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检索条件"任意字段=Conference on Intelligent Robots and Computer Vision XXXII - Algorithms and Techniques"

共 372 条记录，以下是1-10 订阅

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A computer vision Algorithm for Path Planning in Mobile robots 22

A Computer Vision Algorithm for Path Planning in Mobile Robo...

引用

22nd IEEE conference on Pervasive and intelligent Computing

作者： Ruggieri, Claudio Russo, Miriana Santoro, Corrado Santoro, Federico Fausto STMicroelect Syst Res & Applicat I-95121 Catania Italy Univ Catania Dept Math & Informat Catania Italy

ISBN: (纸本)9798331522759;9798331522742

In robotics, route planning is essential to ensure the safe and efficient movement of robots within the workplace. This process involves determining a trajectory, usually a series of points in the workspace, to achieve a specific goal. It is essential to consider criteria such as reducing the length of the route, the number of manoeuvres and the avoidance of obstacles. Route planning techniques generally require modelling the environment, representing both the structure and the obstacles (fixed or mobile), and the implementation of algorithms that generate the trajectory through the free areas of the environment. This approach often includes constructing a graph of possible trajectories and using minimum path search algorithms, such as A*. This article presents a route planning algorithm that uses Voronoi diagrams and uses artificial vision algorithms. In addition, a case study is described in which the proposed technique is applied to guide an automated system through a maze drawn on a whiteboard by a user.

关键词： path planning robotic systems virtual environment voronoi diagrams opencv

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A Novel computer vision Algorithm for Enhancing Image Recognition and Classification 6

A Novel Computer Vision Algorithm for Enhancing Image Recogn...

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6th IEEE International conference on Power, intelligent Computing and Systems, ICPICS 2024

作者： Zhong, Herun School of Arts and Design Hubei University of Technology Wuhan430000 China

ISBN: (纸本)9798350374315

computer vision focuses on optimizing computers to understand and interpret visual data from photos or movies, while image recognition specializes in detecting and categorizing objects or patterns in photographs. Technological advancements have revolutionized the field of computer science and found applications in various fields such as robotics, healthcare, security, and entertainment. This article offers a comprehensive overview of the novel approaches of computer vision algorithms widely used in enhancing image recognition and classification. The discipline deals with extracting, analyzing, and understanding information from images or videos by developing algorithms that enable robots to see and comprehend visual information similar to humans. Image recognition primarily emphasizes automatic identification and categorization of objects or patterns in pictures. According to findings, significant developments have been made due to large datasets availability along with improvements in deep learning techniques leveraging enhanced processor power. Convolutional neural networks are now the dominant approach for image recognition problems within deep learning field attaining consistently high-performance levels across several benchmarks. © 2024 IEEE.

关键词： Convolutional neural networks

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Reducing Computation Requirements for Autonomous Mobile robots on Low Powered Embedded Systems

Reducing Computation Requirements for Autonomous Mobile Robo...

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2023 IEEE International conference on Modelling, Simulation and intelligent Computing, MoSICom 2023

作者： Pathak, Abhinav Singhal, Aditya Tewari, Adit Jain, Aadish Kalaichelvi, V. Birla Institute of Technology and Science Pilani Department of Computer Science Dubai United Arab Emirates Birla Institute of Technology and Science Pilani Department of Electrical and Electronics Engineering Dubai United Arab Emirates Birla Institute of Technology and Science Pilani Department of Electrical and Electronics Engineering United Arab Emirates

ISBN: (纸本)9798350393415

Fully autonomous mobile robots have the potential to revolutionize various industries, from warehouse management to hospital logistics and last-mile deliveries. However, a significant obstacle to achieving reliable autonomy lies in the high computational and energy requirements. In response to this challenge, our paper introduces two innovative algorithms: the Pure Image Segmentation Approach (PISA) and the UNet Based Approach to Semantic Segmentation (UBASS). PISA leverages classical computer vision techniques, offering a fresh perspective on solving crucial tasks such as object detection, object avoidance, and lane detection. In contrast, UBASS harnesses the power of deep learning algorithms for semantic segmentation, unlocking new capabilities in robot perception. Our experiments showcase the effectiveness of these algorithms, demonstrating their accuracy and computational efficiency. Notably, PISA and UBASS outperform or match traditional techniques, including End-to-End Deep Learning and Canny Edge Detection, in terms of both task performance and resource utilization. This research contributes to the advancement of autonomous mobile robotics by offering practical and efficient solutions for navigation and perception challenges. By combining classic and contemporary approaches, we aim to inspire further research in the field, ultimately paving the way for more accessible and dependable autonomous mobile robots. © 2023 IEEE.

