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8th International Conference on robotics and Automation engineering, ICRAE 2023

作者： Varghese, Glace Reddy, Thambbireddygare G. C. Menon, Anjana K Paul, Ajay Kochuvila, Sreeja Varma Divya, R. Bhat, Ravichandra Kumar, Navin Amrita Vishwa Vidyapeetham Department of Electronics and Communication Engineering Bangalore India Bosch Global Software Technologies Robotics BengaluruSX/PJ1-EM India

ISBN: (纸本)9798350327656

In this work, we developed algorithm for coordinated multi-robot system. It is developed for simultaneous localization and mapping (SLAM). Many novel applications of multi-robot system especially, in the industrial indoor environment and complex/dangerous environment are being envisioned. The proposed algorithm focuses on optimizing navigation and execution of tasks within an environment. We used TurtleBot3 Waffle model and robot operating system (ROS) platform and simulated in Gazebo for localization, navigation and comprehensive coverage. The algorithm achieves effective mapping, localization, navigation, and coverage. Algorithm also ensures seamless coordination among multiple robots within a unified transform-tree (tf-tree) facilitating synchronized movement and a holistic understanding of the environment. Furthermore, an innovative coverage path planning and submap division is achieved using this technique. It is expected that such development would lead to practical applications in the real environment. © 2023 IEEE.

关键词： Multipurpose robots

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Towards Collaborative Simultaneous Localization and Mapping: A Survey of the Current Research Landscape

Field Robotics

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Field robotics 2025年 2卷 971-1000页

作者： Pierre-Yves Lajoie Benjamin Ramtoula Fang Wu Giovanni Beltrame Department of Computer and Software Engineering Polytechnique Montréal Canada Oxford Robotics Institute Department of Engineering Science University of Oxford United Kingdom Desay SV Automotive Singapore

Motivated by the tremendous progress, we witnessed in recent years, this paper presents a survey of the scientific literature on the topic of Collaborative Simultaneous Localization and Mapping (C-SLAM), also known as multi-robot SLAM. With fleets of self-driving cars on the horizon and the rise of multi-robot systems in industrial applications, we believe that Collaborative SLAM will soon become a cornerstone of future robotic applications. In this survey, we introduce the basic concepts of C-SLAM and present a thorough literature review. We also outline the major challenges and limitations of C-SLAM in terms of robustness, communication, and resource management. We conclude by exploring the area's current trends and promising research avenues.

关键词： Robots Simultaneous localization and mapping Robot kinematics Robot sensing systems Collaboration Surveys Multi-robot systems Sensors Market research Trajectory

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Graph-Based Feature Learning for Cross-Project software Defect Prediction

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Computers, Materials & Continua 2023年第10期77卷 161-180页

作者： Ahmed Abdu Zhengjun Zhai Hakim A.Abdo Redhwan Algabri Sungon Lee School of Software Northwestern Polytechnical UniversityXi’anChina School of Computer Science Northwestern Polytechnical UniversityXi’anChina Department of Computer Science Hodeidah UniversityPO Box 3114Al-HudaydahYemen Research Institute of Engineering and Technology Hanyang UniversityAnsanKorea Department of Robotics Hanyang UniversityAnsanKorea

Cross-project software defect prediction(CPDP)aims to enhance defect prediction in target projects with limited or no historical data by leveraging information from related source *** existing CPDP approaches rely on static metrics or dynamic syntactic features,which have shown limited effectiveness in CPDP due to their inability to capture higher-level system properties,such as complex design patterns,relationships between multiple functions,and dependencies in different software projects,that are important for *** paper introduces a novel approach,a graph-based feature learning model for CPDP(GB-CPDP),that utilizes NetworkX to extract features and learn representations of program entities from control flow graphs(CFGs)and data dependency graphs(DDGs).These graphs capture the structural and data dependencies within the source *** proposed approach employs Node2Vec to transform CFGs and DDGs into numerical vectors and leverages Long Short-Term Memory(LSTM)networks to learn predictive *** process involves graph construction,feature learning through graph embedding and LSTM,and defect *** evaluation using nine open-source Java projects from the PROMISE dataset demonstrates that GB-CPDP outperforms state-of-the-art CPDP methods in terms of F1-measure and Area Under the Curve(AUC).The results showcase the effectiveness of GB-CPDP in improving the performance of cross-project defect prediction.

