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IEEE International Conference on robotics and automation (ICRA)

作者： Jonas C. Kiemel Torsten Kröger Institute for Anthropomatics and Robotics – Intelligent Process Automation and Robotics (IAR-IPR) Karlsruhe Institute of Technology (KIT)

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In this paper, we present an iterative method to quickly traverse multi-dimensional paths considering jerk constraints. As a first step, we analyze the traversal of each individual path dimension. We derive a range of feasible target accelerations for each intermediate waypoint of a one-dimensional path using a binary search algorithm. Computing a trajectory from waypoint to waypoint leads to the fastest progress on the path when selecting the highest feasible target acceleration. Similarly, it is possible to calculate a trajectory that leads to minimum progress along the path. This insight allows us to control the traversal of a one-dimensional path in such a way that a reference path length of a multi-dimensional path is approximately tracked over time. In order to improve the tracking accuracy, we propose an iterative scheme to adjust the temporal course of the selected reference path length. More precisely, the temporal region causing the largest position deviation is identified and updated at each iteration. In our evaluation, we thoroughly analyze the performance of our method using seven-dimensional reference paths with different path characteristics. We show that our method manages to quickly traverse the reference paths and compare the required traversing time and the resulting path accuracy with other state-of-the-art approaches.

关键词： Accuracy Target tracking Trajectory Iterative methods robotics and automation

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The System of Coordinated Operation of Two Underwater Vehicles in a Complex Environment

The System of Coordinated Operation of Two Underwater Vehicl...

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2023 International Conference on Ocean Studies, ICOS 2023

作者： Filaretov, Vladimir Katsurin, Alexey Institute of Automation and Control Processes Feb Ras Vladivostok State University Laboratory of Robotic Systems Laboratory of Automation and Robotics Vladivostok Russia Far Eastern Federal University M.D. Ageev Institute for Marine Technology Problems Feb Ras Laboratory of Intelligent Information Systems for Marine Robots Department of Automation and Robotics Vladivostok Russia

ISBN: (纸本)9798350382457

The paper discusses methods of coordinated operation of two autonomous underwater vehicles, one of which is equipped with a multi-link manipulator and a vision system, and the second - auxiliary - only a vision system. These methods ensure the joint operation of two autonomous underwater vehicles both in the mode of semi-automatic positional remote control, and in the mode of automatic execution of operations with various objects. At the same time, work is considered in situations when the vision system of the main underwater vehicle does not allow observing the object of work, which is nevertheless located in the working area of the manipulator. In this case, the auxiliary inspection underwater vehicle with its own vision system is used to monitor the object. In the semi-automatic remote control mode, the manipulator automatically takes into account the current spatial orientation of the optical axis of the vision system of the inspection underwater vehicle, as well as the spatial orientations of both underwater vehicles at yaw angles in the absolute coordinate system. And in automatic mode, an accurate transfer of the spatial location and orientation of the object, which are determined using the vision system of the inspection underwater vehicle, is additionally provided to the associated coordinate system of the main underwater vehicle. It is possible to identify and eliminate errors that occur during the operation of the vision system and navigation systems of both underwater vehicles. The results of experimental studies have confirmed the operability of the proposed methods. © 2023 IEEE.

关键词： Autonomous underwater vehicles

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Trans-Domain Sea-Air Integrated Robot 3

Trans-Domain Sea-Air Integrated Robot

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3rd IEEE International Conference on Recent Advances in Systems Science and Engineering, RASSE 2023

作者： Jacob, Philip C Thomas, Anish Adi Shankara Institute of Engineering and Technology Department of Robotics and Automation Kalady India Saintgits College of Engineering Department of Electronics Engineering Kottayam India

ISBN: (纸本)9798350341676

The Trans-Domain Sea-Air Robot (TSAR) design with trans-domain capabilities or converting a normal drone to hybrid technology. It is still a major challenge to achieve miniaturization and enhance the maneuverability and overwater reliability of transmission. Trans-Domain robot is proposed in this work which achieves free trans-domain motion and has the advantages of small size, high maneuverability and high reliability for autonomous water vehicle operation. The proposed has a dc motor-propellers platform which satisfies the power and small diameter requirements and the blades reduce water drag and facilitate transportation. Further, a stable and efficient trans-domain attitude adjustment system is presented which effectively realizes trans-domain attitude switching. Based on the characteristics of the body additional ES32 with a camera was been given in order to image processing for human body detection and fish shoal localization. The Sea-Air Integrated Robot is a conceptual design for a multi-purpose robotic system that can operate both on land and water. The robot is designed to be a versatile platform that can perform a wide range of tasks, from surveillance and inspection to scientific research and rescue operation. The TSAR has many potential applications, including environmental monitoring, oil and gas exploration, search and rescue operations, and military surveillance. Its modular design and ability to operate in multiple environments make it a versatile platform that can be adapted to a wide range of tasks. © 2023 IEEE.

