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A Survey of computer vision Detection, Visual SLAM algorithms, and Their Applications in Energy-Efficient Autonomous Systems

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ENERGIES 2024年第20期17卷 5177页

作者： Chen, Lu Li, Gun Xie, Weisi Tan, Jie Li, Yang Pu, Junfeng Chen, Lizhu Gan, Decheng Shi, Weimin Univ Elect Sci & Technol China Sch Aeronaut & Astronaut Chengdu 611731 Peoples R China Yangtze Normal Univ Sch Elect Informat Engn Chongqing 408100 Peoples R China Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China

Within the area of environmental perception, automatic navigation, object detection, and computer vision are crucial and demanding fields with many applications in modern industries, such as multi-target long-term visual tracking in automated production, defect detection, and driverless robotic vehicles. The performance of computer vision has greatly improved recently thanks to developments in deep learning algorithms and hardware computing capabilities, which have spawned the creation of a large number of related applications. At the same time, with the rapid increase in autonomous systems in the market, energy consumption has become an increasingly critical issue in computer vision and SLAM (Simultaneous Localization and Mapping) algorithms. This paper presents the results of a detailed review of over 100 papers published over the course of two decades (1999-2024), with a primary focus on the technical advancement in computer vision. To elucidate the foundational principles, an examination of typical visual algorithms based on traditional correlation filtering was initially conducted. Subsequently, a comprehensive overview of the state-of-the-art advancements in deep learning-based computer vision techniques was compiled. Furthermore, a comparative analysis of conventional and novel algorithms was undertaken to discuss the future trends and directions of computer vision. Lastly, the feasibility of employing visual SLAM algorithms in the context of autonomous vehicles was explored. Additionally, in the context of intelligent robots for low-carbon, unmanned factories, we discussed model optimization techniques such as pruning and quantization, highlighting their importance in enhancing energy efficiency. We conducted a comprehensive comparison of the performance and energy consumption of various computer vision algorithms, with a detailed exploration of how to balance these factors and a discussion of potential future development trends.

关键词： computer vision object detection visual tracking energy efficiency visual SLAM deep learning driverless robotic vehicles

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Comparison of Indoor Position Recognition Accuracy Using Grid Pattern Tracking Autonomous Mobile Robot

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2025年 1-14页

作者： Kim, Ki-Hyun Choi, Sang-Yule Choi, Jae-Hoon Kim, Jung-Ju Hoseo Univ Dept Automot ICT Cheonan South Korea Induk Univ Dept Mechatron Engn Seoul South Korea Hoseo Univ Dept Robot Cheonan South Korea Hoseo Univ Dept Intelligent Robot Cheonan South Korea

Existing studies on indoor position recognition employ diverse evaluation methods, which complicates direct accuracy comparisons across techniques. To address this issue, this study proposes a novel framework for evaluating the accuracy of indoor position recognition methods. The proposed framework evaluates accuracy by using the position recognition results of a grid-pattern-tracking autonomous mobile robot (GPT-AMR) as a benchmark. To validate the proposed evaluation method, a comparative analysis was conducted on four position recognition algorithms: (1) a computer vision-based algorithm, (2) a Bluetooth Low Energy (BLE)-based trilateration algorithm, (3) a BLE-based adaptive trilateration algorithm, and (4) a least squares method (LSM)-based algorithm. Experimental results demonstrated that the proposed evaluation method, which employs GPT-AMR, offers improved speed, accuracy, and practical applicability compared to conventional approaches. Furthermore, this method enables objective comparisons and evaluations of a wide range of indoor position recognition technologies, including both computer vision- and BLE-based algorithms, using a standardized criterion. Future research will focus on systematically validating the generalizability of the proposed method across different indoor environments and operational conditions. This study aims to advance indoor position recognition technology for autonomous mobile robots (AMRs) and improve their applicability in various service robotics domains.

关键词： Autonomous mobile robot Indoor position recognition Bluetooth low energy Trilateration Least squares method Adaptive trilateration

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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...

