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The role of machine learning in robotics

INDUSTRIAL ROBOT-THE INTERNATIONAL JOURNAL OF robotics RESEARCH AND APPLICATION 2023年第2期50卷 197-202页

作者： Bogue, Robert Consultant Okehampton UK

Purpose- This paper aims to illustrate the growing role of machine learning techniques in robotics. Design/methodology/approach- Following an introduction which includes a brief historical perspective, this paper provides a short introduction to machine learning techniques. It then provides examples of robotic machine learning applications in agriculture, waste management, warehouse automation and exoskeletons. This is followed by a short consideration of applications in future generations of self-driving vehicles. Finally, brief conclusions are drawn. Findings- machine learning is a branch of artificial intelligence and the topic of extensive academic study. Recent years have seen machine learning techniques being applied successfully to a diversity of robotic systems, most of which involve machine vision. They have imparted these with a range of unique or greatly improved operational capabilities, allowing them to satisfy all manner of new applications. Originality/value- This provides a detailed insight into how machine learning is being applied to robotics.

关键词： Robot machine learning artificial intelligence Neural networks

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An Extensive Study of Convolutional Neural Networks: applications in Computer vision for Improved robotics Perceptions

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SENSORS 2025年第4期25卷 1033-1033页

作者： Raj, Ravi Kos, Andrzej Poznan Univ Tech Inst Robot & Machine Intelligence Ul Piotrowo 3A PL-60965 Poznan Poland AGH Univ Krakow Fac Comp Sci Elect & Telecommun al Adama Mickiewicza 30 PL-30059 Krakow Poland

Convolutional neural networks (CNNs), a type of artificial neural network (ANN) in the deep learning (DL) domain, have gained popularity in several computer vision applications and are attracting research in other fields, including robotic perception. CNNs are developed to autonomously and effectively acquire spatial patterns of characteristics using backpropagation, leveraging an array of elements, including convolutional layers, pooling layers, and fully connected layers. Current reviews predominantly emphasize CNNs' applications in various contexts, neglecting a comprehensive perspective on CNNs and failing to address certain recently presented new ideas, including robotic perception. This review paper presents an overview of the fundamental principles of CNNs and their applications in diverse computer vision tasks for robotic perception while addressing the corresponding challenges and future prospects for the domain of computer vision in improved robotic perception. This paper addresses the history, basic concepts, working principles, applications, and the most important components of CNNs. Understanding the concepts, benefits, and constraints associated with CNNs is crucial for exploiting their possibilities in robotic perception, with the aim of enhancing robotic performance and intelligence.

关键词： artificial intelligence (AI) computer vision convolutional neural network (CNN) deep learning (DL) machine learning (ML) mobile robot (MR) perception

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Review of current vision-based robotic machine-tending applications

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2024年第3-4期131卷 1039-1057页

作者： Jia, Feiyu Ma, Yongsheng Ahmad, Rafiq Univ Alberta Dept Mech Engn Smart & Sustainable Mfg Syst Lab SMART LAB Edmonton AB Canada Southern Univ Sci & Technol Dept Mech & Energy Engn Shenzhen Peoples R China

The manufacturing sector is a fundamental pillar of worldwide economies, contributing markedly to global economic growth. However, the manufacturing industry is persistently confronted with issues impeding its development and expansion, such as manpower shortages, safety concerns, high initial investment for installation, and long return on investment. Within this context, machine tending has become a crucial component of the manufacturing process and potentially serves as a viable solution to the afore-mentioned predicaments. Over the past 5 years, implementing automated machine-tending systems has widely extended from simulation or laboratory environments to practical applications in manufacturing workshops as robotics and artificial intelligence develop rapidly. To fully benefit from the potential of machine-tending applications, it is necessary to comprehend and tackle its associated challenges. Therefore, this paper aims to contribute to the evolution of machine-tending applications by investigating the impacts of emerging trends of advanced technologies, such as autonomous mobile robots, computer vision, machine learning, and deep learning. This systematic literature review is based on the Protocol of Preferred Reporting Items for Systematic Review and Meta-Analyses to analyze the 50 scientific literature related to machine tending in the last five years.

关键词： Manufacturing machine tending robotics Computer vision artificial intelligence Deep learning

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artificial intelligence in Food Manufacturing: A Review of Current Work and Future Opportunities

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FOOD ENGINEERING REVIEWS 2025年第2期17卷 1-31页

作者： Canatan, Mert Alkhulaifi, Nasser Watson, Nicholas Boz, Ziynet Univ Florida Dept Agr & Biol Engn 1741 Museum Rd Gainesville FL 32608 USA Univ Nottingham Sch Comp Sci Computat Optimisat & Learning COL Lab Nottingham NG8 1BB England Univ Leeds Fac Environm Sch Food Sci & Nutr Leeds LS2 9JT England

