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检索条件"机构=Center for technologies in Robotics and Mechatronics components"

共 242 条记录，以下是41-50 订阅

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Algebraic approach to the stiffness model reduction for manipulators with double encoders

Algebraic approach to the stiffness model reduction for mani...

引用

2020 International Conference Nonlinearity, Information and robotics, NIR 2020

作者： Mikhel, Stanislav Klimchik, Alexandr Innopolis University Center for Technologies in Robotics and Mechatronics Components Innopolis Russia

ISBN: (纸本)9781728187631

The paper is focused on the problem of the stiffness model estimation that could be used in combination with double encoders for the detection of the compliance errors and applied forces. Algebraic approach is proposed based on knowledge of link stiffness parameters and kinematics of the robot. The application of this technique is demonstrated in the example of a 3-link manipulator. © 2020 IEEE.

关键词： Stiffness

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Transfer Learning for Collision Localization in Collaborative robotics 2020

Transfer Learning for Collision Localization in Collaborativ...

引用

3rd International Conference on Applications of Intelligent Systems, APPIS 2020

作者： Popov, Dmitry Klimchik, Alexandr Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia

ISBN: (纸本)9781450376303

In this work, the transfer learning paradigm is used to improve the performance of neural networks. These networks estimate the collision point between the robot and an external object, using two different approaches. One of them uses a neural network to predict collision point on the internal robot axis and the second uses classification in order to find a collision in points which are sampled on the robot surface. The neural networks are trained on two datasets, one dataset is generated in simulation, the other one captured from the real robot. Obtained results show, that using a pre-trained network allows to greatly increase the overall accuracy of collision localization. © 2020 ACM.

关键词： Collaborative robots

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Analysis of muscle activation patterns during equilibrium seeking activity

Analysis of muscle activation patterns during equilibrium se...

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Saratov Fall Meeting 2020 - Computations and Data Analysis: From Molecular Processes to Brain Functions

作者： Khorev, Vladimir S. Grubov, Vadim V. Badarin, Artem A. Kurkin, Semen A. Neuroscience and Cognitive Technology Lab Center for Technologies in Robotics and Mechatronics Components Innopolis University 1 Universitetskaya Str Innopolis420500 Russia

ISBN: (数字)9781510645332

ISBN: (纸本)9781510645325

The main goal of this project was to identify the patterns of muscular activity of a person in the process of his interaction with the environment, as well as to identify mechanisms that make it possible to adapt behavior in response to changing external conditions. For this, we conducted a series of experiments with subjects placed in an unstable state. We carried out statistical analysis for the received signals of muscle activity. Based on the results of the analysis of behavioral characteristics, we revealed positive dynamics when subjects were reaching a state of balance and a pattern associated with training. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Signal analysis

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Continuous Control in Deep Reinforcement Learning with Direct Policy Derivation from Q Network 2nd

Continuous Control in Deep Reinforcement Learning with Direc...

引用

2nd International Conference on Human Interaction and Emerging technologies: Future Applications, IHIET-AI 2020

作者： Akhmetzyanov, Aydar Yagfarov, Rauf Gafurov, Salimzhan Ostanin, Mikhail Klimchik, Alexandr Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia

ISBN: (纸本)9783030442668

The reinforcement learning approach allows learning desired control policy in different environments without explicitly providing system dynamics. A model-free deep Q-learning algorithm is proven to be efficient on a large set of discrete-action tasks. Extension of this method to the continuous control task usually solved with actor-critic methods which approximate a policy function with additional actor network and uses Q function to speed up policy network training. Another approach is to discretize action space which will not give a smooth policy and is not applicable for large action spaces. A direct continuous policy derivation from the Q network leads to optimization of action on each inference and training step which is not efficient but provides optimal and continuous action. Time-efficient Q function input optimization is required in order to apply this method in practice. In this work, we implement efficient action derivation method which allows using Q-learning in real-time continuous control tasks. In addition, we test our algorithm on robotics control tasks from robotics gym environments and compare this method with modern continuous RL methods. The results have shown that in some cases proposed approach learns smooth continuous policy keeping the implementation simplicity of the original discreet action space Q-learning algorithm. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.

关键词： Reinforcement learning

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Application of CycleGAN-based Augmentation for Autonomous Driving at Night

Application of CycleGAN-based Augmentation for Autonomous Dr...

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2020 International Conference Nonlinearity, Information and robotics, NIR 2020

作者： Ostankovich, Vladislav Yagfarov, Rauf Rassabin, Maksim Gafurov, Salimzhan Innopolis University Center for Technologies in Robotics and Mechatronics Components Innopolis Russia

ISBN: (纸本)9781728187631

Self-driving vehicles contain a number of modules allowing them to autonomously navigate in uncertain environment. The robust, efficient, safe and accurate autonomous navigation are heavily depend on parameters of a perception module. In this paper, we consider perception module as a combination of object detection and road segmentation submodules. As a matter of fact, all of them are based on Deep learning technique. It leads to liability of a big training datasets to provide the accuracy, efficiency and robustness of a perception module for a self-driving car operating in a wide range of scenarios. This paper presents the GAN-based augmentation as a key factor allowing to improve the performances of perception. The provided research shows the comparison between classical augmentation method and CycleGAN-based method. The main focus is made on detection and segmentation problems at nights. The initial training data includes BDD100K dataset and our own one collected in winter time by means of front-view camera of a self-driving car developed in Innopolis University. The obtained results show the improvement of segmentation task in case of application of CycleGAN augmentation. However, the chosen method of GAN-based augmentation has not shown the positive influence on object detection due to appeared visual artifacts. © 2020 IEEE.

关键词： Autonomous vehicles

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Traffic Sign Classification Using Embedding Learning Approach for Self-driving Cars 2nd

Traffic Sign Classification Using Embedding Learning Approac...