关键词： computer vision

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ICACS 2024 - Proceedings of the 2024 8th International conference on algorithms, Computing and Systems

ICACS 2024 - Proceedings of the 2024 8th International Confe...

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8th International conference on algorithms, Computing and Systems, ICACS 2024

ISBN: (纸本)9798400718304

The proceedings contain 24 papers. The topics discussed include: autonomous floating garbage collection device using computer vision and robotics;coordinated optimization of scheduling and path planning for delivery robots in intelligent manufacturing workshops;a preliminary study of the stressed and drowsy driving prediction models using semi-supervised learning;axial capacity prediction of concrete externally strengthened by low-cost natural fiber reinforced polymer composite utilizing machine learning intelligence;enhancing brain tumor diagnosis: a cutting-edge ensemble deep learning approach;a comparative study of sentiment analysis on twitter and reddit using deep learning techniques;generative artificial intelligence for future education: current research status, hot spots, and research trends;methodological considerations for anonymizing tabular data using generative adversarial networks;and data migration in large scale storage systems with varying file sizes.

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Reexamining Indoor Mobile robots from a Cognitive Perspective 5

Reexamining Indoor Mobile Robots from a Cognitive Perspectiv...

引用

5th International conference on Robotics and computer vision, ICRCV 2023

作者： Song, Fei Li, Jinyu Tang, Fengzhen Tang, Yandong Si, Bailu State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China School of Systems Science Beijing Normal University Beijing China

ISBN: (纸本)9798350326369

The field of traditional mobile robot navigation has undergone a gradual transformation, evolving into a standardized and procedural research domain. Through a fresh cognitive perspective on this navigation process, a bridge emerges between robotic researchers and cognitive neuroscientists, thereby fostering the growth of interdisciplinary exploration. This article meticulously examines three pivotal phases of robot navigation: information acquisition, simultaneous localization and mapping (SLAM), and path planning. These phases are intricately linked to pertinent cognitive research. Furthermore, a comprehensive survey of existing biomimetic and neuromorphic algorithms is presented, highlighting their applications in augmenting robot navigation capabilities. Ultimately, this article charts a visionary course for the future of robot navigation, envisioning its potential in facilitating more intelligent and efficient navigation techniques. © 2023 IEEE.

关键词： Navigation

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A computer vision Algorithm for Path Planning in Mobile robots

A Computer Vision Algorithm for Path Planning in Mobile Robo...

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Pervasive and intelligent Computing (PICom), IEEE conference on

作者： Claudio Ruggieri Miriana Russo Corrado Santoro Federico Fausto Santoro STMicroelectronics System Research and Application Catania Italy Department of Mathematics and Informatics University of Catania Catania Italy

ISBN: (数字)9798331522742

ISBN: (纸本)9798331522759

关键词： Machine learning algorithms Accuracy Heuristic algorithms Machine vision Machine learning User interfaces Planning Trajectory Mobile robots Proposals

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Industrial 5.0 Aquaponics System Using Machine Learning techniques 7

Industrial 5.0 Aquaponics System Using Machine Learning Tech...

