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IEEE NW Russia Young Researchers in Electrical and Electronic engineering Conference (EIConRusNW)

作者： Nikolay V. Lazovskiy Oleg A. Kundyukov Kirill K. Ershov Department of Robotics and Automation of Production Systems St. Petersburg state electrotechnical University “LETI” Saint Petersburg Russia Department of Automated Electric Drive and Electrical Engineering Saint Petersburg state University of industrial technologies and design Saint Petersburg Russia

The types of errors of machine tools are considered. Methods for increasing the productivity of machine tools and the accuracy of processing workpieces are analyzed. Promising directions for the development of electri...

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Azimuth Axis Electric Drives for Telescopes and Radio Telescopes: Review and Analysis

Azimuth Axis Electric Drives for Telescopes and Radio Telesc...

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IEEE NW Russia Young Researchers in Electrical and Electronic engineering Conference (EIConRusNW)

作者： Kirill K. Ershov Nikolay S. Blagodarnii Mikhail P. Belov Oleg A. Kundyukov Department of Automated Electric Drive and Electrical Engineering Saint Petersburg State University of Industrial Technologies and Design Saint-Petersburg Russian Federation Department of Robotics of Automated Production Systems Saint Petersburg Electrotechnical University “LETI” Saint-Petersburg Russian Federation

The article deals with the electric drives of telescopes and radio telescopes tracking systems. Requirements to electric drives of azimuthal axis of these mechanisms are given. Various kinematic schemes and types of electric motors used in these drives are considered. At the end the requirements to control systems for drives with different motors and kinematic schemes are formed.

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When and Where to Step: Terrain-Aware Real-Time Footstep Location and Timing Optimization for Bipedal Robots

SSRN

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SSRN 2023年

作者： Wang, Ke Hu, Zhaoyang Jacopo Tisnikar, Peter Helander, Oskar Chappell, Digby Kormushev, Petar Robot Intelligence Lab Dyson School of Design Engineering Imperial College London United Kingdom Department of Bioengineering Imperial College London United Kingdom Vision for Robotics Lab Department of Mechanical and Process Engineering ETH Zürich Switzerland

Online footstep planning is essential for bipedal walking robots, allowing them to walk in the presence of disturbances and sensory noise. Most of the literature on the topic has focused on optimizing the footstep placement while keeping the step timing constant. In this work, we introduce a footstep planner capable of optimizing footstep placement and step time online. The proposed planner, consisting of an Interior Point Optimizer (IPOPT) and an optimizer based on Augmented Lagrangian (AL) method with analytical gradient descent, solves the full dynamics of the Linear Inverted Pendulum (LIP) model in real time to optimize for footstep location as well as step timing at the rate of 200 *** show that such asynchronous real-time optimization with the AL method (ARTO-AL) provides the required robustness and speed for successful online footstep planning. Furthermore, ARTO-AL can be extended to plan footsteps in 3D, allowing terrain-aware footstep planning on uneven terrains. Compared to an algorithm with no footstep time adaptation, our proposed ARTO-AL demonstrates increased stability in simulated walking experiments as it can resist pushes on flat ground and on a 10◦ ramp up to 120 N and 100 N respectively. © 2023, The Authors. All rights reserved.

关键词： Motion planning

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A Modular End Effector with Active Rolling Fingertip for Picking Cloth-Like Objects 19

A Modular End Effector with Active Rolling Fingertip for Pic...

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19th IEEE International Conference on Automation Science and engineering, CASE 2023

作者： Zhang, Kun Yang, Yuanhang Chen, Zhiming Chen, Hua Wang, Michael Yu Zhang, Wei Robotics Institute Hong Kong University of Science and Technology Clear Water Bay Hong Kong School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Hkust Shenzhen-Hong Kong Collaborative Innovation Research Institute Futian Shenzhen China Monash University Department of Mechanical & Aerospace Engineering ClaytonVIC3800 Australia

ISBN: (纸本)9798350320695

Cloth-like items are ubiquitous in both daily life and industrial production, but their softness makes them difficult to manipulate. This paper focuses on solving the problem of automatic manipulation especially in the scenario of flat surface by introducing a novel end-effector mechanism. The gripper utilizes a modular design concept, enabling it to be reconstructed with a varying number of fingers based on specific requirements. One of the fingers has an active rolling fingertip that rolls up the object from the table to create wrinkles, for easier grasping. Additionally, there are fingertips with revolute joints that enable fine-tuning of the grasp configuration to fix the objects. To test the effectiveness of the mechanism, we manufactured a real gripper device and mounted it on a collaborative robotic arm for grasping tests. Experiments were conducted on six kinds of cloth-like objects with different materials, grasping them from above. The experimental results show that the gripper with this mechanism configuration performs well in this scenario. © 2023 IEEE.