关键词： Maneuverability

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Step-by-step Linearized Kalman Filter-Based Fault Detection of Workpiece Machining

Step-by-step Linearized Kalman Filter-Based Fault Detection ...

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2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Sun, Xiaohui Wu, Xinyu Wen, Chenglin Ma, Xue Tsinghua University Department of Automation Beijing China Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Shenzhen China Guangdong University of Petrochemical Technology School of Automation Maoming China

ISBN: (纸本)9798350337754

This paper is concerned with step-by-step linearized Kalman filter to deal with multiplicative faults diagnosis problem for class of nonlinear systems. Multiplicative fault variables are defined as parameter variables to be estimated, and linear dynamic models between fault variables and all variables are established. The indirect linear state and measurement models of fault variables and the corresponding step-by-step linearized Kalman filters are established based on current estimated values of all variable and some obtained estimated values of future variables;further, combined with fault variables, a filter is designed for state variables in the linearized system. On basis of EKF, the redisual generation is obtained. The effectiveness of the novel filter is verified in workpiece machining process. © 2023 IEEE.

关键词： Fault detection

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Small Sample Learning Mechanism Based on Knowledge-transferring for Fault Diagnosis

Small Sample Learning Mechanism Based on Knowledge-transferr...

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2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Ma, Xue Wu, Xinyu Wen, Chenglin Sun, Xiaohui Tsinghua University Department of Automation Beijing China Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Shenzhen China Guangdong University of Petrochemical Technology Department of Automation Maoming China

ISBN: (纸本)9798350337754

Deep learning-based methods have been applied in many fields which has received increased attention. It requires large annotated data to train a deep model with good generalization ability. However, it may be very difficult, time-consuming, and even expensive to collect and annotate practical industrial data, for example, for fault diagnosis in industrial systems. To address this issue, a novel small sample learning (SSL) mechanism based on Knowledge-transferring is proposed in this manuscript, which leverages the expert system and deep neural network simultaneously. Firstly, an annotator is constructed based on a belief rule base (BRB) expert system only with available small labeled samples. Then, the left unlabeled samples will be labeled through the annotator to form an augmented training dataset (ATD) with the available small labeled samples, which can realize the data representation of expert knowledge. Besides, a united optimization model based on CNN that combines data and knowledge is built, which has realized the transfer of expert knowledge. Finally, we demonstrate the capability of the proposed method on the dataset collected from the real experimental platform for rotating machinery fault diagnosis. © 2023 IEEE.

关键词： Failure analysis

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Calculation Method of Road Distress Area Based on Monocular Vision 3

Calculation Method of Road Distress Area Based on Monocular ...

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3rd International Conference on Computer Vision and Pattern Analysis, ICCPA 2023

作者： Wang, Lei Hu, Yuxin Zhang, Zhaowei Jiang, Lin School of Mechanical and Automation Engineering Wuhan University of Science and Technology Wuhan430081 China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan430081 China

ISBN: (纸本)9781510667563

Proposed a method for calculating the area of road diseases using a monocular camera. The camera and the road plane are calibrated to obtain the mapping relationship between pixel points and spatial points;the Mask R-CNN is used to detect and segment the road disease;the inverse perspective transformation is used to convert the target image into a bird’s-eye view to obtain the relationship between the pixel scale and the real scale;binarize the bird’s-eye view according to the segmentation effect, and the area of the road disease could be obtained through calculating the number of target pixels. It has been verified that the calculation error of the algorithm is about 2.1675% in the near vicinity. © 2023 SPIE.

关键词： Birds

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Adaptive Dynamic Speed Limiter for Enhanced Safety and Efficiency in Electric Vehicles

Adaptive Dynamic Speed Limiter for Enhanced Safety and Effic...

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IEEE International Conference on System, Computation, automation and Networking (ICSCAN)

作者： D. Dharani B. Pradaap H. Hemavandhana V. Mohan Raja Department of Robotics and Automation Manakula Vinayagar Institute of Technology Pondicherry India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

Electric Vehicles (EVs) have revolutionized transportation, providing eco-friendly alternatives to traditional internal combustion engine vehicles. As the number of Electric Vehicles on the road increases, there is a critical need for systems that ensure both safety and operational efficiency. The proposed Adaptive Dynamic Speed Limiter system adjusts a vehicle's speed dynamically based on external control signals transmitted wirelessly. The system uses a transmitter to send predefined commands corresponding to different speed levels or stop conditions. A receiver integrated into the EV decodes these signals and modifies the vehicle's motor speed accordingly. An LCD provides real-time feedback to the driver and displays the vehicle's current status. The adaptive dynamic model ensures compliance with traffic regulations, reduces energy consumption, and enhances safety for both drivers and pedestrians. By integrating advanced control mechanisms, the project contributes to developing smarter, safer EVs.