引用

2nd International Conference on Human Interaction and Emerging technologies: Future Applications, IHIET-AI 2020

作者： Yagfarov, Rauf Ostankovich, Vladislav Akhmetzyanov, Aydar Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia

ISBN: (纸本)9783030442668

Image classification is one of the most popular and important problems in computer vision. In self-driving cars image classification is used to classify detected traffic signs. Modern state-of-the-art algorithms based on deep neural networks use softmax function to interpret the output of the network as the probability that the input data belongs to a certain class. This approach works well, however it has several disadvantages. More precisely, it is necessary to know the number of classes in advance, and if one wants to add a new class, then it is necessary to retrain the network. Moreover, a large number of images of each class are required. In the case of road signs, datasets may contain only the most frequent signs while ignoring rarely used ones. Thus, the traffic signs recognition module in autonomous cars will not recognize traffic signs not included into training dataset, which can lead to accidents. In this paper we put forward another approach that does not have disadvantages of networks with softmax. The approach is based on learning image embeddings in which models are trained to bring closer objects of one class and to move away objects of other classes in embeddings space. Therefore, having even a small number of images of rare classes it becomes possible to create a working classification system. In this work, we test the applicability of these algorithms in the traffic signs classification problem, and also compare its accuracy with neural networks with softmax and with networks pre-trained on softmax. We developed publicly available toolbox for training and testing embedding networks with different loss functions, backbone models, training strategies and other configuration parameters and embedding space visualization tools. All our experiments were carried out on the russian road signs dataset. To simplify the process of conducting training experiments, a framework for embedding learning based neural networks making was created. The framework can b

关键词： Embeddings

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Brain activity functional analysis based on the fMRI data in healthy subjects between Stroop and emotion tests 6

Brain activity functional analysis based on the fMRI data in...

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6th Scientific School "Dynamics of Complex Networks and their Applications", DCNA 2022

作者： Khorev, Vladimir Kurkin, Semen Paunova, Rositsa Simeonova, Denitsa Kandilarova, Sevdalina Stoyanov, Drozdstoy Innopolis University Center for Technologies in Robotics and Mechatronics Components Innopolis Russia Immanuel Kant Baltic Federal University Baltic Center for Artificial Intelligence and Neurotechnology Kaliningrad Russia Neurosciences Research Institute Samara State Medical University Samara Russia Plovdiv Medical University Plovdiv Bulgaria

ISBN: (数字)9781665461740

ISBN: (纸本)9781665461740

In this work, we conducted the analysis of the functional magnetic resonance imaging data in healthy subjects that were obtained during experimental research when subjects are performing Stroop and emotion picture tests. The signals that have undergone preprocessing and filtration were used to test the method of functional connectivity analysis to find the connections between the brain regions that demonstrate significant changes between functional tests. © 2022 IEEE.

关键词： Brain

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Synchronization in the inhibitory coupled Hodgkin-Huxley neural networks

Synchronization in the inhibitory coupled Hodgkin-Huxley neu...

引用

Saratov Fall Meeting 2020 - Computations and Data Analysis: From Molecular Processes to Brain Functions

作者： Andreev, Andrei V. Maksimenko, Vladimir A. Neuroscience and Cognivite Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Universitetskaya Str. 1 Innopolis420500 Russia

ISBN: (数字)9781510645332

ISBN: (纸本)9781510645325

We consider two small-world networks of Hodgkin-Huxley neurons interacting via inhibitory coupling. We found that synchronization indices (SI) in both networks oscillate periodically in time, so that time intervals of high SI alternate with time intervals of low SI. Depending on the coupling strength, the two coupled networks can be in the regime of either in-phase or anti-phase synchronization. We suppose that the inherent mechanism behind such a behavior lies in the cognitive resource redistribution between neuronal ensembles of the brain. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Synchronization

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Convolutional Neural Network and Adversarial Autoencoder in EEG images classification 5

Convolutional Neural Network and Adversarial Autoencoder in ...

引用

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

作者： Nasybullin, Albert Kurkin, Semen Faculty of Computer Science and Engineering Innopolis University Innopolis Russia Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia

ISBN: (纸本)9781665442824

In this paper, we consider applying computer vision algorithms for the classification problem one faces in neuroscience during EEG data analysis. Our approach is to apply a combination of computer vision and neural network methods to solve human brain activity classification problems during hand movement. We pre-processed raw EEG signals and generated 2D EEG topograms. Later, we developed supervised and semi-supervised neural networks to classify different motor cortex activities. © 2021 IEEE

关键词： Computer vision

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Unmanned Aerial Vehicle Path Planning for Exploration Mapping

Unmanned Aerial Vehicle Path Planning for Exploration Mappin...

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2020 International Conference Nonlinearity, Information and robotics, NIR 2020

作者： Respall, Victor Massague Devitt, Dmitry Fedorenko, Roman Innopolis University Center for Technologies in Robotics and Mechatronics Components Innopolis420500 Russia

ISBN: (纸本)9781728187631

This paper presents a new path planner for the exploration of previously unknown environments. The proposed algorithm uses a Next-Best-View (NBV) fashion to decide the movements ahead. A Rapidly-Exploring Random Tree (RRT) is built and the node with the highest gain is the next configuration to execute, based on the amount of unmapped volume in the field of view (FoV) of the sensor employed. The proposed planner is able to send online a 3D occupancy map of the volume and work with a variety of different depth sensors. The performance is analyzed in various challenging simulations, such as exploration of a city street or a narrow tunnel. Moreover, real experiments indoor and outdoor were conducted to further validate the proposed approach results. In cases with large scale volumes, it can cover a wide range in affordable flight time. © 2020 IEEE.

关键词： Motion planning

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