The incorporation of artificial intelligence (AI) could deliver a new era in food manufacturing, marked by increased operational efficiencies, higher product quality, and better safety standards. This review offers an in-depth examination of the field's evolution, outlines the leading AI methodologies, and investigates their applications in food manufacturing. This review begins with an introduction to AI and its historical context before classifying the main AI methods used in food manufacturing as machine learning, computer vision, robotics, and natural language processing. machine learning has emerged as an AI application in many areas of food manufacturing due to its ability to learn from data and make predictions. Computer vision is a popular form of AI and plays an important role in visual inspections, ensuring product consistency and detecting defects. robotics, in conjunction with AI, has automated a wide range of labour-intensive tasks, from packaging to palletizing, resulting in significant improvements in operational efficiency. Natural language processing has found applications in customer service and compliance, allowing for more efficient interactions and regulatory compliance. AI applications in food manufacturing are numerous and diverse and key areas such as ingredient sorting, quality assessment, process optimization, and supply chain management are highlighted in this review. Finally, we present issues that the industry is encountering in the implementation of AI, as well as a research agenda based on the findings. In-depth analysis provided in this review including the field's evolution, main AI methods used, and their applications in food manufacturing, can provide valuable insights for researchers, practitioners, and decisionmakers in applications of AI in food manufacturing.

关键词： artificial intelligence Food manufacturing Food processing machine learning Deep learning

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artificial intelligence in veterinary and animal science: applications, challenges, and future prospects

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2025年 235卷

作者： Hossein-Zadeh, Navid Ghavi Univ Guilan Fac Agr Sci Dept Anim Sci Rasht 416351314 Iran

artificial intelligence (AI) is increasingly transforming animal and veterinary science through improved decisionmaking, predictive modeling, and automation. This review comprehensively covers applications of key AI technologies, including machine learning, deep learning, computer vision, natural language processing, robotics, and edge AI, in areas such as disease diagnosis, behavioral monitoring, multi-omics data integration, and precision livestock farming. It also highlights current limitations, including data fragmentation, high implementation costs, and ethical concerns. By addressing these challenges through interdisciplinary collaboration, standardized data systems, and the development of explainable and scalable AI tools, the field can advance toward more sustainable, efficient, and welfare-oriented practices. This review underscores the transformative potential of AI in achieving the goals of One Health and global food security while emphasizing the need for continued research, policy support, and equitable access to AI technologies.

关键词： artificial intelligence Deep learning machine learning Precision livestock farming Veterinary diagnostics

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Bin picking: a review of recent developments

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INDUSTRIAL ROBOT-THE INTERNATIONAL JOURNAL OF robotics RESEARCH AND APPLICATION 2023年第6期50卷 873-877页

作者： Bogue, Robert Consultant Okehampton UK

Purpose - The purpose of this paper is to provide an insight into the present-day state of bin picking by considering research, technology, products and applications. Design/methodology/approach - Following a short introduction, this first provides examples of recent bin picking research. It then discusses a selection of commercial product developments and applications. Finally, brief conclusions are drawn. Findings - Bin picking has the potential to eliminate repetitive, manual part handling practices in many sectors of the manufacturing and logistics industries. Systems combine robotic gripping and manipulation with machine vision and specialist software and tend to be complex to install and commission. They are produced by robot manufacturers, system integrators, software developers and machine vision specialists and all are constantly developing and improving the technology. These developments are supported by a strong academic research effort, much involving artificial intelligence methods, and while the technology is evolving rapidly, it is yet to reach the point where deployments are routine and ***/value - This provides a timely review of recent bin picking research and commercial developments.

关键词： Robot Bin picking artificial intelligence machine vision

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Automating quality control: real-time defect detection and automated decision-making with ai and doosan robotics

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INTERNATIONAL JOURNAL OF INTELLIGENT robotics AND applications 2025年 1-15页

作者： Chaabani, Ameni Cherif, Raef Yaddaden, Yacine Univ Quebec Rimouski Dept Math Data Sci & Engn Rimouski PQ G5L 3A1 Canada

In today's manufacturing industry, there is a growing need for precise and efficient quality control systems. This article proposes a real-time solution for identifying missing or damaged parts in a can by integrating computer vision, deep learning networks, robotics, and artificial intelligence. A collaborative robotic system was used to improve manufacturing efficiency. The system consists of a Doosan robot equipped with a 2.5D vision system and suction end-effectors for accurate object manipulation. The study involved collecting and augmenting a dataset using the Hough Transformation technique to isolate individual cans. Three pre-trained models, namely VGG16, MobileNet, and ResNet101, were used to detect defects on the can top through transfer learning. The performance of the models was evaluated in terms of accuracy, speed, and efficiency. The algorithm was successfully integrated into the robot, enabling it to perform real-time defect detection and classification autonomously. The implementation of this system showcased significant improvements in defect detection, highlighting the potential of automated technologies in industrial applications. The study also explores the implications of these findings for future enhancements, including the potential for increased system precision and the broader application of computer vision and artificial intelligence in manufacturing processes.

