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Literature Review on Advanced Cloud robotics for Enhanced Industrial automation 3

Literature Review on Advanced Cloud Robotics for Enhanced In...

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3rd international Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Manonmani, A. Akash, S. Aswinraj, A. Manikandan, A. Saveetha Engineering College Department of Electronics and Instrumentation Engineering Chennai India

ISBN: (纸本)9798331509675

Cloud robotics is transforming industrial automation by combining cloud computing with robotic systems. This paper reviews recent advancements in cloud robotics and their industrial applications, highlighting the synergy between cloud infrastructure and robotic platforms. Key technologies like artificial intelligence, machine learning, and the Internet of Things are explored for their roles in enhancing scalability, computational efficiency, and real-time data processing. The paper also addresses challenges such as latency and data security, proposing solutions to overcome them. The findings suggest that cloud robotics can significantly improve productivity, cost-efficiency, and flexibility in manufacturing and other sectors, with future research directions aimed at promoting its broader adoption. © 2025 ieee.

关键词： Cloud platforms

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Artificial intelligence in robotics: Revolutionizing Retail Store automation and Beyond 22

Artificial Intelligence in Robotics: Revolutionizing Retail ...

引用

22nd international Learning and Technology Conference, L and T 2025

作者： Ali, Khalufi Nasser Jazan University College of Business Department of Management and Marketing Jazan Saudi Arabia

ISBN: (纸本)9798331532215

AI in robotics involves the application of efficient algorithm and computational techniques that allows robots to operate independently, learn from the environment and make efficient decisions. However, in current studies, mostly contact methods that cannot be learned and do not respond to change are used despite the progress made in this regard. These approaches often fail to respond in real-time to а certain issue, which can result in inefficiency and low overall operational performance. To overcome these shortcomings, this paper presents a new technique in which DRL (Deep Reinforcement Learning) is used for motion control of a robotic arm and limb in retail automation. DRL allows the robotic system to learn the best motion profiles from playing a game with its surroundings with reduced chances of a less optimum setting due to changing environmental conditions. The most important peculiarities of the proposed approach include integration of enhanced neural structures and realization of automatic learning and decision-making. The effectiveness of the DRL approach is shown to be considerably higher than that of traditional ones, with accuracy reaching 99.3%. Such outcomes speak to the prospects to transform robotics through DRL knowledge and skills to enhance real-time adaptation and increase effectiveness. Lastly, this research enhances the existing literature in AI & robotics and accordingly establishes the foundation for further developments of intelligent automation systems in various fields. © 2025 ieee.

关键词： Deep reinforcement learning

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Fabrication of Sensorsless Automated Guided Vehicle for Small Warehouse Rack Management

Fabrication of Sensorsless Automated Guided Vehicle for Smal...

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2025 ieee international Conference on computational, Communication and Information Technology, ICCCIT 2025

作者： Sahu, Neelesh Kumar Chaturvedi, Piyush Gupta, Vaishali Patel, Ruchi Verma, Ashok Shrivastava, Megha Gyan Ganga Institute of Technology and Sciences Artificial Intelligence and Robotics MP Jabalpur India IPS Academy Institute of Engineering & Science Department of Computer Sc. & Engg. MP Indore India Gyan Ganga Institute of Technology and Sciences MP Jabalpur India Medicaps University Department of Mathematics MP Indore India

ISBN: (纸本)9798331512965

Automated guided vehicles (AGVs) are an essential part of today's logistics networks because they save time, reduce wear and maintenance expenses, and maximize efficiency in route *** creation of AGVthat is capable of effectively managing warehouse racks without the utilization of sensors is the primary emphasis of this project. In order to travel and carry out duties, the AGVruns on predetermined pathways and makes use of a button-based interface. For the purpose of offering an efficient and costeffective solution for the automation of small warehouses, it is outfitted with a lever mechanism that is operated by a servo motor and features the ability to connect and move racks. © 2025 ieee.

关键词： Automatic guided vehicles

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Dynamic bimanual human-to-robot object handovers using motion prediction deep neural networks

Dynamic bimanual human-to-robot object handovers using motio...