关键词： Adaptive systems Transmitters Roads Receivers Electric vehicles Regulation Real-time systems Safety Vehicle dynamics Vehicles

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Multi-class Bearing Fault Classification using Deep Learning and Transfer Learning Models

Multi-class Bearing Fault Classification using Deep Learning...

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Innovation in technology (ASIANCON), Asian Conference on

作者： Avanish Shimpi Gunjay Chitr Suhalka Sameer Sayyad Satish Kumar Arunkumar Bongale Dept.Robotics and Automation Symbiosis Institute of Technology Pune India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

In the modern age of industry 4.0 and state-of-the-art manufacturing, automated inspection plays an essential role. The bearing in the rotary machine is a vital and critical component as it provides support and stability to the rotary elements of machines. The defect in the bearing is responsible for the increase in vibrations, noise and reduction in reliability and safety of the rotating machines. It is very challenging to humanly detect different faults in these bearings. This work highlights the methods of multi-class bearing fault classification using Deep Learning (DL) and Transfer Learning (TL) models. The different DL models such as Artificial Neural Network (ANN), and Convolutional Neural Network (CNN) are used along with the TL models such as VGG16 and MobileNet. The models perform with good classification accuracy ranging from 88% to 98%. The Transfer learning models perform better for both binary and multi-class fault classification of the bearings.

关键词： Deep learning Vibrations Technological innovation Accuracy Fault detection Transfer learning Stability analysis Safety Reliability Convolutional neural networks

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AI-Powered Real-Time Detection of Wheel Defects in Railways Using Yolov8 7

AI-Powered Real-Time Detection of Wheel Defects in Railways ...

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7th Edition Global Conference on Wireless and Optical Technologies, GCWOT 2024

作者： Shaikh, Muhammad Zakir Baro, Enrique Nava Baloch, Yaqoob Ali Hussain, Tanweer Uqaili, Mohammad Aslam Chowdhry, Bhawani Shankar School of Industrial Engineering University of Malaga Malaga Spain Jamshoro Pakistan Universidad de Malaga Departamento de Ingeniería de Comunicaciones Campus de Teatinos Malaga Spain National Center of Robotics and Automation Cms Lab Jamshoro Pakistan Mehran University of Engineering and Technology National Center of Robotics and Automation Cms Lab Department of Electrical Engineering Jamshoro Pakistan

ISBN: (纸本)9798331534271

Railway wheels are one of the most critical components of the railway infrastructure, functioning as the load carrier. They are exposed to various forms of damage, caused due to intense sliding friction and inadequate inspection procedures. This research introduces a real-time wheel fault detection system using YOLOv8, leveraging computer vision and AI-based object detection. The main contribution of this research is the utilization of a custom data acquisition setup for capturing wheel images and detecting faults with high accuracy and speed. Our results demonstrate an overall F1 score of 0.80, with precision and recall curves indicating strong wheel detection and moderate defect detection capabilities. This approach aims the reduction of inspection costs and the improvement of railway safety through automation and digitization. © 2024 IEEE.

关键词： Railroads

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Research on the driver fatigue early warning model of electric vehicles based on the fusion of EMG and ECG signals

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High technology Letters 2024年第4期30卷 333-343页

作者： REN Bin LI Qibing ZHOU Qinyu LUO Wenfa Shanghai Key Laboratory of Intelligent Manufacturing and Robotics School of Mechatronic Engineering and AutomationShanghai UniversityShanghai 200444P.R.China Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo Institute of Materials Technology&EngineeringChinese Academy of SciencesNingbo 315201P.R.China SAIC Motor R&D Innovation Headquarters SAIC Motor Corporation LimitedShanghai 201804P.R.China

Electric vehicles have been rapidly developing worldwide due to the use of new ***,at the same time,serious traffic accidents caused by driver fatigue in emergency situations have also drawn widespread *** lack of datasets in real vehicle test environments has always been a bottleneck in the research of driver fatigue in electric ***,this study establishes a dataset from real vehicle test,applies the Bayesian optimization support vector machine(BOA-SVM)algorithm to take features of electromyography(EMG)and electrocardiography(ECG)signals as input and develop an early warning model for driving fatigue ***,the driver’s EMG and ECG signals are collected through real vehicle testing experiments and then combined with the driver’s subjective fatigue evaluation scores to establish the ***,the study establishes a driver fatigue early warning model for emergency ***-domain and frequency-domain features are extracted from the EMG *** component analysis(PCA)is applied for dimensionality reduction of these *** experimental results show that based on the input of dimensionality reduced EMG features and ECG features,the BOA-SVM algorithm achieved an accuracy of 94.4%in classification.

关键词： driver fatigue early warning electromyography(EMG)signal electrocardiography(ECG)signal principal component analysis(PCA) support vector machine(SVM)

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