关键词： Grippers

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Collaborative Fall Detection and Response using Wi-Fi Sensing and Mobile Companion Robot

arXiv

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arXiv 2024年

作者： Chen, Yunwang Kang, Yaozhong Zhao, Ziqi Hong, Yue Meng, Lingxiao Meng, Max Q.-H. The Shenzhen Key Laboratory of Robotics Perception and Intelligence Southern University of Science and Technology Shenzhen518055 China The Department of Electrical and Electronic Engineering SUSTech Shenzhen China The School of System Design and Intelligent Manufacturing SUSTech Shenzhen China

This paper presents a collaborative fall detection and response system integrating Wi-Fi sensing with robotic assistance. The proposed system leverages channel state information (CSI) disruptions caused by movements to detect falls in non-line-of-sight (NLOS) scenarios, offering non-intrusive monitoring. Besides, a companion robot is utilized to provide assistance capabilities to navigate and respond to incidents autonomously, improving efficiency in providing assistance in various environments. The experimental results demonstrate the effectiveness of the proposed system in detecting falls and responding effectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Fall detection

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When and Where to Step: Terrain-Aware Real-Time Footstep Location and Timing Optimization for Bipedal Robots

arXiv

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arXiv 2023年

Online footstep planning is essential for bipedal walking robots, allowing them to walk in the presence of disturbances and sensory noise. Most of the literature on the topic has focused on optimizing the footstep placement while keeping the step timing constant. In this work, we introduce a footstep planner capable of optimizing footstep placement and step time online. The proposed planner, consisting of an Interior Point Optimizer (IPOPT) and an optimizer based on Augmented Lagrangian (AL) method with analytical gradient descent, solves the full dynamics of the Linear Inverted Pendulum (LIP) model in real time to optimize for footstep location as well as step timing at the rate of 200 Hz. We show that such asynchronous real-time optimization with the AL method (ARTO-AL) provides the required robustness and speed for successful online footstep planning. Furthermore, ARTO-AL can be extended to plan footsteps in 3D, allowing terrain-aware footstep planning on uneven terrains. Compared to an algorithm with no footstep time adaptation, our proposed ARTO-AL demonstrates increased stability in simulated walking experiments as it can resist pushes on flat ground and on a 10◦ ramp up to 120 N and 100 N respectively. Videos1 and open-source code2 are released. © 2023, CC BY.

关键词： Constrained optimization

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design and Development of an IoT Enabled and Robot Integrated Smart Manufacturing Work Cell

Design and Development of an IoT Enabled and Robot Integrate...

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Conference on Information and Communication Technology (CICT)

作者： Deep Patel Sreekumar Muthuswamy Department of Mechanical Engineering Centre for AI IoT and Robotics Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai India

ISBN: (数字)9781665473125

ISBN: (纸本)9781665473132

Globalisation and the growing demand for goods are forcing the manufacturing industries to be more efficient, economical, and sustainable. With the rise of Industry 4.0, the manufacturing sector can immensely benefit from smart manufacturing technologies by transforming the conventional system into an IoT enabled system. This paper presents the development of an IoT enabled and robot integrated smart manufacturing work cell as a platform for research, teaching, and innovation toward Industry 4.0. The system is designed and developed with the integration of a CNC lathe, 6 Dof ABB robot manipulator, Cognex Vision System, performance monitoring sensors, IoT gateway, edge devices, human-machine interface and IoT platform (ThinkSpeak). The preliminary testing results indicate the established work cell's ability to supervise the system's performance data over the cloud; control and monitor the system using the web interface.