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2025 ieee/SICE international symposium on System Integration, SII 2025

作者： Mavsar, Matija Uchibe, Eiji Morimoto, Jun Ude, Ales Jožef Stefan Institute Dept. of Automatics Biocybernetics and Robotics Ljubljana Slovenia ATR Computational Neuroscience Laboratories Dept. of Brain-Robot Interface Kyoto Japan Kyoto University Graduate School of Informatics Kyoto Japan

ISBN: (纸本)9798331531614

Facilitating dynamic bimanual handovers between humans and robots is a complex endeavor that requires integrating human pose estimation, motion prediction, and generating appropriate trajectories for receiving robots. This study introduces a method designed to predict required bimanual receiver robot motion during handover tasks, leveraging pose estimation and motion prediction networks. Additionally, we propose a real-time control approach for a dual-arm humanoid robot to dynamically adjust its receiving trajectories. We evaluate the ability of neural networks to accurately predict receiver trajectories and thus improve the handover process. We compare long short-term memory (LSTM) and transformer architectures for motion prediction and also assess prediction accuracy of both absolute and relative receiving trajectories as well trajectories for each separate robot arm, and show that the use of absolute and relative coordinates is beneficial for generating more accurate receiver motions. © 2025 ieee.

关键词： Trajectories

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A Literature Review of Machine Learning Techniques for Dance Recognition and Robotic Vision

A Literature Review of Machine Learning Techniques for Dance...

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robotics and Technologies for Industrial automation (ROBOTHIA), ieee international Conference on

作者： Lee Wei San Samuel-Soma M. Ajibade Muhammed Basheer Jasser Adefemi Ayodele Babatunde Adedotun Ajayi Mbiatke Anthony Bassey Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Darul Ehsan Malaysia Research Centre for Nanomaterials and Energy Technology (RCNMET) Faculty of Engineering and Technology Sunway University Selangor Malaysia Research Centre for Human-Machine Collaboration (HUMAC) Faculty of Engineering and Technology Sunway University Petaling Jaya Selangor Malaysia University of East London London UK Department of Cyber security Faculty of Computing & Informatics Ladoke Akintola University of Technology Ogbomoso Nigeria Department of Business Administration Universiti Tun Hussein Onn Malaysia Batu Pahat Malaysia

ISBN: (数字)9798350356755

ISBN: (纸本)9798350356762

Image recognition, powered by machine learning (ML), has significantly advanced applications in both dance movement recognition and robotic vision. This review examines key ML techniques, including Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), Self-Organizing Maps (SOMs), and Long Short-Term Memory (LSTM) networks, alongside pose estimation methods like OpenPose and Part Affinity Fields (PAFs). These techniques enhance dance classification, real-time feedback, and motion analysis, with OpenPose + LSTMs and PAFs + LSTMs demonstrating the highest accuracy. Notwithstanding progress, obstacles such as high computational costs, data dependency, and real-time implementation challenges persist. Beyond dance, these methods are critical in robotic vision, intelligent automation, and industrial image processing, enabling autonomous robotic navigation, defect detection in manufacturing, and AI-driven motion tracking. By leveraging human movement analysis for robotics, ML improves human-robot interaction, robotic-assisted rehabilitation, and industrial automation. Despite progress, challenges such as high computational demands, data dependency, and real-time constraints remain. This review explores future directions, including multimodal data fusion, hybrid AI models, and real-time optimization, bridging the gap between AI-driven motion systems and intelligent automation to enhance adaptability and efficiency across domains.

关键词： Intelligent automation Humanities Solid modeling Image recognition Accuracy Service robots Robot sensing systems Real-time systems Robots Long short term memory

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MamUNet: Mamba Enhances Vessel Segmentation in NIR-II Fluorescence Imaging

MamUNet: Mamba Enhances Vessel Segmentation in NIR-II Fluore...

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ieee international symposium on Biomedical Imaging

作者： Keyi Han Anqi Xiao Jie Tian Zhenhua Hu CAS Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

The Transformer architecture achieves excellent global context understanding of sequential data through the self-attention mechanism, but it faces computational challenges when modeling long-range dependencies. Mamba, based on the state space model (SSM), has emerged as a notable approach for effectively addressing these long-range dependencies, significantly improving computational and storage efficiency. Therefore, we propose MamUNet, a new method that combines the powerful modeling capabilities of the Mamba model with the strengths of the UNet architecture for complex vessel segmentation tasks in NIR-II fluorescence images. The encoder employs the classic network ResNet to learn local low-level features, followed by Mamba for global feature learning to capture more contextual information. We conducted experiments on a dataset of human brain NIR-II fluorescence vessel images and three publicly available retinal vessel datasets. The results demonstrate that our method achieves more accurate vessel segmentation with lower computational cost, outperforming other comparison methods.

