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

作者： Amirmasood Khaloujini Amin Talaeizadeh Alireza Taheri Aria Alastya Advanced Research Lab for Control and Agricultural Robotics Department of Mechanical Engineering Social and Cognitive Robotics Lab. Sharif University of Technology Tehran Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

Optimal route planning for autonomous robots is a crucial element of contemporary agriculture, especially for activities like spraying and pollination. This paper presents a Graph Convolutional Network (GCN) model especially developed to address the Coverage Path Planning (CPP). The aim of this paper is to convert the CPP problem into a Traveling Salesman Problem (TSP), so that by solving the TSP using the proposed neural network, an optimal route can be extracted for each agricultural field in the shortest possible time and with sufficient accuracy, ensuring full coverage of the field's area. The proposed model is a GCN, augmented with a transformer layer and a Multi-Layer Perceptron (MLP), was trained using datasets produced by a Genetic Algorithm (GA) Method. The model obtained a network error of 0.12 while solving TSP cases with 20 points. It showed robust generalization skills, as evidenced by validation and test errors of 0.1261 and 0.1264, respectively. Significantly, the GCN model reduces the solution time to approximately one-tenth compared to conventional approaches, a benefit that increases as the number of points increases. The effectiveness of the model in producing ideal coverage routes highlights its potential for wider use in optimization and coverage path planning, especially in dynamic and extensive agricultural environments.

关键词： Training Accuracy Graph convolutional networks Neural networks Spraying Traveling salesman problems Transformers Path planning Vehicle dynamics Genetic algorithms

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Physics-Informed Deep Learning-Based Monocular Vision and IMU Fusion for Extracting Position and Heading Data of Multirotors

Physics-Informed Deep Learning-Based Monocular Vision and IM...

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

作者： Arshia Rezaei Erfan Radfar Amin Talaeizadeh Aria Alasty Department of Mechanical Engineering Advanced Research Lab for Control and Agricultural Robotics (Sharif AgRoLab) Sharif University of Technology

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

Multirotor Unmanned Aerial Vehicles (UAV)s flight performance rely on accurate estimation of their attitude and position for stable and robust control, which is essential for precise path following and management. Traditional methods for estimating six Degrees of Freedom (DoF) parameters, such as position, orientation, and velocity, often combine data from Global Navigation Satellite System (GNSS), Inertial Measurement Unit (IMU), and vision sensors using complex algorithms for fusion and filtering. This approach can lead to challenges in real-time processing and robustness to environmental variations. In this paper, we propose a novel approach that leverages deep learning and incorporates a physics-informed loss function based on monocular vision data to enhance the accuracy of these sensors by eliciting position and heading data from a trained model of flow detection, enhanced with a mathematical model of optical flow. We implement a fusion of camera, IMU, magnetometer, and ultrasonic sensors for robust and accurate 6 DoF data acquisition in multirotor systems. Our proposed framework utilizes Convolutional Neural Networks (CNN)s to directly learn the mapping between sensor data and 6 DoF parameters. By jointly processing visual and inertial data in an end-to-end manner, our model exploits the complementary information provided by each sensor modality, thereby enhancing estimation accuracy and robustness to external disturbances. We demonstrate the effectiveness of our approach through extensive experiments on real-world multirotor platforms, show-casing significant improvements in pose and motion estimation accuracy compared to conventional methods. Our fusion deep learning framework offers a promising pathway for enhancing the autonomy and performance of multi rotor UAVs.

关键词： Deep learning Accuracy Measurement units Magnetic sensors Motion estimation Estimation Autonomous aerial vehicles Robot sensing systems Robustness Real-time systems

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3D Affordance Keypoint Detection for Robotic Manipulation

3D Affordance Keypoint Detection for Robotic Manipulation

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Zhiyang Liu Ruiteng Zhao Lei Zhou Chengran Yuan Yuwei Wu Sheng Guo Zhengshen Zhang Chenchen Liu Marcelo H. Ang Francis EH Tay Advanced Robotics Centre National University of Singapore Singapore Department of Mechanical Engineering National University of Singapore Singapore

