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Saratov Fall Meeting 2021: Computational Biophysics and Nanobiophotonics

作者： Andreev, Andrey V. Maksimenko, Vladimir A. Neuroscience and Cognivite Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Universitetskaya Str. 1 Innopolis 420500 Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University A. Nevskogo ul. 14 Kaliningrad236016 Russia

ISBN: (数字)9781510653702

ISBN: (纸本)9781510653696

We consider a network of networks consisting of small input neural network and four small-world subnetworks Hodgkin-Huxley neurons. Input network receives an external signal and transfers it to subnetworks via excita-tory couplings while the subnets interact with each other via inhibitory couplings. We show that the subnets are divided into 2 clusters under the influence of an inhibitory couplings between them. The synchronization indexes of subnetworks periodically change in time. We found that SIs can oscillate either in-phase or anti-phase depending on the couplings between subnetworks. © 2022 SPIE.

关键词： Complex networks

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Sensor Data Compression for the Normal Reaction Predictor Training for Walking Robots 4

Sensor Data Compression for the Normal Reaction Predictor Tr...

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4th Scientific School on Dynamics of Complex Networks and their Application in Intellectual robotics, DCNAIR 2020

作者： Salikhzyanov, Alek Savin, Sergei Vorochaeva, Lyudmila Innopolis University Laboratory of Mechatronics Control and Prototyping Russia Southwest State University Department of Mechanics Mechatronics and Robotics Kursk Russia

ISBN: (纸本)9781728172866

In this paper, the problem of compression and feature extraction for walking robot sensor data is studied. The aim is to facilitate the process of building a machine learning-based predictor for the ground reaction forces, which should allow to simplify generating control systems for walking robots, capable of working in changing contact interaction scenarios. In particular, the paper proposes use of undercomplete and overcomplete autoencoders for an automated feature extraction, with the aim of using the encoders as a preprocessing step in the machine learning (ML)-based ground reaction predictor, implemented by solving a regression problem. Papers shows trends in the mean squared error (MSE) on reconstruction tasks for different depths and structures of the encoders, demonstrates the trade-off between complexity and the quality of the reconstruction. © 2020 IEEE.

关键词： Feature extraction

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Study of the Automated Process-Induced Residual Stresses in Multi-Axis Laminates

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Procedia Structural Integrity 2022年 42卷 1328-1335页

作者： Mohamed M.A. Ammar Bijan Shirinzadeh Robotics and Mechatronics Research Laboratory Department of Mechanical and Aerospace Engineering Monash University Clayton 3800 Australia Department of Mechanical Engineering Faculty of Engineering Alexandria University Alexandria 11432 Egypt

The integrity of the composite structures is affected by the manufacturing conditions. Controlling the ply orientation during the manufacturing process plays an important role in defining the induced residual stresses inside the laminate. This work evaluates the generated residual stresses inside multi-axis laminates of carbon fiber reinforced polymers. The samples are manufactured using the robotic fiber placement technique. The residual stresses are measured through the incremental hole-drilling method. Finite element modeling is used to provide the calibration process of the strain measurements. Additionally, microstructure analyses have been performed to show the configuration of the manufactured samples. The effect of the laminate configurations and plies orientations are investigated and compared. The layers’ orientations have shown a considerable effect on the generated stresses.

关键词： Multi-axis composites Residual stresses Carbon fiber Robotic fiber placement

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Optimization Approach on the Topology Structure of Beam-Based Flexural Pivots with Arbitrary Center of Rotation

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SSRN 2024年

作者： Zhang, Zhaowei Pieber, Michael Gerstmayr, Johannes State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Nanta Street 114 Liaoning Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences No.135 Chuangxin Rd Liaoning Shenyang110169 China Department of Mechatronics University of Innsbruck Technikerstraße 13 Innsbruck6020 Austria

