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4th IEEE International Conference on Modern Electrical and Energy System, MEES 2022

作者： Lebedenko, Yurii Polyvoda, Oksana Derkachenko, Anatolii Modlo, Yevhenii Demishonkova, Svitlana Pylypenko, Yurii Kyiv National University of Technologies and Design Information and Computer Technologies Department Kyiv Ukraine Kherson National Technical University Automation Robotics and Mechatronics Department Kherson Ukraine Technological Institute State University of Economics and Technology Kryvyi Rih Ukraine Kyiv National University of Technologies and Design Department of Computer Engineering and Electromechanics Kyiv Ukraine

ISBN: (纸本)9798350346831

The work is devoted to the study of the control system of the moving platform of the manipulative robot. The purpose of the work is the development and research of an instrumental complex of control, analysis and modeling of the operation of positioning platforms based on a spherical mechanism for use in automated industrial systems. The state of use of multi-link positioning platforms in various industries was analyzed and their classification was provided, the main areas of use were determined. A comparative analysis of mathematical methods for describing the kinematic properties of positioning platforms was performed, namely: the closed vector contour method, the coordinate transformation method. A model with an analytical apparatus for controlling the drive links of the spherical mechanism according to the given scenarios of the orientation of the positioning platform was developed, as well as a mathematical model of inverse kinematics was made, the working space was modeled, and the service angles were determined, and a calculation and real-life experiment was conducted. For the first time, the work uses the dependencies of analytical geometry to implement algorithms for controlling a spherical mechanism by inverse kinematics, and offers software and hardware that combines visual modeling tools with real-time data transmission to a controller that controls the drivers of a spherical mechanism by direct kinematics. The result of the field experiment is the testing of the proposed control algorithm on the manufactured manipulator mechanism on the most common movements of the platform of the spherical mechanism and its exit to the extreme positions in order to determine the working space and the values of the service angles. A system of remote control of the spherical mechanism at the hardware level with an interactive interface and tracking of the positioning target has been implemented. © 2022 IEEE.

关键词： Inverse kinematics

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Analog Magnetic Sensor-Robotic System for Steel Structure Inspection

Analog Magnetic Sensor-Robotic System for Steel Structure In...

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IEEE/SICE International Symposium on System Integration

作者： Anh Quyen Pham Duc La Hung Manh La AIR-VIET Corporation in Vietnam Kahlert School of Computing University of Utah Salt Lake City UT USA Department of Computer Science and Engineering Advanced Robotics and Automation (ARA) Lab University of Nevada Reno NV USA

More and more steel bridge collapse accidents occur worldwide due to broken steel structures, causing significant l oss o f l ife a nd p roperty t o m ankind. T his has spurred much research into robots that can climb steel surfaces and carry smart sensors with the desire to assist inspectors in inspecting steel defects. However, current non-destructive evaluation (NDE) sensors such as eddy-current and giant magnetoresistance (GMR) are not able to be integrated with the robotic platforms due to their large size, mass, and limited ability to interface. More importantly, these NDE sensors may fail to detect hidden and underlying cracks. Therefore, in this paper, we present a novel and compact analog magnetic sensor system to detect different types of cracks in steel. Validations are carried out on a steel test plate 600mm long, 140mm wide, and 6mm thick, and precision machined by CNCs to produce various man-made cracks including penetrating cracks, surface cracks, internal/hidden cracks, and underlying cracks. These cracks have a width varying from 0.6mm to 1.2mm with different depth levels. Combined with applying the Kalman filter t o t he n oise r eduction s ystem, t he r esults o btained are accurate, and the response speed is fast. The sensor is small enough and has firmware/software with an interface to make it possible to be integrated with a small drone or a climbing robot to perform an in-depth inspection of the steel bridge. A demonstration of the system can be seen in this video: https://***/OrdpLdnFlYk

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Comparative Study on Fall Detection using Machine Learning Approaches 19

Comparative Study on Fall Detection using Machine Learning A...

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19th International Conference on Electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2022

作者： Tongskulroongruang, Thienrawit Wiphunawat, Paphatchaya Jutharee, Wisanu Kaewmahanin, Watchara Rassameecharoenchai, Teerameth Jennawasin, Tanagorn Kaewkamnerdpong, Boonserm King Mongkut's University of Technology Thonburi Department of Control Systems and Instrumentation Engineering Bangkok Thailand Institute of Field Robotics and Automation King Mongkut's University of Technology Thonburi Bangkok Thailand Naresuan University Faculty of Allied Health Sciences Department of Cardiothoracic Technology Phitsanulok Thailand King Mongkut's University of Technology Thonburi Biological Engineering Program Faculty of Engineering Bangkok Thailand

ISBN: (纸本)9781665485845

Falls are one of the most dangerous problems for the elderly. A reliable fall detection system can aid in reducing the harmful repercussions of an unintentional fall. The focus of this paper is on a dataset that includes signals from portable devices. We propose a simple feature selection approach to reduce the number of effective input attributes based on a voting strategy. The dataset is classified into three categories, i.e., pre-fall, fall and post-fall using various machine learning approaches. The computational time is significantly reduced for most of the classification algorithms, and the relative reduction reaches to 66.67% with decision tree algorithm. Classification accuracy can reach as high as 95.17% when using neural network, while the relative reductions on the classification accuracy due to the feature selection do not exceed 1.50%. © 2022 IEEE.

