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arXiv 2025年

作者： Cao, Muqing Nguyen, Thien-Minh Yuan, Shenghai Anastasiou, Andreas Zacharia, Angelos Papaioannou, Savvas Kolios, Panayiotis Panayiotou, Christos G. Polycarpou, Marios M. Xu, Xinhang Zhang, Mingjie Gao, Fei Zhou, Boyu Chen, Ben M. Xie, Lihua Robotics Institute Carnegie Mellon University United States School of Electrical and Electronic Engineering Nanyang Technological University Singapore KIOS Research Innovation Center of Excellence Department of Electrical and Computer Engineering University of Cyprus Cyprus School of Artificial Intelligence Sun Yat-sen University China Zhejiang University China Department of Mechanical and Automation Engineering Chinese University of Hong Kong Hong Kong

We propose the Cooperative Aerial Robot Inspection Challenge (CARIC), a simulation-based benchmark for motion planning algorithms in heterogeneous multi-UAV systems. CARIC features UAV teams with complementary sensors, realistic constraints, and evaluation metrics prioritizing inspection quality and efficiency. It offers a ready-to-use perception-control software stack and diverse scenarios to support the development and evaluation of task allocation and motion planning algorithms. Competitions using CARIC were held at IEEE CDC 2023 and the IROS 2024 Workshop on Multi-Robot Perception and Navigation, attracting innovative solutions from research teams worldwide. This paper examines the top three teams from CDC 2023, analyzing their exploration, inspection, and task allocation strategies while drawing insights into their performance across scenarios. The results highlight the task’s complexity and suggest promising directions for future research in cooperative multi-UAV systems. © 2025, CC BY-NC-ND.

关键词： Motion planning

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Discrete-Time Distributed Optimization for Linear Uncertain Multi-Agent Systems

Discrete-Time Distributed Optimization for Linear Uncertain ...

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IEEE Conference on Decision and Control

作者： Tong Liu Michelangelo Bin Ivano Notarnicola Thomas Parisini Zhong-Ping Jiang Department of Electrical and Computer Engineering Control and Networks Lab Tandon School of Engineering New York University Brooklyn NY USA Department of Electrical Electronic and Information Engineering University of Bologna Bologna Italy Department of Electrical and Electronic Engineering Imperial College London London UK Department of Engineering and Architecture University of Trieste Trieste Italy KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus

The distributed optimization algorithm proposed by J. Wang and N. Elia in 2010 has been shown to achieve linear convergence for multi-agent systems with single-integrator dynamics. This paper extends their result, including the linear convergence rate, to a more complex scenario where the agents have heterogeneous multi-input multi-output linear dynamics and are subject to external disturbances and parametric uncertainties. Disturbances are dealt with via an internal-model-based control design, and the interaction among the tracking error dynamics, average dynamics, and dispersion dynamics is analyzed through a composite Lyapunov function and the cyclic small-gain theorem. The key is to ensure a small enough stepsize for the convergence of the proposed algorithm, which is similar to the condition for time-scale separation in singular perturbation theory.

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Dynamic Clustering-based Task Orchestrator in Mobile Edge Computing

Dynamic Clustering-based Task Orchestrator in Mobile Edge Co...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Mona Alghamdi Atm Alam Arumugam Nallanathan Asma Cherif School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Information Technology Faculty of Computing and Information Technology Center of Excellence in Smart Environment Research King AbdulAziz University Saudi Arabia

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

Multi-access Edge Computing (MEC) is an emerging paradigm designed to provide storage, computing and communication capabilities in the proximity of end-user devices. This approach facilitates the deployment of real-time on mobile devices with limited capabilities. To realize the MEC goals, it is essential to effectively manage and offload computing tasks to both edge and cloud-based resources. However, the dynamic nature, uncertainty and mobility within edge computing environments pose significant challenges to resource management. Furthermore, the inherent software and hardware heterogeneity, coupled with the distributed nature of architecture, complicates the development of efficient task offloading strategies that can adeptly manage resources across both edge and cloud platforms. In this paper, we propose a cluster-based task edge orchestrator, where edge servers are grouped based on service demands, resource ability and other factors to improve the overall service. Our proposed method leverages the K-Medoids clustering algorithm to dynamically form clusters of suitable edge servers for offloading computing tasks with minimum response time. To validate our proposed solution, we have orchestrated a comprehensive series of tests using EdgeCloudSim. Results show that our approach outperforms its competitor in terms of average service time by around 8 %.

关键词： Wireless communication Wide area networks Multi-access edge computing Uncertainty Sensitivity software Servers

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Development of a Game Play in Chulalongkorn University Central Library Metaverse

Development of a Game Play in Chulalongkorn University Centr...

