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检索条件"机构=Dept. of Automation and Robotics Engineering"

共 197 条记录，以下是41-50 订阅

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Fuzzy Controlled Adaptive Follow the Gap Obstacle Avoidance Algorithm 21

Fuzzy Controlled Adaptive Follow the Gap Obstacle Avoidance ...

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Proceedings of the 7th International Conference on robotics and Artificial Intelligence

作者： Bilal Yucel Abdurrahman Yilmaz Osman Ervan Hakan Temeltas Control and Automation Engineering Dept. Robotics Laboratory Istanbul Technical University Turkey

ISBN: (纸本)9781450385855

Follow the Gap Method (FGM) and Improved Follow the Gap Method (FGM-I) are geometric obstacle avoidance algorithms for navigation. In these methods, the vehicle detects the gaps around the object and navigates to the midpoint of the optimal gap calculated according to a defined function. One missing point of these algorithms is failure to the goal point when there is an obstacle near to it. Another drawback is that early consideration of obstacles causes long trajectories. In this paper, Adaptive Follow the Gap (A-FGM) is presented to overcome these two points. In A-FGM, a fuzzy controlled evaluation radius is set and only obstacles within this region are included in the evaluation. A differential drive robot is used in simulations and results show that A-FGM increases the success rate of reaching the goal and efficiency of previous algorithms. The source code of the developed approach is shared on GitHub 1.

关键词： Mobile Robots Fuzzy Control Path Planning Robot Navigation Adaptive Control Obstacle Avoidance

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Comparative Performance Analysis of Lithium-ion and Lead-acid Batteries for Electric Scooters: A Simulink Modelling Approach

Comparative Performance Analysis of Lithium-ion and Lead-aci...

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Information Technologies and Communications (GCITC), Global Conference on

作者： Aashish P Puradannavar Anish P Puradannavar Rakesh P. Tapaskar Dept. of Electrical and Electronics Engineering BVB College of Engineering Campus Hubballi KLE Technological University India Dept. of Automation and Robotics BVB College of Engineering Campus Hubballi KLE Technological University India

The increasing fascination in electric scooters as an environmentally friendly means of transportation in metropolitan areas has placed significant emphasis on the performance of their power sources, specifically batteries. The present work undertakes a complete comparative examination of two widely used battery technologies, namely Lithium-ion (Li-ion) and Lead-acid. The models for both types of batteries were methodically constructed using the powerful capabilities of Simulink, which is a simulation-based environment. The aim of the study is to evaluate the performance metrics of the subjects under controlled and consistent conditions, in order to gain valuable insights about their compatibility with electric scooters. The initial findings suggest that there are noticeable differences in performance between the two, with each exhibiting distinct benefits and difficulties. This study not only makes a valuable contribution to the scholarly discussion on battery technologies but also offers practical insights for manufacturers and players in the electric scooter market, facilitating well-informed decision-making.

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Robot-Assisted Navigation for Visually Impaired through Adaptive Impedance and Path Planning

Robot-Assisted Navigation for Visually Impaired through Adap...

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IEEE International Conference on robotics and automation (ICRA)

作者： Pietro Balatti Idil Ozdamar Doganay Sirintuna Luca Fortini Mattia Leonori Juan M. Gandarias Arash Ajoudani HRI2 Lab Istituto Italiano di Tecnologia Genoa Italy Dept. of Informatics Bioengineering Robotics and System Engineering University of Genoa Genoa Italy Systems Engineering and Automation Department Robotics and Mechatronics Lab University of Malaga Malaga Spain

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper presents a framework to navigate visually impaired people through unfamiliar environments by means of a mobile manipulator. The Human-Robot system consists of three key components: a mobile base, a robotic arm, and the human subject who gets guided by the robotic arm via physically coupling their hand with the cobot’s end-effector. These components, receiving a goal from the user, traverse a collision-free set of waypoints in a coordinated manner, while avoiding static and dynamic obstacles through an obstacle avoidance unit and a novel human guidance planner. With this aim, we also present a legs tracking algorithm that utilizes 2D LiDAR sensors integrated into the mobile base to monitor the human pose. Additionally, we introduce an adaptive pulling planner responsible for guiding the individual back to the intended path if they veer off course. This is achieved by establishing a target arm end-effector position and dynamically adjusting the impedance parameters in real-time through a impedance tuning unit. To validate the framework we present a set of experiments both in laboratory settings with 12 healthy blindfolded subjects and a proof-of-concept demonstration in a real-world scenario.