关键词： Computer vision Doosan robotics Real-time object detection artificial intelligence Automated decision-making Manufacturing efficiency Image processing

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Editorial for the Special Issue on Methodology, Microfabrication and applications of Advanced Sensing and Smart Systems

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MICROmachineS 2024年第9期15卷 1149-1149页

作者： Jia, Luyu Ji, Shanling Gao, Yuze Wen, Haiying Zhu, Jianxiong Chongqing Univ Technol Sustainable Dev Res Grp Chongqing 400054 Peoples R China Huizhou Univ Guangdong Prov Key Lab Elect Funct Mat & Devices Huizhou 516001 Peoples R China Southeast Univ Sch Mech Engn Nanjing 211189 Peoples R China

Smart sensing and advanced systems have played crucial roles in the modern industrialization of society, which has led to many sensors being used in fabrication methodologies for various applications, such as in medical equipment [1], robotics activities [2], sustainable electronics systems [3], and smart devices with artificial intelligence and the Internet of Things (IoT) [4]. Whether combining machine learning technology with wearable sensors to monitor human activity [5,6] or applying sensors to a Triboelectric Nanogenerator (TENG) [7], these examples vividly demonstrate that sensing technology is increasingly deeply integrated with other cutting-edge technologies. Due to the requirements of these technologies, the methodologies used in the microfabrication of advanced sensing and smart systems have become increasingly sustainable and advanced [8], and the structural design is more ingenious, enhancing the sensor’s stability and responsiveness [9]. This Special Issue seeks to showcase the latest advancements in and contributions to the micro/nanofabrication, methodology, integration, and application of sensors and advanced systems with the aid of technologies in the fields of artificial intelligence, multisensor fusion, machine vision, human–machine interaction, machine learning, big data, advanced robotics, and others.

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machine vision system for automatic defect detection of ultrasound probes

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2024年第7-8期135卷 3421-3435页

作者： Profili, Andrea Magherini, Roberto Servi, Michaela Spezia, Fabrizio Gemmiti, Daniele Volpe, Yary Univ Florence Dept Ind Engn Via Santa Marta 3 I-50139 Florence Italy Esaote SpA Via Caciolle 15 I-50127 Florence Italy

Industry 4.0 conceptualizes the automation of processes through the introduction of technologies such as artificial intelligence and advanced robotics, resulting in a significant production improvement. Detecting defects in the production process, predicting mechanical malfunctions in the assembly line, and identifying defects of the final product are just a few examples of applications of these technologies. In this context, this work focuses on the detection of ultrasound probes' surface defects, with a focus on Esaote S.p.A.'s production line probes. To date, this control is performed manually and therefore biased by many factors such as surface morphology, color, size of the defect, and by lighting conditions (which can cause reflections preventing detection). To overcome these shortfalls, this work proposes a fully automatic machine vision system for surface acquisition of ultrasound probes coupled with an automated defect detection system that leverage artificial intelligence. The paper addresses two crucial steps: (i) the development of the acquisition system (i.e., selection of the acquisition device, analysis of the illumination system, and design of the camera handling system);(ii) the analysis of neural network models for defect detection and classification by comparing three possible solutions (i.e., MMSD-Net, ResNet, EfficientNet). The results suggest that the developed system has the potential to be used as a defect detection tool in the production line (full image acquisition cycle takes similar to 200 s), with the best detection accuracy obtained with the EfficientNet model being 98.63% and a classification accuracy of 81.90%.

关键词： Product characterization Inspection system Image processing artificial intelligence Virtual prototyping

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artificial intelligence Technology in the Agricultural Sector: A Systematic Literature Review

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IEEE ACCESS 2023年 11卷 171-202页

作者： Elbasi, Ersin Mostafa, Nour AlArnaout, Zakwan Zreikat, Aymen I. Cina, Elda Varghese, Greeshma Shdefat, Ahmed Topcu, Ahmet E. Abdelbaki, Wiem Mathew, Shinu Zaki, Chamseddine Amer Univ Middle East Coll Engn & Technol Kuwait 54200 Kuwait

Due to the increasing global population and the growing demand for food worldwide as well as changes in weather conditions and the availability of water, artificial intelligence (AI) such as expert systems, natural language processing, speech recognition, and machine vision have changed not only the quantity but also the quality of work in the agricultural sector. Researchers and scientists are now moving toward the utilization of new IoT technologies in smart farming to help farmers use AI technology in the development of improved seeds, crop protection, and fertilizers. This will improve farmers' profitability and the overall economy of the country. AI is emerging in three major categories in agriculture, namely soil and crop monitoring, predictive analytics, and agricultural robotics. In this regard, farmers are increasingly adopting the use of sensors and soil sampling to gather data to be used by farm management systems for further investigations and analyses. This article contributes to the field by surveying AI applications in the agricultural sector. It starts with background information on AI, including a discussion of all AI methods utilized in the agricultural industry, such as machine learning, the IoT, expert systems, image processing, and computer vision. A comprehensive literature review is then provided, addressing how researchers have utilized AI applications effectively in data collection using sensors, smart robots, and monitoring systems for crops and irrigation leakage. It is also shown that while utilizing AI applications, quality, productivity, and sustainability are maintained. Finally, we explore the benefits and challenges of AI applications together with a comparison and discussion of several AI methodologies applied in smart farming, such as machine learning, expert systems, and image processing.

关键词： artificial intelligence applications agriculture smart farming Internet of Things sensors machine learning deep learning

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