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International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON

作者： Aisha Munir Suebphong Noisri Lunchakorn Wuttisittikulkij Rabiab Sangchantr Jintavee Khlaisang Pisit Vanichchanunt Department of Electrical Engineering Wireless Communication Ecosystem Research Unit Chulalongkorn University Bangkok Thailand Office of Academic Resources Chulalongkorn University Bangkok Thailand Department of Educational Technology and Communications Faculty of Education Center of Excellence in Educational Invention and Innovation Chulalongkorn University Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Monkut's University of Technology North Bangkok Thailand

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

Metaverse is the next generation of internet that allows people to interact with digital content in a more immersive and interactive way. This paradigm shift extends to the realm of education, where the concept of a smart campus is reshaping traditional educational systems. This research focuses on the convergence of the metaverse and digital twin (DT) in education, with a specific emphasis on smart libraries. Recognizing libraries as integral components of educational systems, this study proposes a metaverse platform for Chulalongkorn university central library. The proposed platform enables users to explore the virtual library remotely along with the simulation of resource classification physical service in 3D environment. This immersive virtual simulation enhances the learning experience of patrons through enjoyable gameplay. Additionally, this innovative time saving approach enables new students and staff to learn book categorization procedures efficiently.

关键词： Three-dimensional displays Metaverse Education Games Libraries Telecommunications Digital twins

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A Deep Learning Framework for Object Detection and Tracking: CNN-Based Parcel Tracking on Conveyor Belts

A Deep Learning Framework for Object Detection and Tracking:...

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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Md Jobair Hossain Faruk Md Kamrul Siam Alex Theilmann Kristiyan Georgiev Jerry Q. Cheng Hossain Shahriar Department of Computer Science New York Institute of Technology USA Nova School of Business and Economics Portugal R&D Computer Vision and Software Engineering Manager Materials Handling Systems Inc. USA Center for Cybersecurity University of West Florida USA

ISBN: (数字)9798331531973

ISBN: (纸本)9798331531980

Object Detection and Tracking (ODT) represents a critical area within computer vision, with broad applications across various industries, particularly in the automation of logistics operations. However, significant challenges persist, including the logistics-specific datasets and the inefficiency of data annotation, which is crucial for training robust artificial intelligence models for parcel detection and tracking. These challenges are exacerbated when human experts are required to manually assign unique identifiers to objects across frames, hindering the scalability of AI model training. In this research, we present a novel deep learning-based ODT framework, leveraging Convolutional Neural Networks (CNN) integrated with evaluation metrics such as Euclidean Distance and Intersection over Union (IoU) to facilitate robust object detection and tracking. To address the limitations of manual annotation, we develop an automated framework for assigning unique identifiers to bounding boxes across sequential frames. The framework is based on three logistics-related datasets, comprising time-series images of packages on moving conveyor belts. The first two datasets, containing 100 and 1,021 images, are manually annotated, while the third dataset of 5,046 images is automatically annotated using the proposed tool. We also utilize the Hungarian algorithm to optimize identity assignment across frames. The results demonstrate that the framework achieves an accuracy of 99.09% in object detection and tracking with a precision of 99.25% and a recall of 99.10% for the manually annotated dataset. The automated annotation illustrates an accuracy of 98.70%. These findings indicate the robustness and scalability of the proposed framework in automating parcel tracking for logistics operations.

关键词： Training computer vision Accuracy Annotations Scalability Object detection Robustness Convolutional neural networks Object tracking Logistics

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AgriLLM:Harnessing Transformers for Framer Queries

arXiv

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arXiv 2024年

作者： Didwania, Krish Seth, Pratinav Kasliwal, Aditya Agarwal, Amit Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering India Department of Data Science & Computer Applications India Enterprise Analytics & Data Science Artificial Intelligence - Center of Excellence Wells Fargo International Solutions Private Limited Bangalore India

Agriculture, vital for global sustenance, necessitates innovative solutions due to a lack of organized domain experts, particularly in developing countries where many farmers are impoverished and cannot afford expert consulting. Initiatives like Farmers Helpline play a crucial role in such countries, yet challenges such as high operational costs persist. Automating query resolution can alleviate the burden on traditional call centers, providing farmers with immediate and contextually relevant information. The integration of Agriculture and Artificial Intelligence (AI) offers a transformative opportunity to empower farmers and bridge information gaps. Language models like transformers, the rising stars of AI, possess remarkable language understanding capabilities, making them ideal for addressing information gaps in agriculture. This work 1 explores and demonstrates the transformative potential of Large Language Models (LLMs) in automating query resolution for agricultural farmers, leveraging their expertise in deciphering natural language and understanding context. Using a subset of a vast dataset of real-world farmer queries collected in India, our study focuses on approximately 4 million queries from the state of Tamil Nadu, spanning various sectors, seasonal crops, and query types. Copyright © 2024, The Authors. All rights reserved.

关键词： Agriculture

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Design and Implementation of a Following Vehicle Based on ROS and UWB

Design and Implementation of a Following Vehicle Based on RO...

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Automation, Control and Robots (ICACR), International Conference on

作者： Yun-Shuai Yu Chin-Wei Chen Department of Computer Science and Information Engineering National Formosa University Yunlin Taiwan Software Research & Development Center Hon Hai Technology Group (Foxconn) New Taipei City Taiwan

Autonomous vehicle technology has gained significant attention in various industries recently. This paper presents the design and implementation of a following vehicle that utilizes the Robot Operating System (ROS) and Ultra-Wideband (UWB) RF transceiver modules to achieve precise target localization, map the surrounding environment, and navigate accordingly. A mobile application has also been developed to track the following vehicle’s position.

