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检索条件"任意字段=Proceedings of the 2020 2nd International Conference on Robotics Systems and Vehicle Technology"

共 1003 条记录，以下是71-80 订阅

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Smart Educational robotics Laboratory Ecosystem for Remote Control of Robotic Manipulators Through Telepresence Technologies 2

Smart Educational Robotics Laboratory Ecosystem for Remote C...

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2nd international conference on technology Enhanced Learning in Higher Education, TELE 2022

作者： Kondratyev, Sergey Pikalov, Vladimir Muravyev, Artem Evseev, Alexei Lipetsk State Technical University Automation and Computer Science Faculty Lipetsk Russia

ISBN: (纸本)9781665470070

This paper discusses a smart laboratory robotics system designed to allow students remote connection and work with robotic manipulation devices and applications, as well as for teaching and conducting collaborative research projects in the field of electrical engineering and automation. A scheme of connecting all the necessary robotic devices and video cameras to the main server in the laboratory for the possibility of connecting to a remote desktop is presented. The developed solution for remotely connecting students to a physical robotic cell, which is connected to the lab server, is presented. Thanks to the proposed system, it was possible to conduct a debugging of new solutions for the control of robotic manipulation systems on a particular connected technological equipment. A telepresence system with real-time video streaming of physical robotic cell was developed to monitor the behavior of the robotic system remotely in order to understand the process of the connected devices clearly after the solution was uploaded to the robot controller. © 2022 IEEE.

关键词： Remote control

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Application Research of Landmarks in Path Planning Algorithms of Mobile Robots 2

Application Research of Landmarks in Path Planning Algorithm...

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2nd international conference on Digital Society and Intelligent systems, DSInS 2022

作者： Fan, Linghao Liu, Xiangde Zhang, Yi Chongqing University of Posts and Telecommunications Intelligent Systems and Robotics Laboratory Chongqing China Chongqing Information Accessibility and Service Robot Engineering Technology Research Center Chongqing China

ISBN: (纸本)9781510663244

A way of incorporating landmark information matrix QR to affect the global path planning algorithm is proposed for the use of landmarks in robot localization and navigation. It is also investigated how this method affects the pace at which the original algorithm solves problems. The reward and punishment items in this technique include dangerous locations with lots of obstacles around them, landmarks with location information connected to the ground, and the two combined. Three representative common robot path planning algorithms are also imposed as influences. Through MATLAB simulation experiment, find the optimal path planning algorithm that is appropriate for applying reward and punishment by comparing the quality of the robot's driving path and the algorithm solution speed before and after the reward and punishment. According to the simulation results, the reward and punishment approach is practical and efficient. The heuristic search algorithm is better suited for reward and punishment, and both its path quality and speed of solution have greatly increased. © 2023 SPIE.

关键词： Genetic algorithms

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Advanced Brain Tumor Segmentation Using UNet and Multiple Residual Networks 2

Advanced Brain Tumor Segmentation Using UNet and Multiple Re...

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2nd IEEE international conference on Networking and Communications, ICNWC 2024

作者： Ramya, D. Manigandan, S.K. Velmurugan, J. Vishnukumar, A. Umarani, V. Reddy, P. V. Sai Ram Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Information Technology Chennai India Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Information Technology Chennai India Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Computer Science and Business Systems Chennai India Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350365269

Accurate brain tumors segmentation is essential for precise diagnosis, planning, treatment, and monitoring of the tumor. However, the variations in tumor size, shape, and location, automating this process can be challenging. Segmentation using manual method is both time-taking and can produce inconsistent results. A promising solution to automated brain tumor segmentation is through deep learning approach that use convolutional neural networks. The models that have used U-Net shown considerable promise in enhancing the precision and effectiveness of automated Brain tumor segmentation. Still, there is a scope for improvement in accurate segmentation of brain MRI images. The segmentation of Brain MRI Images greatly helps in earlier diagnosis of Brain tumor effectively. The proposed model using ResNet, ResNext, and Inception as a backbone of U-Net with BraTs 2020 dataset could potentially reduce the workload of radiologists and provide more consistent results across experts. Future research can explore the use of other architectures or hybrid models to enhance brain tumor segmentation performance. © 2024 IEEE.

关键词： Tumors

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Research on Accuracy Verification of an Intelligent Tennis Practicing Device Based on Stochastic Differential Equations 2

Research on Accuracy Verification of an Intelligent Tennis P...