关键词： Target tracking Navigation Robot kinematics End effectors Real-time systems Path planning Sensors

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Using The Machine Learning Method in Portable ECG Monitoring System to Identify Diagnostically Complex Heart Diseases

Using The Machine Learning Method in Portable ECG Monitoring...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Aidana K. Yerkebay Zhadyra N. Alimbayeva Chingiz A. Alimbayev Aiman K. Ozhikenova Nurlan A. Bayanbay Kassymbek A. Ozhikenov dept. of Robotics and technical means of automation Satbayev University Almaty Kazakhstan dept. of Robotics and technical means of automation Satbayev University Kazakh National Women's Teacher Training University Almaty Kazakhstan Satbayev University U. Joldasbekov Institute of Mechanics and Engineering Almaty Kazakhstan

Due to the high level of disability and death caused by heart diseases worldwide, the problem of combating cardiovascular diseases is becoming more and more important and relevant. In fact, one of the leading causes of death worldwide has long been cardiovascular disease, according to the World Health Organization. Among heart diseases, the most dangerous to life is myocardial infarction. In connection with the rapid morbidity, early diagnosis of this disease is one of the topical problems of modern public health care. At present, although there is a wide range of different statistical, math methods and computer tools for the processing of cardiological information, the task of increasing the accuracy of methods for the analysis of electrocardiograms and diagnostic tools in this field of study remained relevant. Taking into account the imperfect tools and systems for diagnosing heart diseases, as well as the human factor, the lack of a method that can help doctors to diagnose myocardial infarction early requires the development of new diagnostic information methods. This article presents a neural network analysis methodology for diagnosing myocardial infarction based on the joint analysis of direct and reverse signs of myocardial infarction from electrocardiographic information obtained from a portable cardiac analyzer.

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A controller for reaching and unveiling a partially occluded object of interest with an eye-in-hand robot

A controller for reaching and unveiling a partially occluded...

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IEEE-RAS International Conference on Humanoid Robots

作者： Dimitrios Papageorgiou Leonidas Koutras Zoe Doulgeri Dept. of Electrical & Computer Engineering Automation & Robotics Lab Aristotle University of Thessaloniki Greece

ISBN: (纸本)9798350309805

In this work, a control scheme for approaching and unveiling a partially occluded object of interest is proposed. The control scheme is based only on the classified point cloud obtained by the in-hand camera attached to the robot's end effector. It is shown that the proposed controller reaches in the vicinity of the object progressively unveiling the neighborhood of each visible point of the object of interest. It can therefore potentially achieve the complete unveiling of the object. The proposed control scheme is evaluated through simulations and experiments with a UR5e robot with an in-hand RealSense camera on a mock-up vine setup for unveiling the stem of a grape cluster.

关键词： Point cloud compression Robot vision systems Pipelines Humanoid robots Cameras End effectors

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Cooperative obstacle avoidance method of multiple unmanned underwater vehicles based on improved artificial potential field method

Cooperative obstacle avoidance method of multiple unmanned u...

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Chinese Control and Decision Conference, CCDC

作者： HongLi Xu Kuo Luan BenQing Jia HaiTao Gu Dept. Faculty of Robot Science and Engineering Northeastern University Shenyang China Dept. State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

Aiming at the obstacle avoidance problem of multiple unmanned underwater vehicles (UUV) formation in different obstacle avoidance scenarios, the collaborative collision avoidance algorithm of multiple UUV formation was studied. In terms of obstacle avoidance algorithm, an algorithm combining artificial potential field method and consistent formation control algorithm is proposed. A variety of obstacle scenes are set by using the information of forward looking sonar, and different weights are set for the fusion of control instructions of the two algorithms. The problems of formation recovery time and formation error caused by formation "grouping" in the course of UUV obstacle avoidance are solved. Finally, in the underwater simulation platform, the effectiveness and advantages of the collaborative obstacle avoidance algorithm in multiple scenes are verified by experiments.