Beam-based flexural pivots are widely adopted as compliant mechanisms due to their ideal design for achieving low torsional stiffness and high translational stiffness. Usually, the flexural hinges are optimized for a specific application in compliant mechanisms. However, designing flexure hinges with an arbitrary center of rotation presents a significant challenge, especially when aiming to accommodate large deformations. Addressing this, we introduce an approach to optimize the topology structure of beam-based flexural pivots undergoing large deformation, aiming to achieve an optimized configuration with an arbitrary center of rotation. Both the stiffness based objective function and strain energy based objective function are introduced. For the implementation, a geometrically exact beam element is utilized to establish a dual-layer ground structure for optimization. A genetic algorithm is employed to identify optimal configurations for flexural hinges, including traditional notch hinges and cross-spring pivots. Furthermore, how different objective functions and their corresponding parameters influence the optimized configurations are examined. Ultimately, using three examples with an arbitrary center of rotation, this approach achieves optimal design results. The results demonstrate that this approach can serve as a foundation for designing compliant mechanisms with an arbitrary center of rotation. © 2024, The Authors. All rights reserved.

关键词： Genetic algorithms

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Robotic Learning of Haptic Skills from Expert Demonstration for Contact-Rich Manufacturing Tasks

Robotic Learning of Haptic Skills from Expert Demonstration ...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Sara Hamdan Yusuf Aydin Erhan Oztop Cagatay Basdogan Robotics and Mechatronics Laboratory and the KUIS-AI Center Koc University Istanbul Turkey Department of Electrical and Electronics Engineering MEF University Istanbul Turkey Department of Computer Science Ozyegin University Istanbul Turkey SISReC/OTRI Osaka University Japan

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

We propose a learning from demonstration (LfD) approach that utilizes an interaction (admittance) controller and two force sensors for the robot to learn the force applied by an expert from demonstrations in contact-rich tasks such as robotic polishing. Our goal is to equip the robot with the haptic expertise of an expert by using a machine learning (ML) approach while providing the flexibility for the user to intervene in the task at any point when necessary by using an interaction controller. The utilization of two force sensors, a pivotal concept in this study, allows us to gather environmental data crucial for effectively training our system to accommodate workpieces with diverse material and surface properties and maintain the contact of polisher with their surfaces. In the demonstration phase of our approach where an expert guiding the robot to perform a polishing task, we record the force applied by the human (F h ) and the interaction force (F int ) via two separate force sensors for the polishing trajectory followed by the expert to extract information about the environment (F env =F h −F int ). An admittance controller, which takes the interaction force as the input is used to output a reference velocity to be tracked by the internal motion controller (PID) of the robot to regulate the interactions between the polisher and the surface of a workpiece. A multilayer perceptron (MLP) model was trained to learn the human force profile based on the inputs of Cartesian position and velocity of the polisher, environmental force (F env ), and friction coefficient between the polisher and the surface to the model. During the deployment phase, in which the robot executes the task autonomously, the human force estimated by our system $\left({{{\hat F}_h}}\right)$ is utilized to balance the reaction forces coming from the environment and calculate the force $\left({{{\hat F}_h} - {F_{env}}}\right)$ needs to be inputted to the admittance controller to gene

关键词： Surface cleaning Force Robot sensing systems Real-time systems Haptic interfaces Trajectory Manufacturing Force sensors Admittance Robots

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Machine Learning Approach for Tensegrity Form Finding: Feature Extraction Problem 4

Machine Learning Approach for Tensegrity Form Finding: Featu...

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4th Scientific School on Dynamics of Complex Networks and their Application in Intellectual robotics, DCNAIR 2020

作者： Zalyaev, Eduard Savin, Sergei Vorochaeva, Lyudmila Innopolis University Laboratory of Mechatronics Control and Prototyping Innopolis Russia Southwest State University Department of Mechanics Mechatronics and Robotics Kursk Russia

ISBN: (纸本)9781728172866

In this paper, the problem of form finding for a tensegrity structure with the use of a machine learning pipeline, including feature extraction and regression, is studied. Tensegrity robots present a range of new opportunities in a number of areas in robotics, however the lack of efficient and scalable tools for controlling their motion slows down their deployment. This study aims to add a new tool for solving one of the basic problems encountered in tensegrity robot control: Finding a stable equilibrium state (and its inverse task). Using machine learning approach makes the solution scalable and potentially faster than iterative optimization-based methods. The paper provides a view into the issue of feature extraction, and provides implementation of a number of feature extraction methods: Principle Component Analysis (PCA), Kernel PCA, undercomplete, denoising and sparse autoencoders © 2020 IEEE.