关键词： Cross-project defect prediction graphs features deep learning graph embedding

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Smart Robot: Artificial Intelligent Cleaning Robot with Wifi Boundary Sensing

Smart Robot: Artificial Intelligent Cleaning Robot with Wifi...

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International Conference on Smart Applications and Sustainability in the Artificial Intelligence of Things, SAS-AIoT 2024

作者： Tannous, Gresmarteneanos Ame, Ibrahim Kian, Nafiseh Yousefi Al-Turjman, Fadi Mlimwa, Cliford Peter Artificial Intelligence Software Information Systems Engineering Departments AI and Robotics Institute Research Centre for AI and IoT Nicosia Cyprus Research Centre for AI and IoT Faculty of Engineering University of Kyrenia Kyrenia Cyprus Department of Computer Engineering Cyprus International University Nicosia Cyprus

ISBN: (纸本)9783031782756

The environment surrounds our daily lives, whether we want to or not. When we have a clean environment, we can enjoy very healthy lifestyles. However, when we have dirty environments, it can cause a myriad of diseases, health risks, and worse, global pandemics like plagues. This became very apparent during the years of the COVID-19 pandemic when we had to sanitize our hands on an hourly basis after touching suspicious items that might have had human contact. This study entails a way to keep our environment clean by picking up trash and items that are left on the floor by other people. A smart robot that uses artificial intelligence to be able to not only move around and understand its surroundings, but also know if there is an object that is identified as trash and needs to be removed from that area. The smart robot is fitted with a high-definition camera for object detection and can see them from 3–4 m. From the image, it will scan the angle direction of the object, change its course, and follow it. After reaching the said location, it will begin its procedure of picking up and storing it in its trash compartment. The robot can be tuned to only clean a certain area and not cross its boundary by comparing fingerprints of registered Wi-Fi RSSIs. This method is most accurate for indoor boundary setting. For outdoors, a GPS method is available to track and bind the robot within a map circumference. Finally, all these metrics and location services can be viewed on a custom-made mobile application that complements the robot's features. What ties all this together is a cloud-based server that feeds the app data from the robot and stores it for future review. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Wi-Fi

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Feature Concatenation-Based Deep Learning Method for Multiclass Wound Classification

Feature Concatenation-Based Deep Learning Method for Multicl...

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2024 International Conference on Electrical and Computer engineering Researches, ICECER 2024

作者： Aljasem, Muteb Alhababi, Mustafa Javed, Ali Aldoulah, Zaid Abouheaf, Mohammed Mayyas, Mohammad Al-Rousan, Wasseem Bowling Green State University Robotics Engineering Bowling GreenOH United States Wayne State University Electrical and Computer Engineering DetroitMI United States University of Engineering and Technology Department of Software Engineering Taxila Pakistan Alasala colleges Electrical and Electronics Engineering Dammam Saudi Arabia Hamilton Miami University Department of Engineering Technology OH United States Philadelphia University Department of Electrical Engineering Amman Jordan

ISBN: (纸本)9798331539733

Chronic wounds represent a significant global healthcare challenge, affecting millions of individuals and leading to substantial financial burdens. Accurate classification of chronic wounds is crucial for guiding effective treatment strategies, improving patient outcomes, and reducing healthcare costs. However, existing methods in chronic wound classification predominantly focus on binary classification, with limited success in multi-class scenarios. Consequently, there is a need for diagnostic solutions that can reliably classify multiple wound types. To overcome these challenges, we present a novel feature concatenation-based deep learning method for reliable multiclass classification of chronic wounds. Specifically, we employed the DenseNet121 and XceptionNet models for generating effective and compact feature maps that are then passed to a Multilayer perceptron Classifier to accurately identify different types of wounds. The ability of DenseNet121 to truncate redundant features and XceptionNet to handle complex features with fewer computational resources allows us to generate compact and efficient feature maps. We evaluated the performance of our method using the publicly available AZH and Medetec datasets, as well as their augmented versions. Multiple augmentation techniques were employed to increase sample size and diversity, addressing overfitting and class imbalance issues. A rigorous experimental study performed on the AZH, Medetec, and augmented collections demonstrates the effectiveness of our method for multiclass wound classification. © 2024 IEEE.