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7th International conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Venkatraman, M. Surendran, R. Saveetha School of Engineering Department of Computer Science and Engineering Saveetha Institute of Medical and Technical Science Chennai India

ISBN: (纸本)9798350340600

Due to the advancements in digital technology, the fifth industrial revolution, also known as aquaponics, has brought changes in the traditional manufacturing and industrial processes. The primary objective of the industry 5.0 initiative is to encourage customization and innovation while simultaneously increasing the productivity, efficiency, and adaptability of manufacturing processes. The industry's coming to terms with the 5.0 vision is underpinned by machine learning. It makes it possible for machines and systems to see patterns in data, make decisions based on that data, and learn without having to be explicitly programmed. algorithms that learn from machines are able to analyze vast amounts of data collected from sensors, machines, and other sources in order to make predictions, improve processes, and acquire insights. This capacity has applications in a variety of domains, including quality control, predictive maintenance, supply chain optimization, process optimization, and autonomous robots. Machine learning-based predictive maintenance reduces downtime and optimizes maintenance schedules by detecting industrial equipment faults. Machine learning-powered quality control systems monitor manufacturing, identify errors, and ensure product quality. U sing machine learning algorithms on supply chain data improves inventory management, logistics, and efficiency. Process optimization uses machine learning to find trends in production data to improve efficiency and energy use. Machine learning-enabled autonomous robots and intelligent decision- making systems help humans complete complicated tasks safely and efficiently. Aquaponics is a major step toward a more connected, intelligent, and data-driven industrial landscape. © 2023 IEEE.

关键词： Decision making

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IoT-Based Automation System for Vertical Hydroponic Farms 26

IoT-Based Automation System for Vertical Hydroponic Farms

引用

26th International conference on Industrial Technology, ICIT 2025

作者： Kumburage, D.K. Hewavitharana, H.V.T.G. Perera, M.N.T. Amarasinghe, Y.W.R. Jayathilaka, W.A.D.M. University of Moratuwa Dept. of Mechanical Engineering Sri Lanka

ISBN: (纸本)9798331521950

Greenhouse vertical rack hydroponic systems offer a sustainable and efficient solution for meeting the increasing global food demand. This paper introduces an IoT-integrated automated system designed to perform labor intensive and repetitive tasks in hydroponic farming. The proposed system integrates a robotic platform for transplanting and inspecting plants, as well as an intelligent controller to control and monitor greenhouse conditions and nutrient solution parameters. To enhance performance and accessibility, the system is integrated with an IoT platform, which includes a user-friendly web interface, enabling remote monitoring and control. Key features such as computer vision techniques and advanced control algorithms are implemented to maximize operational efficiency. Experimental results validate its ability to reduce labor, improve productivity, and ensure consistent crop quality. This solution highlights the potential for scalable and sustainable advancements in modern agriculture. © 2025 IEEE.

关键词： intelligent robots

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A Novel computer vision Algorithm for Enhancing Image Recognition and Classification

A Novel Computer Vision Algorithm for Enhancing Image Recogn...

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IEEE International conference on Power, intelligent Computing and Systems (ICPICS)

作者： Herun Zhong School of Arts and Design Hubei University of Technology Wuhan China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

关键词： Deep learning computer vision Visualization Image recognition Benchmark testing Visual systems Classification algorithms Object recognition Convolutional neural networks Videos

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Development of computer vision Based Aluminium Scrap Sorting Using Real Time Metal Scrap Dataset

Development of Computer Vision Based Aluminium Scrap Sorting...

引用

Sustainable Expert Systems (ICSES), International conference on

作者： Adhithya S J. Kathirvelan School of Electronics Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

The aluminium manufacturing industries face numerous challenges when it comes to sorting metal scraps, particularly when scraps are small in size. Traditional methods like flotation and gravity separation are inefficient, energy-intensive, and often fail when dealing with size variations. Recent advancements in computer vision and deep learning algorithms, including intelligent robotic systems, offer promising solutions. However, challenges such as accurate detection in complex environments, varying lighting conditions, and overlapping objects still persist. Newer techniques like YOLO (You Only Look Once) for real-time object detection, and Transfer Learning to improve model accuracy with smaller datasets, have shown potential. This research aims to overcome these challenges by developing an AI-powered, computer vision-based sorting system that can identify and classify aluminium scraps by color and shape at high speed. The authors developed a custom dataset and trained models using CNN architectures such as DenseNet-161, ResNet-152, and VGG 19. By employing ensemble techniques, this research achieved an 75% of accuracy.

关键词： Deep learning YOLO computer vision Accuracy Service robots Computational modeling Aluminum Real-time systems Recycling Sorting

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