关键词： Feature extraction

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Biomarkers of brain activity for the performance evaluation in the process of solving cognitive tasks 5

Biomarkers of brain activity for the performance evaluation ...

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5th Scientific School on Dynamics of Complex Networks and their Applications, DCNA 2021

作者： Badarin, Artem Smirnov, Nikita Kurkin, Semen Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University Kaliningrad Russia

ISBN: (纸本)9781665442824

In this paper, we analyze the brain activity during the execution by the subject of simple cognitive tasks associated with visual attention and symbol perception. We obtain biomarkers of brain activity in the process of solving cognitive tasks. These biomarkers make it possible to relate the quality and speed of execution of the tasks offered to the subject with the brain’s activity of the cortical network. We use a combination of time-frequency and statistical analysis to calculate activity characteristics. The results obtained can be used to develop neural interfaces for training attention and adjusting the learning process. © 2021 IEEE

关键词： Biomarkers

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Investigating Neural Network Hyperparameter Variations in Robotic Arm Inverse Kinematics for Different Arm Lengths

Investigating Neural Network Hyperparameter Variations in Ro...

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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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Modelling of the Exoskeleton Control System with a Linear Gravity Compensator

Modelling of the Exoskeleton Control System with a Linear Gr...

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2020 International Russian Automation Conference, RusAutoCon 2020

作者： Yatsun, Andrey Jatsun, Sergey South-West State University Laboratory of Robotics and Mechatronics Kursk Russia South-West State University Mechatronics Department Kursk Russia

ISBN: (纸本)9781728161303

This paper presents investigation results in a field of industrial exoskeleton modelling. Integrated human-machine system with an exoskeleton in the article is shown as the biotechnical walking system. General scheme of the exoskeleton with the combined linear gravity compensator system and block diagram of HMI are presented. The consideration of the combined type linear gravity compensator, which uses the positive properties of the passive and active compensators is shown. Solution algorithm which allows for designing such a HMI that will ensure the most efficient operation of the walking system is listed. Mathematical model of the load lifting for the system includes an exoskeleton, a human, and the HMI and results of numerical simulation are shown. It is figured out that the use of linear gravity compensators significantly increases the efficiency of the exoskeleton. © 2020 IEEE.

关键词： Numerical models

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Generalized Optimization Approach to Design In-Plane Distributed Compliant Remote Center of Motion Mechanism

SSRN

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SSRN 2024年

Remote center of motion (RCM) mechanisms are widely used because their center of rotation is outside the mechanical device. Usually, compliant RCM mechanisms use a linkage-based design with flexure hinges to achieve relative motion. It is still an open question to design a distributed compliant RCM mechanism using flexural beams. Addressing this, the paper proposes a generalized optimization approach for the design. The optimization approach is implemented in two steps. First, we use beams to establish a dual-layer ground structure. Using a genetic algorithm and considering the relative density of beams as variables, we obtain the optimized topology. Second, based on the topology and employing curved beams for size-shape optimization, we achieve optimized distributed compliant RCM mechanisms. Based on this approach, we explore and identify four distinct topologies and four detailed distributed compliant RCM mechanisms. With the comparison of stiffnesses and rotational axis shift, two kinds of optimized distributed compliant RCM mechanisms are considered. For verification, commercial finite element method software ABAQUS were used, demonstrating excellent alignment. Ultimately, this approach can be generalized for optimizing distributed compliant RCM mechanisms. © 2024, The Authors. All rights reserved.

关键词： Topology

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