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

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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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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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Detecting Changes in Contact Interaction Regime with a Reaction Predictor and a Linear Contact Model 3

Detecting Changes in Contact Interaction Regime with a React...

引用

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

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

ISBN: (纸本)9781728124704

This paper is focused on a problem of using reaction predictors in the control pipeline of walking robots. The use of such predictors has been proposed for bipedal robots and the main advantage of this proposal is that it allows to exclude unknown reaction forces from the dynamics equations, and hence from model-based control formulations for those robots. In this paper, an additional benefit of predictor-based control formulation is discussed, namely the possibility of using it to predict changes in the contact interaction scenario, such as slipping or take off from the supporting surface. © 2019 IEEE.

关键词： Mobile robots

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Numerical calculation of the Jacobian derivative

Numerical calculation of the Jacobian derivative

引用

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

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

ISBN: (纸本)9781665424066

The robot's Jacobian is an important tool since it allows to solve different problems, from calculating the trajectory of motion to modeling dynamics in task space. Here we demonstrate the algorithm that allows calculating the Jacobian and its time derivative simultaneously. The algorithm is focused on numerical computations but can be used to get the analytical expressions as well. © 2021 IEEE.

关键词： Manipulators

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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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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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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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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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