引用

2nd international conference on Artificial Intelligence, Human-Computer Interaction and robotics, AIHCIR 2023

作者： Liang, Xianrong Yu, Jiangxi Song, Qi Yan, Cheng School of Art and Design Guangdong University of Finance and Economics Guangzhou China International Office Guangzhou Academy of Fine Arts Guangzhou China College of Physical Education Hunan City University Yiyang China

ISBN: (纸本)9798350360363

This Research focuses on the intelligent tennis practicing device design based on stochastic differential equations. By constructing the Fokker-Planck equation, we aim to test the accuracy of the algorithm and improve the precision of data feedback. We use the intelligent tennis practicing device of the Hong Kong Swallowsing Sports technology Co., LTD., as the research object. By reading the data of simulated physical tennis through sensors, such as moving acceleration, angular velocity, and tennis rotation speed, we perform data selection and calculation to generate the virtual three-dimensional tennis trajectory and landing point in the tennis court. At the same time, we provide corresponding training guidance based on the athlete's performance and training needs to improve the training effect. This research provides a new method and approach for the design and accuracy verification of intelligent tennis practicing device. © 2023 IEEE.

关键词： Fokker Planck equation

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YOLOv5-Pothole: An Improved Pothole Perception Method Based on YOLOv5-Seg 2

YOLOv5-Pothole: An Improved Pothole Perception Method Based ...

引用

2nd international conference on Artificial Intelligence, Human-Computer Interaction and robotics, AIHCIR 2023

作者： Chen, Xiaokang Ma, Nan Wang, Mohan Beijing Union University Beijing Key Laboratory of Information Service Engineering Beijing China Beijing University of Technology Engineering Research Center of Intelligence Perception and Autonomous Control Beijing China Beijing University of Technology Faculty of Information and Technology Beijing China

ISBN: (纸本)9798350360363

Road pothole damage represents a significant challenge in transportation infrastructure. Numerous studies have aimed to automate pothole detection by employing diverse computer vision techniques. There is a critical need for an automated road pothole detection process that offers sufficient accuracy and speed, while also being cost-effective and straightforward to implement. We introduce an enhanced road pothole detection algorithm, YOLOv5-pothole, based on the YOLOv5-seg model. Inputs for this algorithm include RGB images, depth images, and camera position data, all captured by a D455 depth camera. Concurrently, LiDAR sensors are used to acquire 3D point cloud data. The RGB images are semantically segmented by the self-trained YOLOv5-seg network, and corresponding virtual point clouds are generated through coordinate transformation. Then we register the 3D point clouds with the virtual point clouds generated from 2D pothole images to supplement the semantic information of the pothole. Finally, the pothole point clouds are individually segmented using Euclidean clustering, providing the information of the pothole. The GRDDC2020 experimental dataset was used to train the YOLOv5-seg network. We tested the YOLOv5-pothole algorithm on five randomly selected road potholes, with experimental results indicating a mean detection error of 7.0%. The algorithm demonstrated effective detection across potholes of varying sizes. This study offers valuable insights for future real-time road pothole detection research. © 2023 IEEE.

关键词： Object detection

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End-Effector Impedance Control of Robotic Arm Based on Enhanced Neural Network RBF-PID-PSO 2

End-Effector Impedance Control of Robotic Arm Based on Enhan...

引用

2nd international conference on Artificial Intelligence, Human-Computer Interaction and robotics, AIHCIR 2023

作者： Wu, Gengyao Zhang, Pengfei Zhang, Jie Ma, Ang Xu, Weitao Hangzhou Power Supply Company of State Grid Zhejiang Electric Power Co.Ltd. Market Technology Center Zhejiang Province China Modern Postal College Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798350360363

The robotic arm's reliance on the location of the terminal based on camera positioning for electrical power transformer calibration systems has resulted in errors in the wiring terminals' insertion and extraction. Unfortunately, this approach fails to ensure exact assembly of transformer terminals. For a seamless terminal assembly, active compliance control must be incorporated between the wiring terminal and An end-effector on a robotic arm. This work offers an algorithm for control of mechanical arm end-effector impedance for terminal assembly according to RBF-PID-PSO. This algorithm modifies the center and weight matrix of the Radial Basis Function (RBF) neural network using a particle swarm optimization technique. The robotic arm model incorporates the impedance characteristics from the RBF neural network into an implementation of a PID algorithm that optimizes impedance model of end-effector forces. This makes it possible to convert end-effector forces into position errors in order to control impedance, which can subsequently be converted into six joint angles using the inverse kinematics of the robot. Simultaneously. The controller, demonstrated by Matlab experiments and simulations, is capable of control of end-effector forces through impedance, thus reducing errors in the control of force and torque. It also exhibits an improved ability to withstand force feedback signal disturbances, which greatly improves conforming control operation. © 2023 IEEE.

关键词： Errors

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Predicting Breast Cancer Malignancy using Deep Neural Networks 2

Predicting Breast Cancer Malignancy using Deep Neural Networ...