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Design and Analysis of Knee Joint for Transfemoral Amputees

Design and Analysis of Knee Joint for Transfemoral Amputees

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2021 International Conference on Artificial Intelligence and Mechatronics Systems, AIMS 2021

作者： Qadir, Muhammad Usman Khan, Muhammad Awais Hussain, Muzammal Ul Haq, Izhar Akhtar, Nizar Shah, Kmaran Advanced Robotics Automation Lab University of Engineering Technology Dept. of Mechatronics Engineering Peshawar Pakistan University of Engineering Technology Dept. of Mechatronics Engineering Peshawar Pakistan International Committee of Red Cross Sanaa Yemen

ISBN: (纸本)9781665424820

Lower limb amputation not only reduces mobility but also results in significant impairment in quality of life. Microprocessor based transfemoral knee joints have gained significant importance in the last decade to not only restore cosmetics but also provide functionality comparable to real limb. One of the key issues in such devices is selection of the suitable material for manufacturing of knee joint to minimize chances of failure under various loading conditions, while at the same time ensuring the weight of the knee is within acceptable limits. The purpose of this research is to design a microprocessor-based knee joint and perform various static structural analysis for different materials to optimize strength to weight ratio of the proposed design. Initially, a model for the proposed microprocessor-based knee was developed in SolidWorks followed by displacement study and computation of von-mises stresses in Ansys Workbench. Different materials used in the analysis include Plain Carbon Steel, Stainless Steel 304, Aluminum 6061. Based on the results Aluminum 6061 was identified as a suitable material for manufacturing knee joint as it not only reduces weight of the knee joint but also the deformation and stress values are within acceptable limits. The minimum and maximum Von Mises stress of Aluminum 6061 was 559.9 Pa and 71.37 MPa respectively with tensile strength of 313 MPa. © 2021 IEEE.

关键词： Manufacture

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Steel Defect Detection in Bridges Using Deep Encoder-Decoder Networks 13

Steel Defect Detection in Bridges Using Deep Encoder-Decoder...

引用

13th International Workshop on Structural Health Monitoring: Enabling Next-Generation SHM for Cyber-Physical Systems, IWSHM 2021

作者： Ahmed, Habib La, Hung Manh Advanced Robotics and Automation Laboratory Dept. of Computer Science and Engineering University of Nevada Reno RenoNV89527 United States

ISBN: (纸本)9781605956879

Recent major accidents related to bridges have emphasized the need for developing effective technological solutions for defect detection, which can minimize the possibility of bridge-related accidents in the future. In this respect, this research will focus towards development of automated system for the detection of defective regions within different steel parts of bridges. At present, there is no open-source image dataset, which can be used for this purpose. Consequently, the training dataset has been developed by using images acquired from bridges in Vietnam and validation was performed using images acquired from Lovelock bridge situated at Highway-80, Lovelock, NV, USA. A total of 5,500 (4,000 images for training and 1,500 for validation) images of different dimensions have been used the original dimensions of the steel bridge images have been modified 572 × 572 pixels, which have been used for the training and evaluation of the dataset on different Deep Encoder-Decoder networks. The use of diverse data from different bridges will allow the development of a robust Deep Encoder-Decoder network with considerable implications for practical systems in the future. This study will employ state-of-the-art Deep Encoder-Decoder network, which have been recently developed for other applications. However, no such study has been developed for defect detection in steel bridges. A comparative evaluation of different Deep Encoder-Decoder networks will be examined. At the same time, the performance of the system will be compared with recent advanced approaches. The results reveal the considerable potential of Deep Encoder-Decoder towards defect detection of steel bridges, which will be further exploited in the future studies. © 2021 Structural Health Monitoring 2021: Enabling Next-Generation SHM for Cyber-Physical Systems - Proceedings of the 13th International Workshop on Structural Health Monitoring, IWSHM 2021. All rights reserved.