关键词： Decision trees

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Distributed Event-Triggered Formation Control for a Multi-robotic Fish System

Distributed Event-Triggered Formation Control for a Multi-ro...

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International Conference on Guidance, Navigation and Control, ICGNC 2022

作者： Dai, Shijie Wu, Zhengxing Tan, Min Yu, Junzhi School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China State Key Laboratory for Turbulence and Complex System Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Peking University Beijing100871 China

ISBN: (纸本)9789811966125

This paper investigates the formation control problem for a multi-robotic fish system and a distributed event-triggered-based formation control framework is proposed. The framework consists of a communication topology, a distributed formation control law, and a dynamic event-triggered communication mechanism. In particular, a position-based formation control law is presented to drive the multi-robotic fish system to an anticipated configuration based on local measurements while keeping pace with a moving target. Meanwhile, a dynamic event-triggered mechanism is utilized to measure importance of the interactive information and decide the communication timing. The proposed formation control method shows its merits to greatly reduce communication consumption with a limited loss of formation control performance. Finally, simulations with quantitative comparisons are presented to verify the effectiveness of the proposed framework. This formation control framework provides a solid foundation for future marine cooperative control of the multi-robotic fish system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multi agent systems

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Miniature Reconfigurable Modular Soft Robots Using Liquid Crystal Elastomer Actuation

Miniature Reconfigurable Modular Soft Robots Using Liquid Cr...

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ASME/IFToMM International Conference on Reconfigurable Mechanisms and Robots, ReMAR

作者： Huayu Zhang Jiajun An Tianle Pan Upinder Kaur Zhijian Wang Xuchen Wang Qiguang He Xin Ma Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong SAR Multiscale Medical Robotics Centre Hong Kong SAR Department of Agricultural and Biological Engineering Purdue University USA Key Laboratory of Aerospace Advanced Materials and Performance Ministry of Education School of Materials Science and Engineering Beihang University China

ISBN: (数字)9798350395969

ISBN: (纸本)9798350395976

Soft robotics is rapidly evolving, fueled by advancements in controllable materials. Liquid crystal elastomers (LCEs) stand out with their exceptional actuation capabilities, ease of processing, and built-in programmability. However, current LCE robots struggle with limited dexterity and adaptability, hindering their real-world potential. We address this limitation by introducing a novel modular design approach that unlocks the full potential of LCEs. Our miniature, highly deformable soft robot utilizes reconfigurable LCE modules for bending and twisting, enabling enhanced manipulability through serial combinations. Demonstrating its dexterity, the modular robot performs intricate fingertip manipulation, allowing a miniature gripper to rotate a small object. This achievement highlights the reconfigurability and utility of our system, paving the way for more dexterous and versatile LCE-based robots.

关键词： Elastomers Soft robotics Bending Grippers

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ROBUST: 221 Bugs in the Robot Operating System

arXiv

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arXiv 2024年

作者： Timperley, Christopher S. van der Hoorn, Gijs Santos, André Deshpande, Harshavardhan Wąsowski, Andrzej School of Computer Science Carnegie Mellon University United States Department of Cognitive Robotics faculty of Mechanical Maritime and Materials Engineering Delft University of Technology Netherlands VORTEX CoLab Portugal Fraunhofer Institute for Manufacturing Engineering and Automation IPA Germany Department of Computer Science IT University of Copenhagen Denmark

As robotic systems such as autonomous cars and delivery drones assume greater roles and responsibilities within society, the likelihood and impact of catastrophic software failure within those systems is increased. To aid researchers in the development of new methods to measure and assure the safety and quality of robotics software, we systematically curated a dataset of 221 bugs across 7 popular and diverse software systems implemented via the Robot Operating System (ROS). We produce historically accurate recreations of each of the 221 defective software versions in the form of Docker images, and use a grounded theory approach to examine and categorize their corresponding faults, failures, and fixes. Finally, we reflect on the implications of our findings and outline future research directions for the community. © 2024, CC BY-NC-SA.

关键词： Robot Operating System

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Learning Rigidity-based Flocking Control using Gaussian Processes with Probabilistic Stability Guarantees

Learning Rigidity-based Flocking Control using Gaussian Proc...