关键词： Technological innovation Service robots System performance Logic gates Robot sensing systems Sensor systems Fourth Industrial Revolution

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Classification of Lower Limb Electromyographical Signals Based on Autoencoder Deep Neural Network Transfer Learning

Classification of Lower Limb Electromyographical Signals Bas...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： F.H. Daryakenari M. Mollahossein A. Taheri G.R. Vossoughi Mechanical Engineering Department Center of Excellence in Design Robotics and Automation Mechatronic Research Lab Sharif University of Technology Tehran Iran

ISBN: (纸本)9781665454537

In this paper we aim to propose an Artificial Neural Network (ANN) model in order to classify lower limb surface Electromyographical signals, without applying any pre-processing to the inputs. To this end, first three different autoencoder models have been proposed in order to choose the autoencoder architecture with the best performance. Then encoder layers of the best autoencoder model transferred to a classification network. This network aims to classify raw input sEMG signals into three categories, namely; sitting, standing and walking. After training the classification network, we were able to successfully classify input sEMG signals and reach to 81.51% accuracy for all the classes and 91% and 94% accuracy for the first two classes, with the unseen subjects. The major difference of this research with previous ones lies in fact that evaluation of our network occurs in a realistic scenario, where data from test and validation subjects is not seen by the network before and also our transferred network uses unlabeled raw sEMG signals for extracting features from signal, which gives us the ability to transfer this model in situations where labeled data is not available.

关键词： Training Adaptation models Transfer learning Noise reduction Neurons Artificial neural networks Feature extraction

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GNN-Assisted BiG-AMP: Joint Channel Estimation and Data Detection for Massive MIMO Receiver

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IEEE Transactions on Wireless Communications 2025年

作者： Liu, Zishen Wu, Nan He, Dongxuan Yuan, Weijie Li, Yonghui Quek, Tony Q. S. School of Information and Electronics Beijing Institute of Technology Beijing100081 China School of System Design and Intelligent Manufacturing Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China School of Electrical and Information Engineering The University of Sydney SydneyNSW2008 Australia Singapore University of Technology and Design 487372 Singapore Department of Electronic Engineering Kyung Hee University Yongin17104 Korea Republic of

In this paper, we develop a graph neural network (GNN)-assisted bilinear inference approach to enhance the receiver performance of the MIMO system through message passing-based joint channel estimation and data detection (JCD). Specifically, based on the bilinear generalized approximate message passing (BiG-AMP) framework and conditional correlation of signal, we propose a GNN-assisted BiG-AMP (GNN-BiGAMP) approach, which integrates a GNN module into the data-detection-loop to compensate the inaccurate marginal likelihood approximation. By leveraging the coupling between the channel and received symbols, a bilinear GNN-assisted BiG-AMP (BiGNN-BiGAMP) JCD receiver is further proposed. This method incorporates two GNNs with similar graph representation into the bilinear posterior estimation loops, which not only compensates for approximation errors but also alleviates performance loss due to premature variance convergence, thereby enhancing the receiver performance significantly. To fully exploit the supervised information from channel estimation and data detection, we propose a multitask learning based training scheme, which coordinates GNNs with different tasks in two loops. Simulation results show that our proposed GNN-assisted JCD receivers significantly outperform other JCD counterparts in terms of both channel estimation and data detection. © 2025 IEEE. All rights reserved.

关键词： Graph neural networks

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Learning Quadrupedal Locomotion with Impaired Joints Using Random Joint Masking

arXiv

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arXiv 2024年

作者： Kim, Mincheol Shin, Ukcheol Kim, Jung-Yup The Humanoid Robot Research Laboratory Department of Mechanical Design and Robot Engineering Seoul National University of Science and Technology Seoul01811 Korea Republic of Robotics Institute Carnegie Mellon University PittsburghPA15217 United States

Quadrupedal robots have played a crucial role in various environments, from structured environments to complex harsh terrains, thanks to their agile locomotion ability. However, these robots can easily lose their locomotion functionality if damaged by external accidents or internal malfunctions. In this paper, we propose a novel deep reinforcement learning framework to enable a quadrupedal robot to walk with impaired joints. The proposed framework consists of three components: 1) a random joint masking strategy for simulating impaired joint scenarios, 2) a joint state estimator to predict an implicit status of current joint condition based on past observation history, and 3) progressive curriculum learning to allow a single network to conduct both normal gait and various joint-impaired gaits. We verify that our framework enables the Unitree's Go1 robot to walk under various impaired joint conditions in real-world indoor and outdoor environments. © 2024, CC BY-NC-ND.

关键词： Deep learning

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