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This paper presents a novel approach for affordance-informed robotic manipulation by introducing 3D keypoints to enhance the understanding of object parts’ functionality. The proposed approach provides direct information about what the potential use of objects is, as well as guidance on where and how a manipulator should engage, whereas conventional methods treat affordance detection as a semantic segmentation task, focusing solely on answering the what question. To address this gap, we propose a Fusion-based Affordance Keypoint Network (FAKP-Net) by introducing 3D keypoint quadruplet that harnesses the synergistic potential of RGB and Depth image to provide information on execution position, direction, and extent. Benchmark testing demonstrates that FAKP-Net outperforms existing models by significant margins in affordance segmentation task and keypoint detection task. Real-world experiments also showcase the reliability of our method in accomplishing manipulation tasks with previously unseen objects. Our source code and video demo will be public.

关键词： Three-dimensional displays Codes Affordances Source coding Semantic segmentation Focusing Manipulators Planning Reliability Intelligent robots

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Comparing the Performance of Quaternion, Rotation Matrix, and Euler Angles Based Attitude PID Controllers for Quadrotors

Comparing the Performance of Quaternion, Rotation Matrix, an...

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

作者： Arshia Rezaei Amin Talaeizadeh Aria Alasty Department of Mechanical Engineering Advanced Research Lab for Control and Agricultural Robotics (Sharif AgRoLab) Sharif University of Technology

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

Attitude states are critical parameters in the control, navigation, and guidance of aerial robotics. Various models, including Euler angles, rotation matrices, and quaternions, are used to represent the orientation of multirotors. The performance of a quadrotor's attitude controller is significantly influenced not only by optimal gain design but also by the structure and strategy of error allocation and control command management. Given the dynamic behavior of the quadrotor, challenges arise with the Proportional-Integral-Derivative (PID) controller, which, as a Single Input Single Output (SISO) controller, struggles to manage the system's channels independently. This paper presents three attitude control strategies for a quadrotor, focusing on both controller design and state estimation by utilizing three-dimensional spatial rotation models. The aim is to compare and improve quadrotor attitude control strategies by applying diverse criteria through these spatial rotation models. The models are implemented on a 3 Degrees of Freedom (DoF) quadrotor stand. The comparison evaluates tracking error, energy consumption, computational effort, and other key performance metrics.

关键词： Solid modeling Attitude control Quaternions Computational modeling 3-DOF Stability criteria Resource management State estimation Robots Quadrotors

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On Reducing the Localisation Error of Modern Mobile Robotic Platforms 15

On Reducing the Localisation Error of Modern Mobile Robotic ...

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15th CIRP Conference on Intelligent Computation in Manufacturing engineering, ICME 2021

作者： James, Jithin Clarke, Graham Mathew, Robins Mulkeen, Brian Papakostas, Nikolaos School of Electrical and Electronic Engineering University College Dublin Ireland Laboratory for Advanced Manufacturing Simulation and Robotics School of Mechanical and Materials Engineering University College Dublin Ireland

From drug delivery in hospitals to transportation of goods in warehouses, mobile robotic platforms have a wide variety of applications in industry today. The integration of mobile platforms with a manipulator opens up the possibility of more practical applications. However, this also introduces a new problem regarding the localisation accuracy of the platform, which in turn affects the accuracy of the manipulation. This paper details a comparative study of different strategies for improving the accuracy of pick and place tasks using a mobile robotic platform with an integrated arm. These strategies include fine positioning, LiDAR feedback and vision system feedback. © 2022 The Author(s).