关键词： Deep learning

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Autonomous Environmental Exploration and Target Tracking Control for Unmanned Surface Vehicles in Uncertain Environment

Autonomous Environmental Exploration and Target Tracking Con...

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2024 IEEE International Conference on Real-Time Computing and robotics, RCAR 2024

作者： Zhu, Kaiwei Kong, Shihan Wu, Fang Sun, Jinan Yu, Junzhi College of Engineering Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics Beijing100871 China SPIC Nuclear Energy Co. Ltd. China Peking University National Engineering Research Center for Software Engineering Beijing100871 China

ISBN: (纸本)9798350372601

In this paper, a set of methods of autonomous obstacle exploration, avoiding, and target-tracking control in the uncertain environment is proposed. First, a secure and reliable obstacle exploration framework via multi-sensors information fusion is developed for USV to autonomously perceive obstacles in unknown environments. Second, a modified dynamic artificial potential field-based path planning method is proposed to dynamically generate a safe and feasible path even without complete prior obstacle position information. Third, a robust sliding mode control law with extended state observer is designed to track expectational target in the dynamical and uncertain environment. Finally, the target-tracking control law has been validated through the simulation experiments. Besides, the proposed methods of obstacle exploration, path planning, and target-tracking control are comprehensively evaluated through the simulation experiments in WEBOTS software, which is closer to the real world. © 2024 IEEE.

关键词： Target tracking

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Adaptive-extremum seeking control algorithm to seek the odor source using a multi-agent system 41

Adaptive-extremum seeking control algorithm to seek the odor...

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第41届中国控制会议

作者： Meh Jabeen Qing-Hao Meng Li-Cheng Jin Sheng Jin Zhen-Yu Deng Institute of Robotics and Autonomous Systems School of Electrical and Information EngineeringTianjin University Department of Software Engineering The University of Lahore Tianjin Fire Science and Technology Research Institute of MEM

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This study aims to seek the odor source using a multi-agent *** main challenge of the odor source seeking is to search the optimal extremum in the presence of multiple local extrema in a gradient complex *** this,an adaptive-extremum seeking control(adaptive-ESC) algorithm has been *** are navigated to the vicinity of the source based on an estimated gradient using *** the proposed algorithm,both the integral gain and the amplitude of the perturbation signal are adjusted *** local convergence of the algorithm has been proved using averaging *** performance of the proposed algorithm is validated through simulations by employing a multi-agent system.

关键词： Odor source seeking Extremum seeking control Adaption approaches

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Crop protection and disease detection using artificial intelligence and computer vision: a comprehensive review

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Multimedia Tools and Applications 2025年第7期84卷 3723-3743页

作者： Shah, Kanish Sushra, Rajat Shah, Manan Shah, Dhairya Shah, Haard Raval, Megh Prajapati, Mitul Software Engineer Walmart SunnyvaleCA United States Atmiya Vidya Mandir Gujarat Surat India Department of Chemical Engineering School of Energy Technology Pandit Deendayal Energy University Gujarat Gandhinagar India Robotics ECE Concentration Northeastern University BostonMA United States Department of Computer Science Concentration in AI Hofstra University HempsteadNY United States Department of Chemical Engineering S. S Agrawal Institute of Engineering & Technology Gujarat Navsari India R &D Engineer Span Inspection Systems Pvt. Ltd Gujarat Gandhinagar India