引用

2nd international conference on Smart Technologies, Communication and robotics, STCR 2022

作者： Swetha, V. Vadivu, G. SRM Institute of Science and Technology Data Science and Business Systems Tamilnadu Kattankulathur India

ISBN: (纸本)9781665460477

A lot of people are scared of cancer since it's so deadly. However, if caught and treated early, cancer has a high chance of being cured. The ability of computer-assisted diagnosis to serve as a primary screening test for many illnesses, including cancer, has contributed to its rise in popularity in recent years. Deep learning is an artificial intelligence technology that gives computers intelligence by programming them to think like people. In this study, we explore the feasibility of training a deep neural network to provide such a prediction for breast cancer. Information is taken from a UCI-supplied dataset on breast cancer in Wisconsin. Over fitting is prevented by the early halting mechanism and the dropout layers in the neural network model, which together allow for an F1 score of more than 97. © 2022 IEEE.

关键词： Deep neural networks

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Parsimonious Technologies for Sensing Upper Limb Muscles Activation 2

Parsimonious Technologies for Sensing Upper Limb Muscles Act...

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2nd Edition IEEE international conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023

作者： Azhar, Mahrukh Luciani, Beatrice Bednarczyk, MacIej Wach, Benoit Gandolla, Marta Bayle, Bernard ICube Laboratory University of Strasbourg 1 Pl. de l'Hôpital Strasbourg67000 France WE-COBOT Laboratory Polo Territoriale di Lecco Politecnico di Milano Via G.Previati 1/c Lecco23900 Italy Department of Mechanical Engineering Politecnico di Milano Via La Masa 1 Milano20156 Italy

ISBN: (纸本)9798350300802

Low-cost sensing technologies are experiencing significant growth. They can offer cost-effective and simpler alternatives to existing systems at cost of accuracy. This has the potential to drive innovation and allow rehabilitation robotics to expand. This expansion would facilitate the integration of rehabilitation robotics into clinical contexts. This paper addresses the analysis of force myography (FMG) as an alternative to surface electromyography (sEMG) for sensing upper limb muscle activation in the case of elbow flexion and extension. This analysis will ultimately allow the design and proof of concept of a parsimonious wearable sensing technology that consists of a low-cost force sensor embedded in a proper ergonomic structure. © 2023 IEEE.

关键词： low-cost parsimonious technology rehabilitation robotics wearable sensors

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Multiple Attitude Estimation Models based on a Pressure Sensor Array 2

Multiple Attitude Estimation Models based on a Pressure Sens...

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2nd international conference on robotics, Automation and Intelligent Control, ICRAIC 2022

作者： Wang, Chongyang Yu, Huapeng National Innovation Institute of Defense Technology Academy of Military Science Beijing China

Autonomous navigation is critical for autonomous underwater vehicle to complete underwater tasks, and attitude is an essential parameter of autonomous navigation. Inspired by the biomimicry studies, three attitude estimation models based on a pressure sensor array are constructed, which are the direct method, the multi-layer perception model and the attitude estimation model based on physical knowledge respectively. The field tests are carried out to verify the effectiveness of the proposed methods, and the advantages and disadvantages of the three methods are compared. It turns out that the attitude estimation model based on physical knowledge achieves the optimal results, and the estimated error of pitch is lower than 1.9133°, and the estimated error of roll is lower than 2.6719°. Further, there is no cumulative attitude error, which complies that the attitude estimation model based on physical knowledge has a high potential for applications in autonomous navigation systems. © Published under licence by IOP Publishing Ltd.

关键词： Autonomous underwater vehicles

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An Enhanced Approach for Detecting Alzheimer's Disease 2

An Enhanced Approach for Detecting Alzheimer's Disease

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2nd international conference on Smart Technologies, Communication and robotics, STCR 2022

作者： Sanjay, V. Swarnalatha, P. Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Vellore India

ISBN: (纸本)9781665460477

Alzheimer's disease affects most of the elderly in today's world. It directly affects the neurotransmitters and leads to dementia. MRI images can spot brain irregularities related to mild cognitive damage. It can be useful for predicting Alzheimer's disease, though it is a big challenge. In this research, a novel technique is proposed to find to detect Alzheimer's disease using Adaboost classifier with a hybrid PSO algorithm. Initially, MRI image features are extracted, and the best features are identified by the curvelet transform and Principal Component Analysis (PCA). Adaboost proposed methods yield greater accuracy than the existing systems for analyzing MRI images and give excellent classification accuracy. To evaluate the proposed method three methods metrics namely accuracy, specificity, and sensitivity are used. Based on the results the proposed methods yield greater accuracy than the existing systems. © 2022 IEEE.

关键词： Support vector machines

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