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Dual-View Pyramid Pooling in Deep Neural Networks for Improved Medical Image Classification and Confidence Calibration

arXiv

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arXiv 2024年

作者： Zhang, Xiaoqing Nie, Qiushi Xiao, Zunjie Zhao, Jilu Wu, Xiao Guo, Pengxin Li, Runzhi Liu, Jin Wei, Yanjie Pan, Yi Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Statistics and Actuarial Science The University of Hong Kong 999077 Hong Kong Cooperative Innovation Center of Internet Healthcare Zhengzhou University Zhengzhou450001 China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi830046 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China

Spatial pooling (SP) and cross-channel pooling (CCP) operators have been applied to aggregate spatial features and pixel-wise features from feature maps in deep neural networks (DNNs), respectively. Their main goal is to reduce computation and memory overhead without visibly weakening the performance of DNNs. However, SP often faces the problem of losing the subtle feature representations, while CCP has a high possibility of ignoring salient feature representations, which may lead to both miscalibration of confidence issues and suboptimal medical classification results. To address these problems, we propose a novel dual-view framework, the first to systematically investigate the relative roles of SP and CCP by analyzing the difference between spatial features and pixel-wise features. Based on this framework, we propose a new pooling method, termed dual-view pyramid pooling (DVPP), to aggregate multi-scale dual-view features. DVPP aims to boost both medical image classification and confidence calibration performance by fully leveraging the merits of SP and CCP operators from a dual-axis perspective. Additionally, we discuss how to fulfill DVPP with five parameter-free implementations. Extensive experiments on six 2D/3D medical image classification tasks show that our DVPP surpasses state-of-the-art pooling methods in terms of medical image classification results and confidence calibration across different DNNs. © 2024, CC BY.

关键词： Deep neural networks

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Design and Deployment of a Simulation Platform: Case Study of an Agent-Based Model for Youth Suicide Prevention 22

Design and Deployment of a Simulation Platform: Case Study o...

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Proceedings of the Winter Simulation Conference

作者： Joshua Huddleston Michael C. Galgoczy Kareem A. Ghumrawi Philippe J. Giabbanelli Ketra L. Rice Nisha Nataraj Margaret M. Brown Christopher R. Harper Curtis S. Florence Department of Computer Science & Software Engineering Miami University Oxford OH USA National Center for Injury Prevention and Control Centers for Disease Control and Prevention (CDC) Atlanta GA USA

Research has examined the process of engaging end-users in co-designing a simulation model and using it within workplace settings. In contrast, few studies document the software development aspect of creating, packaging, and deploying a simulation platform that satisfies end-users' needs. In this case study, we detail these aspects regarding a platform involving Agent-Based Modeling for youth suicide prevention. Goals and constraints are detailed in three categories of needs, each showing how competing demands are addressed via software solutions: 1) data, encompassing data security (e.g., no direct access to individual-level data) and data updates (e.g., changing a model's initialization without coding expertise); 2) deployment, to satisfy security protocols (e.g., secure intranet communications) and the end-users' experience (e.g., ease of installation); 3) accessibility, ensuring that individuals with impairments are not excluded from using simulations. By presenting practical solutions to each category, our case study supports modelers in addressing their own deployment needs.

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Benchmarking Multi-Object Grasping

arXiv

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arXiv 2025年

作者： Chen, Tianze Frumento, Ricardo Pagnanelli, Giulia Cei, Gianmarco Keth, Villa Gafarov, Shahaddin Gong, Jian Ye, Zihe Baracca, Marco D’Avella, Salvatore Bianchi, Matteo Sun, Yu of Computer Science and Engineering Department University of South Florida TampaFL33620 United States Research Center"E. Piaggio" Department of Information Engineering University of Pisa Pisa Italy Rutgers University New BrunswickNJ08901 United States Department of Excellence in Robotics & AI Mechanical Intelligence Institute Scuola Superiore Sant’Anna Pisa Italy

In this work, we describe a multi-object grasping benchmark to evaluate the grasping and manipulation capabilities of robotic systems in both pile and surface scenarios. The benchmark introduces three robot multi-object grasping benchmarking protocols designed to challenge different aspects of robotic manipulation. These protocols are: 1) the Only-Pick-Once protocol, which assesses the robot’s ability to efficiently pick multiple objects in a single attempt;2) the Accurate pick-trnsferring protocol, which evaluates the robot’s capacity to selectively grasp and transport a specific number of objects from a cluttered environment;and 3) the Pick-transferring-all protocol, which challenges the robot to clear an entire scene by sequentially grasping and transferring all available objects. These protocols are intended to be adopted by the broader robotics research community, providing a standardized method to assess and compare robotic systems’ performance in multi-object grasping tasks. We establish baselines for these protocols using standard planning and perception algorithms on a Barrett hand, Robotiq parallel jar gripper, and the Pisa/IIT Softhand-2, which is a soft underactuated robotic hand. We discuss the results in relation to human performance in similar tasks we well. © 2025, CC BY-NC-ND.

关键词： Robot programming

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