关键词： Error compensation

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Reliability Prediction of Wrought Carbon Steel Castings under Fatigue Loading Using Coupled Mold Optimization and Finite Element Simulation

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Computer Modeling in engineering & Sciences 2024年第12期141卷 2325-2350页

作者： Muhammad Azhar Ali Khan Syed Sohail Akhtar Abba AAbubakar Muhammad Asad Khaled S.Al-Athel Mechanical Engineering Department Prince Mohammad Bin Fahd UniversityAlkhobar34754Saudi Arabia Mechanical Engineering Department King Fahd University of Petroleum and MineralsDhahran31261Saudi Arabia Interdisciplinary Research Center for Intelligent Manufacturing and Robotics King Fahd University of Petroleum and MineralsDhahran31261Saudi Arabia Interdisciplinary Research Center for Advanced Materials King Fahd University of Petroleum and MineralsDhahran31261Saudi Arabia Interdisciplinary Research Center for Industrial Nuclear Energy King Fahd University of Petroleum&MineralsDhahran31261Saudi Arabia

The fatigue life and reliability of wrought carbon steel castings produced with an optimized mold design are predicted using a finite element method integrated with reliability *** optimization of the mold is carried out using MAGMASoft mainly based on porosity reduction as a *** validating the initial mold design with experimental data,a spring flap,a common component of an automotive suspension system is designed and optimized followed by fatigue life prediction based on simulation using *** taking into consideration the variation in both stress and strength,the stress-strength model is used to predict the reliability of the component under fatigue *** typical loading conditions of 70 kN,the analysis showed that 95%of the steel spring flaps achieve infinite ***,under maximum loading conditions of 90 kN,reliability declined significantly,with only 65%of the spring flaps expected to withstand the stress without *** study also identified a safe load-induced stress of 95 MPa on the spring *** findings suggest that transitioning from forged to cast spring flaps is a promising option,particularly if further improvements in casting design reduce porosity to negligible levels,potentially achieving 100%reliability under typical loading *** integrated approach of mold optimization coupled with reliability estimation under realistic service loading conditions offers significant potential for the casting industry to produce robust,cost-effective products.

关键词： Casting optimization simulation finite element reliability automotive suspension

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Off-Design Performance of 9F Gas Turbine Based on gPROMs and BP Neural Network Model

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Journal of Thermal Science 2022年第1期31卷 261-272页

作者： HAO Xuedi SUN Lei CHI Jinling ZHANG Shijie School of Mechanical Electronic&Information Engineering China university of Mining&Technology-BeijingBeijing 100083China Key Laboratory of Intelligent Mining and Robotics Ministry of Emergency ManagementBeijing 100083China Key Laboratory of Advanced Energy and Power Institute of Engineering ThermophysicsChinese Academy of SciencesBeijing 100190China

Gas turbines are increasingly and widely used,whose research and production reflect a country’s industrial capacity and *** to the changeable working environment,gas turbines usually work under the condition of simultaneous changes of ambient temperature,load and ***,the current researches mainly focus on the change in single condition,and do not fully consider the simultaneous change in different *** the basis of single condition,this paper further studies the dual off-design performance of gas turbines under three conditions:temperature-load,fuel-load and ***,the whole machine model of a gas turbine is established,in which the compressor model has the greatest impact on the performance of gas ***,this paper obtains a more accurate compressor model by combining the engineering modeling advantages of gPROMs and the powerful mathematical calculation ability of MATLAB neural ***,according to the established gas turbine model,the dual off-design performance is studied,which is mainly based on the parameter of output and *** result shows that the efficiency and power output of gas turbines will decrease with the increase of ambient *** the decrease of fuel calorific value,power output and efficiency will *** the load decreases,the efficiency of the gas turbines will decrease,and these changes are consistent with the single off-design ***,when the fuel and temperature change simultaneously,only adjusting the IGV angle cannot avoid the surge when the temperature is above 30°*** this time,it is necessary to adjust the extraction rate in order to ensure the safe and stable operation of gas ***,the research on dual off-design performance of gas turbines has an important significance for the peak shaving operation of gas turbines.