关键词： Representation learning Image segmentation Accuracy computational modeling Computer architecture Fluorescence Transformers Retinal vessels computational efficiency Faces

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international symposium on computational intelligence in robotics and automation Proceedings

International Symposium on Computational Intelligence in Rob...

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1999年

作者： ieee Computer Society ieee, Institute Of Electrical

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Model of Research for Engineering Related Investigations

Model of Research for Engineering Related Investigations

引用

international symposium on Applied Machine intelligence and Informatics (SAMI)

作者： László Horváth Institute of Applied Mathematics and Doctoral School of Applied Informatics and Applied Mathematics Óbuda University Budapest Hungary

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

Integration of all aspects, activities as well as machine and human qualities is one of the main objectives of development in computer and computing related achievements for more than four decades. One of the main subjects of this integration is engineering modeling and by now engineering platforms are capable of model-based integration regarding all objects within and outside of an achievement such as industrial and commercial product or service. This paper is about a novel concept, theory and methodology to realize integration of all activities during given research in an experimental model serving engineering related investigation for high automation in a broad sense. Model mediated research is realized using models with reactive and autonomous characteristics. These models are purposefully complex and include all objects, activities, processes and contextual connections for actual research. Serving full innovation cycle is aimed from the first idea to support achievement which utilizes the related research results. To manage these models, comprehensive engineering modeling platform (EMP) is configured then applied at developing integrated experimental model together with its inside and outside contextual connections. Results in this paper are implemented in the Virtual Research Laboratory (VRL, Obuda University) serving primarily PhD research in applied informatics and the related applied mathematics.

关键词： Technological innovation automation computational modeling Mathematical models Informatics Machine intelligence Context modeling

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Explaining Internal Representations in Deep Networks: Adversarial Vulnerability of Image Classifiers and Learning Sequential Tasks with Sparse Reward

Explaining Internal Representations in Deep Networks: Advers...

引用

international symposium on Applied Machine intelligence and Informatics (SAMI)

作者： Igor Farkaš Department of Applied Informatics Physics and Informatics Centre for Cognitive Science Faculty of Mathematics Comenius University Bratislava Slovak Republic

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

Modern artificial intelligence based on deep neural networks has demonstrated in the past decade great achievements in concrete tasks, sometimes even surpassing human performance. On the other hand, there exist fundamental problems in these models, be it image classification or natural language tasks. Since deep networks are inherently black boxes, it is important to design and apply techniques that help shed light on the functioning of the trained models. In the talk, we will discuss two domains. First, in the context of image classification we will illustrate the effect of adversarial examples that can easily fool trained models, hence revealing their lack of robustness. In the second part, we will deal with sequential tasks, such as computer games, with an extremely sparse reward. Introducing the concept of intrinsic motivation, we will describe the neural networks based model that can successfully use reinforcement learning and self-supervised knowledge distillation to solve these tasks thanks to optimized organization of its internal representations. Finally, we briefly mention our current work related to cognitive robotics.

关键词： Knowledge engineering Video games computational modeling Natural languages Reinforcement learning Organizations Robustness Informatics Machine intelligence Image classification

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A Restoration Method Based on Color Compensation and Depth Prior Using a Revised Underwater Imaging Model 9th

A Restoration Method Based on Color Compensation and Depth P...

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9th international symposium on Artificial intelligence and robotics, ISAIR 2024

作者： Zhao, Xixuan Zuo, Jingchuan Wu, Hao Qi, Tongrong Liu, Ning School of Technology Beijing Forestry University Beijing100083 China Key Laboratory of State Forestry Administration on Forestry Equipment and Automation Beijing100083 China School of Information Science and Technology Beijing100083 China

ISBN: (纸本)9789819629138

Underwater images often suffer from low contrast, blurring, and color distortion due to light absorption and scattering, limiting their usefulness in fields like robotics and computer vision. To address these issues, we propose a restoration method based on a revised underwater imaging degradation model, using different attenuation coefficients for direct and backscattering. We adopt linear fitting for small depth distances to improve computational efficiency and propose a statistical method for depth prior to enhance depth estimation accuracy. Our color correction preprocessing removes blue-green bias, solving color shift problems. The proposed method shows a 15.8% improvement in performance metrics compared to the conventional methods and operates 5–10 times faster. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Underwater imaging

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