The technological advancements in the field of agriculture have increased to a great extent in recent years, and many techniques have evolved from other techniques. Some methods are improved or upgraded from the previous versions by implementing a new model or using better hardware devices. This has been helpful for the farmers in increasing crop productivity, and the life expectancy of crops has also increased as the diseases inside or outside the crops can be detected much earlier, and learning at an early stage helps prevent other crops. In this paper, we have presented a study where many varieties of fruits and vegetables have been taken to determine which method was used for a particular crop. By analyzing the various works carried out by the authors, it was inferred that most of the works revolved around image processing and hyperspectral imaging. Due to this, we had also included most of the papers, particularly as the models and hardware components used were much better than other works. Then, a comparative study was done where different fruits and vegetables highlighted the two main areas: the method used and the accuracy obtained. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Crops

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RT-Swap: Addressing GPU Memory Bottlenecks for Real-Time Multi-DNN Inference 30

RT-Swap: Addressing GPU Memory Bottlenecks for Real-Time Mul...

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30th IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS 2024

作者： Kang, Woosung Lee, Jinkyu Lee, Youngmoon Oh, Sangeun Lee, Kilho Chwa, Hoon Sung DGIST Dept. of Electrical Engineering and Computer Science Korea Republic of Dept. of Computer Science and Engineering Korea Republic of Hanyang University Dept. of Robotics Korea Republic of Ajou University Department of Software and Computer Engineering Korea Republic of School of AI Convergence Soongsil University Korea Republic of

ISBN: (纸本)9798350358414

The increasing complexity and memory demands of Deep Neural Networks (DNNs) for real-Time systems pose new significant challenges, one of which is the GPU memory capacity bottleneck, where the limited physical memory inside GPUs impedes the deployment of sophisticated DNN models. This paper presents, to the best of our knowledge, the first study of addressing the GPU memory bottleneck issues, while simultaneously ensuring the timely inference of multiple DNN tasks. We propose RT-Swap, a real-Time memory management framework, that enables transparent and efficient swap scheduling of memory objects, employing the relatively larger CPU memory to extend the available GPU memory capacity, without compromising timing guarantees. We have implemented RT-Swap on top of representative machine-learning frameworks, demonstrating its effectiveness in making significantly more DNN task sets schedulable at least 72% over existing approaches even when the task sets demand up to 96.2% more memory than the GPU's physical capacity. © 2024 IEEE.

关键词： Deep neural networks

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Robust Counting in Overcrowded Scenes Using Batch-Free Normalized Deep ConvNet

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Computer Systems Science & engineering 2023年第9期46卷 2741-2754页

作者： Sana Zahir Rafi Ullah Khan Mohib Ullah Muhammad Ishaq Naqqash Dilshad Amin Ullah Mi Young Lee Institute of Computer Sciences and Information Technology(ICS/IT) The University of AgriculturePeshawar25130Khyber PakhtunkhwaPakistan Department of Convergence Engineering for Intelligent Drone Sejong UniversitySeoul05006Korea Collaborative Robotics and Intelligent Systems(CoRIS)Institute Oregon State UniversityORUSA Department of Software Sejong UniversitySeoul05006Korea

The analysis of overcrowded areas is essential for flow monitoring,assembly control,and *** counting’s primary goal is to calculate the population in a given region,which requires real-time analysis of congested scenes for prompt reactionary *** crowd is always unexpected,and the benchmarked available datasets have a lot of variation,which limits the trained models’performance on unseen test *** this paper,we proposed an end-to-end deep neural network that takes an input image and generates a density map of a crowd *** proposed model consists of encoder and decoder networks comprising batch-free normalization layers known as evolving normalization(EvoNorm).This allows our network to be generalized for unseen data because EvoNorm is not using statistics from the training *** decoder network uses dilated 2D convolutional layers to provide large receptive fields and fewer parameters,which enables real-time processing and solves the density drift problem due to its large receptive *** benchmark datasets are used in this study to assess the proposed model,resulting in the conclusion that it outperforms conventional models.

关键词： Artificial intelligence deep learning crowd counting scene understanding

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