关键词： Embedded systems

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Solution for Agri Disaster Using Climate Changes Adaptation by Ml 6

Solution for Agri Disaster Using Climate Changes Adaptation ...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Ganesh, L. Venkatesh, R. Prasanna Kumar, Kaushal Kumari, Khushboo Borkakoty, Sangeeta Raju, A. Rajalakshmi Engineering College Robotics and Automation Dept. Tamilnadu Chennai India SACS MAVMM Engineering College Department of Mechanical Engineering Tamilnadu Madurai India School of Life Sciences Department of Botany Mahatma Gandhi Central University Bihar India University of Science & Technology Department of Computer Science & Electronic Meghalaya India KLEF Vaddeswaram Green Field Department of Commerce Vijayawada India

ISBN: (纸本)9798350304480

This paper proposes using good sized getting to know fashions to recognize lesions of cotton leaves in the context of pictures of the reaping within the subject. Cotton is among the maximum economically essential plant harvests, as it's far grown all over the international. Its spread in tropical areas has made it the goal of a wide variety of farming irritations and illnesses, necessitating succesful plans. moreover, the consequences of commonplace disturbances and disorders are difficult to isolate inside the early stages, and the author may additionally battle to locate the suitable visible verification of a scientific difficulty. To help in resolving the difficulty, it contemporary inquiry proposes a solution that takes into consideration substantial training within the filtering of cotton leaves, making it possible to reveal the cotton yield's health and make higher judgments approximately its management. The studying models GoogleNet like Resnet50, which use convolutional cerebrum associations, performed an accuracy of 86.6 percentage and 89.2 percentage, respectively. assist vector machines, Closest k-pals, faux creativeness associations, and neuro-tender are some of the extraordinary as well as everyday tactics again for treatment of photographs. The convolutional cerebellum networks ended up being 25% extra unique, implying that this method can upload to a more strong and short investigation of the flora looking to fill inside the field. © 2023 IEEE.

关键词： Artificial Intelligence Convolutional Neural Networks Image Processing Precision Agriculture

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Robot Trajectron: Trajectory Prediction-based Shared Control for Robot Manipulation

Robot Trajectron: Trajectory Prediction-based Shared Control...

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IEEE International Conference on robotics and automation (ICRA)

作者： Pinhao Song Pengteng Li Erwin Aertbeliën Renaud Detry Dept. Mechanical Engineering Research Unit Robotics Automation and Mechatronics KU Leuven Leuven Belgium College of Computer Science and Software Engineering Shenzhen University Shenzhen China Flanders Make@KU Leuven Dept. Electrical Engineering Research Unit Processing Speech and Images KU Leuven Leuven Belgium

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

We address the problem of (a) predicting the trajectory of an arm reaching motion, based on a few seconds of the motion’s onset, and (b) leveraging this predictor to facilitate shared-control manipulation tasks, by reducing the operator’s cognitive load through assistance in their anticipated direction of motion. Our novel intent estimator, dubbed the Robot Trajectron (RT), produces a probabilistic representation of the robot’s anticipated trajectory based on its recent position, velocity and acceleration history. By taking arm dynamics into account, RT can capture the operator’s intent better than other SOTA models that only use the arm’s position, making it particularly well-suited to assist in tasks where the operator’s intent is susceptible to change. We derive a novel shared-control solution that combines RT’s predictive capacity to a representation of the locations of potential reaching targets. Our experiments demonstrate RT’s effectiveness in both intent estimation and shared-control tasks. We will make the code and data supporting our experiments publicly available at https://***/detry-lab/public/robot-trajectron

关键词： Codes Dynamics Estimation Grasping Probabilistic logic Cognitive load Trajectory

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