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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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Artificial intelligence in medical science: a review

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IRISH JOURNAL OF MEDICAL SCIENCE 2024年第3期193卷 1419-1429页

作者： Bindra, Simrata Jain, Richa Motilal Nehru Coll Dept Phys Benito Juarez Rd New Delhi 110021 India

Artificial intelligence (AI) is a technique to make intelligent machines, mainly by using smart computer programs. It is based on a statistical analysis of data or machine learning. Using machine learning, software algorithms are designed according to the desired application. These techniques are found to have the potential for advancement in the medical field by generating new and significant perceptions from the data generated using various types of healthcare tests. Artificial intelligence (AI) in medicine is of two types: virtual and physical. The virtual part decides the treatment using electronic health record systems using various sensors whereas the physical part assists robots to perform surgeries, implants, replacement of various organs, elderly care, etc. Using AI, a machine can examine various kinds of health care test reports in one go which could save the time, money, and increase the chances of the patient to be treated without any hassles. At present, artificial intelligence (AI) is used while deciding the treatment, and medications using various tools which could analyze X-rays, CT scans, MRIs, and any other data. During the COVID pandemic, there was a huge/massive demand for AI-supported technologies and many of those were created during that time. This study is focused on various applications of AI in healthcare.

关键词： Artificial intelligence (AI) computer vision Clustered regularly interspaced short palindromic repeats (CRISPR) Deep learning Machine learning MYCIN

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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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Deep Learning-Based Visual Navigation algorithms for Mobile robots: A Comprehensive Study

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Journal of Computing and Information Technology 2022年第4期30卷 257-273页

作者： Yu, Wei Tian, Xinzhi Xi'an Siyuan University Shaanxi Xi'an China

This research addresses the challenges faced by mobile robots in efficiently navigating complex environments. A novel approach is proposed, leveraging deep learning techniques, and introducing the Neo model. The method combines Split Attention with the ResNeSt50 network to enhance the recognition accuracy of key features in the observed images. Furthermore, improvements have been made in the loss calculation method to improve navigation accuracy across different scenarios. Evaluations conducted on AI2THOR and active vision datasets demonstrate that the improved model achieves higher average navigation accuracy (92.3%) in scene 4 compared to other methods. The success rate of navigation reached 36.8%, accompanied by a 50% reduction in ballistic length. Additionally, compared to HAUSR and LSTM-Nav, this technology significantly reduced collision rates to 0.01 and reduced time consumption by over 8 seconds. The research methodology addresses navigation model accuracy, speed, and generalization issues, thus making significant advancements for intelligent autonomous robots. ACM CCS (2012) Classification: Computing methodologies → Machine learning → Machine learning algorithms Artificial intelligence → computer vision → vision for robotics. © 2022, University of Zagreb Faculty of Electrical Engineering and Computing. All rights reserved.

关键词： Navigation

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Artificial Intelligence for Robotics: Build intelligent robots using ROS 2, Python, OpenCV, and AI/ML techniques for real-world tasks

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2024年

作者： Francis X. Govers III Dr. Kamesh Namuduri

Let an AI and robotics expert help you apply AI, systems engineering, and ML concepts to create smart robots capable of interacting with their environment and users, making decisions, and navigating autonomouslyKey Fe...

ISBN: (数字)9781805124399

Key Features

Gain a holistic understanding of robot design, systems engineering, and task analysis
Implement AI/ML techniques to detect and manipulate objects and navigate robots using landmarks
Integrate voice and natural language interactions to create a digital assistant and artificial personality for your robot
Purchase of the print or Kindle book includes a free PDF eBook

Book Description

Unlock the potential of your robots by enhancing their perception with cutting-edge artificial intelligence and machine learning techniques. From neural networks to computer vision, this second edition of the book equips you with the latest tools, new and expanded topics such as object recognition and creating artificial personality, and practical use cases to create truly smart robots. Starting with robotics basics, robot architecture, control systems, and decision-making theory, this book presents systems-engineering methods to design problem-solving robots with single-board computers. You"ll explore object recognition using YOLO and genetic algorithms to teach your robot to identify and pick up objects, leverage natural language processing to give your robot a voice, and master neural networks to classify and separate objects and navigate autonomously, before advancing to guiding your robot arms using reinforcement learning and genetic algorithms. The book also covers path planning and goal-oriented programming to prioritize your robot"s tasks, showing you how to connect all software using Python and ROS 2 for a seamless experience. By the end of this book, you"ll have learned how to transform your robot into a helpful assistant with NLP and give it an artificial personality, ready to tackle real-world tasks an

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Reexamining Indoor Mobile robots from a Cognitive Perspective 5

Reexamining Indoor Mobile Robots from a Cognitive Perspectiv...

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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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