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IEEE Conference on Decision and Control

作者： Thomas Beckers George J. Pappas Leonardo J. Colombo Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia PA USA Centre for Automation and Robotics (CSIC-UPM) Madrid Spain

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Flocking control of multi-agents system is challenging for agents with partially unknown dynamics. These dynamics typically occur due to unfavorable nonlinearities such as friction or production inaccuracies. This paper proposes an online learning-based controller to stabilize flocking motion of double-integrator agents with additional state-dependent, unknown dynamics by using a Gaussian process (GP). Agents interaction is described by a time-invariant infinitesimally minimally rigid undirected graph. We provide a decentralized control law that exponentially stabilizes the motion of the agents and captures Reynold boids motion for swarms by using GPs as an online learning-based oracle for the prediction of the unknown dynamics. In particular the presented approach guarantees a probabilistic bounded tracking error with high probability.

关键词： Visualization Tracking Dynamics Gaussian processes Production Probabilistic logic Solids

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Electric Vehicle Charging Navigation Using Waterwheel Plant Algorithm for Coordinating Smart Grids and Intelligent Transportation Systems

Electric Vehicle Charging Navigation Using Waterwheel Plant ...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： M. Siva Ramkumar M. Sivaramkrishnan Kaparthi Uday Mohammad Kanan C. Mohan Raj Jayant Giri Dept of ECE SNS College of Technology Dept of EEE Karpagam College of Engnieering India EIE Department CVR College of Engineering Ibrahimpatanam Hyderabad Department of Industrial Engineering College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Mechanical Engineering College of Engineering Zarqa University Zarqa Jordan Department or EEE Centre for Robotics and Industrial Automation Sri Eshwar College of Engineering Coimbatore Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Division of Research and Development Lovely Professional University Phagwara India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The efficient charging of electric vehicles (EVs) remains essential to create sustainable mobility while EVs transform into crucial elements of upcoming transportation systems. The present EV charging navigation systems deal with three main issues including grid congestion and inadequate charging station (CS) selection and delays from traffic *** paper proposes a novel Waterwheel Plant Algorithm (WWPA) for optimizing EV charging navigation within the Smart Grid (SG) and Intelligent Transportation Systems (ITS). The suggested method strives to enhance SG-EV coordination through demand balancing in addition to charge price reduction and decreased grid congestion levels. WWPA algorithm serves as the proposed method to optimize EV charging schedules. The MATLAB platform serves as the implementation environment for the proposed method which demonstrates superior performance than Multi-agent Reinforcement Learning (MARL), Deep Reinforcement Learning (DRL), Coati-Northern Goshawk Optimization (Coati-NGO) and Grey Sail Fish Optimization (GSFO). Results demonstrate that the WWPA algorithm reduces average charging expenses by 18.14% throughout diverse charging operations as it produces superior performance than existing algorithms operating in the SG ITS.

关键词： Navigation Heuristic algorithms Transportation Transforms Traffic control Electric vehicle charging Smart grids Delays Vehicle dynamics Testing

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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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M2UD: A Multi-model, Multi-scenario, Uneven-terrain Dataset for Ground Robot with Localization and Mapping Evaluation

arXiv

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arXiv 2025年

作者： Jia, Yanpeng Wang, Shiyi Shao, Shiliang Wang, Yue Zhang, Fu Wang, Ting State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Zhejiang University Zhejiang China Department of Mechanical Engineering The University of Hong Kong Hong Kong

As one of the most widely used types of robots, ground robots play a crucial role in inspection, exploration, rescue, and other applications. In recent years, advancements in Light Detection and Ranging (LiDAR) technology have made sensors more accurate, lightweight, and cost-effective. Therefore, researchers increasingly integrate LiDAR with other sensors, such as Inertial Measurement Units (IMUs) and cameras, for Simultaneous Localization and Mapping (SLAM) (SLAM) studies, providing robust technical support for ground robots and expanding their application domains. Public datasets that incorporate multiple sensors and diverse scenarios are essential for advancing SLAM technology in ground robots. However, existing datasets for ground robots are typically restricted to flat-terrain motion with 3 Degrees of Freedom (DOF) and cover only a limited range of scenarios. Although handheld devices and unmanned aerial vehicle (UAV) exhibit richer and more aggressive movements, their datasets are predominantly confined to small-scale environments due to endurance limitations. To fill these gap, we introduce M2UD, a multi-modal, multi-scenario, uneven-terrain SLAM dataset for ground robots. This dataset contains a diverse range of highly challenging environments, including cities, villages, open fields, long corridors, plazas, underground parking, and mixed scenarios. Additionally, it presents extreme weather conditions such as darkness, smoke, snow, and dust. The aggressive motion and degradation characteristics of this dataset not only pose challenges for testing and evaluating existing SLAM methods but also advance the development of more advanced SLAM algorithms. To benchmark SLAM algorithms, M2UD provides smoothed ground truth localization data obtained via Real-time Kinematics (RTK) and introduces a novel localization evaluation metric that considers both accuracy and efficiency. Additionally, we utilize a high-precision millimeter-level laser scanner to acquire ground

关键词： Unmanned aerial vehicles (UAV)

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