we present a method to create a virtual scenario composed of virtual objects having the same spatial occupancy of the corresponding real ones. The real scene is scanned to detect the position and bounding box of objects and obstacles, then virtual elements having similar spatial properties are added to the virtual scene. Two different real environment structure detection and clustering techniques are described and compared, both quantitatively and by considering users’ sense of presence with respect to the standard Chaperone technique. Our results show that the method is a good solution to maintain the real environment awareness while keeping an high level of immersivity and sense of presence.

关键词： Three-dimensional displays Virtual environments Solid modeling Computational modeling Transmission line matrix methods Resists

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