关键词： off-design performance gas turbine gPROMs MATLAB neural network peak shaving operation

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Developmental Prediction of Poststroke Patients in Activities of Daily Living by Using Tree-Structured Parzen Estimator-Optimized Stacking Ensemble Approaches

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IEEE Journal of Biomedical and Health Informatics 2024年第5期28卷 2745-2758页

作者： Lin, Pei-Hua Kuo, Ping-Huan Chen, Kuan-Lin China Medical University An Nan Hospital Tainan70965 Taiwan National Chung Cheng University Department of Mechanical Engineering Chiayi62102 Taiwan National Chung Cheng University Advanced Institute of Manufacturing with High-tech Innovations Chiayi62102 Taiwan National Pingtung University Department of Intelligent Robotics Pingtung90004 Taiwan

Poststroke injuries limit the daily activities of patients and cause considerable inconvenience. Therefore, predicting the activities of daily living (ADL) results of patients with stroke before hospital discharge can assist clinical workers in formulating more personalized and effective strategies for therapeutic intervention, and prepare hospital discharge plans that suit the patients needs. This study used the leave-one-out cross-validation procedure to evaluate the performance of the machine learning models. In addition, testing methods were used to identify the optimal weak learners, which were then combined to form a stacking model. Subsequently, a hyperparameter optimization algorithm was used to optimize the model hyperparameters. Finally, optimization algorithms were used to analyze each feature, and features of high importance were identified by limiting the number of features to be included in the machine learning models. After various features were fed into the learning models to predict the Barthel index (BI) at discharge, the results indicated that random forest (RF), adaptive boosting (AdaBoost), and multilayer perceptron (MLP) produced suitable results. The most critical prediction factor of this study was the BI at admission. Machine learning models can be used to assist clinical workers in predicting the ADL of patients with stroke at hospital discharge. © 2013 IEEE.

关键词： Forecasting

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Motion Planning of Mobile Robot with Non-holonomic Constraints 18

Motion Planning of Mobile Robot with Non-holonomic Constrain...

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Zhang, Kewen Liu, Weilong Wang, Jin Chen, Leijie Lu, Guodong Zhejiang University of Technology Key Laboratory of Special Purpose Equipment and Advanced Processing Technology Ministry of Education College of Mechanical Engineering Hangzhou310023 China Robotics Institute of Zhejiang University Hangzhou310027 China Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Hangzhou310027 China Robotics Research Center of Yuyao City Ningbo315400 China

ISBN: (纸本)9798350312201

This paper proposes a modified spatio-temporal A∗ path planning algorithm for the trajectory planning of mobile robots with non-holonomic constraints. Most path planning algorithms treat mobile robots as to be with holonomic constraints. However, wheeled mobile robots are affected by non-holonomic constraints, which leads to trajectory unreachability. To solve this problem, an improved spatio-temporal A∗ algorithm based on non-holonomic constraints is given, which considers the efficiency, completeness and smoothness of the path. At the same time, by improving the construction form of collision constraint and adding spatio-temporal constraints to the path, the success rate and solving speed of the path planning algorithm for non-holonomic constrained mobile robot are increased. Finally, simulation experiments are given to indicate that the proposed planning algorithm provides an optimized path, while improving the planning success rate and reducing the computational time. © 2023 IEEE.

关键词： Motion planning

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In-depth analysis of recall initiators of medical devices with a Machine Learning-Natural language Processing workflow

arXiv

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arXiv 2024年

作者： Hu, Yang Ghadimi, Pezhman Laboratory for Advanced Manufacturing Simulation and Robotics School of Mechanical & Materials Engineering University College Dublin Belfield Dublin Ireland

Persistent quality problems with medical devices and the associated recall present potential health risks to patients and users, bringing extra costs to manufacturers and disturbances to the entire supply chain (SC). Recall initiator identification and assessment are the preliminary steps to prevent medical device recall. Conventional analysis tools are inappropriate for processing massive and multi-formatted data comprehensively and completely to meet the higher expectations of delicacy management with the increasing overall data volume and textual data format. To address these problems, this study presents a bigdata-analytics-based Machine learning (ML) - Natural language Processing (NLP) tool to identify, assess and analyse the medical device recall initiators based on the public medical device recall database from 2018 to 2024. Results suggest that the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm can present each single recall initiator in a specific manner, therefore helping practitioners to identify the recall reasons comprehensively and completely. This is then followed by text similarity-based textual classification to assist practitioners in controlling the group size of recall initiators and provide managerial insights from the operational to the tactical and strategic levels. More proactive practices and control solutions for medical device recalls are expected in the future. © 2024, CC BY-NC-ND.

关键词： Machine learning

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