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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21-30 订阅

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Trajectory Planning for Biped Walk with Non-instantaneous Double Support Phase 3

Trajectory Planning for Biped Walk with Non-instantaneous Do...

引用

3rd School on Dynamics of Complex Networks and their Application in Intellectual robotics, DCNAIR 2019

作者： Khusainov, Ramil Mamedov, Shamil Dmitry, Popov Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia

ISBN: (纸本)9781728124704

In this paper we present a technique for finding periodic gaits for five-link planar bipedal robot with point feet. Unlike most works that are limited to the consideration of only underactuated single support phase (SSP) we show that this phase change condition is state dependant and extend our approach to the presence of double support phase (DSP). The concept of virtual holonomic constraints is used with full set of constraints during SSP and during SSP to DSP change. © 2019 IEEE.

关键词： Robot programming

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Obstacle avoidance for robotic manipulator using Mixed reality glasses 3

Obstacle avoidance for robotic manipulator using Mixed reali...

引用

3rd School on Dynamics of Complex Networks and their Application in Intellectual robotics, DCNAIR 2019

作者： Evlampev, A. Ostanin, M. Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia

ISBN: (纸本)9781728124704

The problem of finding the shortest path for manipulators with obstacle avoidance has many solutions. The paper presents the usage of well-known methods for solving that problem using mixed reality glasses. Glasses contain the Spatial mapping module, presented in the form of an continuously updating mesh. Analyzing space through mixed reality glasses, we have developed algorithms for obtaining the shortest path in the Cartesian and Joint spaces. In the beginning, we implement A∗ simple algorithm and then realized the improved RRT algorithm. The approaches were verified using Unity, Microsoft HoloLens and 6 DOF robot UR10e. © 2019 IEEE.

关键词： Motion planning

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Numerical Optimisation-Based Control Pipeline for Robot Arm for Machine Learning Experiments

Numerical Optimisation-Based Control Pipeline for Robot Arm ...

引用

2019 International Russian Automation Conference, RusAutoCon 2019

作者： Savin, Sergei Golousov, Svyatoslav Ivanov, Mikhail Klimchik, Alexander Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia

ISBN: (纸本)9781728102658

In this paper, a control pipeline for an industrial robot arm is presented. The robot is proposed as a test bench for machine learning experiments, where it is important to have a reliable and repeatable behavior of the robot while its control system needs to be flexible. The proposed controller is model-based, and is implemented as a quadratic program, imitating feedback linearization and computed torque controller with stable control error dynamics. The paper discusses practical aspects of the software suit developed for the project and presents a favorable set of the third party packages which allowed to create a fast and reliable implementation of the proposed control scheme. The properties of the proposed control were studied using simulation in the Gazebo environment. © 2019 IEEE.

关键词： Quadratic programming

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

Traffic Sign Classification Using Embedding Learning Approac...

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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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Multi-collision Detection for Collaborative Robot 3

Multi-collision Detection for Collaborative Robot

引用

3rd School on Dynamics of Complex Networks and their Application in Intellectual robotics, DCNAIR 2019

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

ISBN: (纸本)9781728124704

This study is focused on the problem of robot physical interaction with environment. Physical interaction in case of collaborative industrial robots means one or more physical contact with robot and work piece, robot and another robot, robot and human, robot and environment. Depending on location on the robot surface where this contact or collision happened, robot reaction should be different in order to provide safety and increase work cell performance if possible. In this work, algorithm for multiple contact point identification is presented. The algorithm is capable of running with 100 Hz loop and 1.09 cm RMSE. The approach was tested in the simulation with KUKA iiwa but can be extended to any robot with torque sensing capabilities. © 2019 IEEE.

关键词： Industrial robots

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Application of CycleGAN-based Augmentation for Autonomous Driving at Night

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

引用

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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State-based velocity profile for manipulator 3

State-based velocity profile for manipulator

引用

3rd School on Dynamics of Complex Networks and their Application in Intellectual robotics, DCNAIR 2019

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

ISBN: (纸本)9781728124704

The study is focused on velocity control of a manipulator motion in the case when the precise execution of commands cannot be guaranteed because of the imperfection of the control system or interaction with the robot environment. The proposed approach combines the real-time state estimation with a trapezoidal velocity profile to optimize the performance of the linear Cartesian and point-to-point motions. © 2019 IEEE.

关键词： Robot applications

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Robot description formats and approaches: Review

Robot description formats and approaches: Review

引用

2021 International Conference "Nonlinearity, Information and robotics", NIR 2021

作者： Ivanou, Mikhail Mikhel, Stanislav Savin, Sergei Innopolis University Center for Technologies in Robotics and Mechatronics Components Innopolis Russia

ISBN: (纸本)9781665424066

Modern robotics represents a collaboration between industry and scientific community, with interdisciplinary research being at its forefront. This leads to wide variation in the requirements for robotic systems, which encourages the development and use of different description formats. Currently, some of the existing formats are gaining wide recognition;at the same time, their individual limitations become more apparent. The goal of this work is to generalize the idea of a robot description format, putting together different approaches to representing a robot, including formats that rely on human-readable and binary data representation, formats tied to specific software, and ones that exist independently, and others. We outline the main advantages and limitations of the existing formats, with the aim to make the following efforts at standardization or development more informed. © 2021 IEEE.

关键词： Robots

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Convex Optimization-based Motion Planner for Linear Variable Stiffness Actuators 2

Convex Optimization-based Motion Planner for Linear Variable...

引用

2nd International Conference on Control and Robot Technology, ICCRT 2019

作者： Savin, Sergei Golousov, Svyatoslav Klimchik, Alexandr Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia

ISBN: (纸本)9781450372527

This paper is focused on control of variable stiffness actuators (VSA). One of the new approaches in this area is based on a linear VSA model, whose main features are the directly controlled linear stiffness dynamics and bilinear output torque model. The paper tackles the problem of motion planning for linear VSA. While the problem appears to be non-convex, the study demonstrates that it is possible to introduce a new parametrization of the model that produces a convex optimization formulation. It is proposed to limit the power input allowed for the controller, making sure that it does not exceed the physical capabilities of the motors implementing the trajectories. The paper shows the behavior of the planer when it reaches the limits of the allowed power input. The paper demonstrates the use of the proposed planner in planning trajectories for a lower-limb exoskeleton performing sit-to-stand transfer. © 2019 ACM.

关键词： Convex optimization

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