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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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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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Review of Underwater Visual Navigation and Docking: Advances and Challenges 6

Review of Underwater Visual Navigation and Docking: Advances...

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6th Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition

作者： Li, Yiyang Sun, Kai State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (数字)9781510679733

ISBN: (纸本)9781510679726

The widespread utilization of autonomous underwater vehicles in marine science and engineering has underscored the paramount importance of precise underwater navigation and docking capabilities for underwater robots. Underwater vision navigation systems serve as a pivotal technology, encompassing various domains such as computer vision, image processing, and machine learning. Nevertheless, they confront a series of challenges. This review aims to consolidate the most recent research advancements in underwater vision navigation systems, with a particular focus on critical aspects such as beacon design, monocular and binocular vision, and detection algorithms. At present, underwater beacons can be categorized into active and passive types, with the flexibility to choose between them contingent upon the specific task and environment. On the other hand, relative to the prevalent use of monocular vision, binocular vision systems offer the potential to furnish more accurate depth information, and the seamless integration of monocular and binocular vision systems enhances the precision and stability of navigation tasks. Concerning detection algorithms, traditional feature extraction methods and deep learning approaches each exhibit their merits and drawbacks, but deep learning methods demonstrate immense potential in complex underwater environments. By summarizing and analyzing the above research results, the application of each key technology of underwater vision navigation and docking is sorted out, and its further development direction is foreseen. © 2024 SPIE. All rights reserved.

关键词： Autonomous underwater vehicles

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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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Cognitive Exoskeletons: Harnessing AI for Enhanced Wearable Robotics in Shipbuilding 1st

Cognitive Exoskeletons: Harnessing AI for Enhanced Wearable ...

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1st European Symposium on Artificial Intelligence in Manufacturing (ESAIM)

作者： Kanakis, Alexandros Katsampiris-Salgado, Konstantinos Zacharaki, Natalia Dimitropoulos, Nikos Makris, Sotiris Univ Patras Lab Mfg Syst & Automat Rion 26504 Greece

ISBN: (纸本)9783031574955;9783031574962

In recent years, manufacturing has been undergoing major changes in search of more flexible and intelligent solutions, powered by Industry 4.0. While automation is what drove previous progress in industry, not every process can be automated, therefore, there is still a need for humans to carry out more complex tasks. Thus, in order to increase productivity and efficiency, while maintaining flexibility, robots can assist operators in their tasks rather than replace them. This paper proposes techniques for achieving seamless Human - Robot Collaboration (HRC), with wearable robots equipped with cognitive abilities that make them aware of their environments and the tasks the operator is performing. The main focus is on semi-active exoskeletons with adjustable torque and how they can be made autonomous. Their awareness is powered by computer vision techniques, mainly using anRGB-Dcamera and can automatically adjust the level of assistance they provide to the operator based on what they perceive, rather than having the operator control them directly. Consequently, they do not only improve the workload of the operator, but this assistance is offered without requiring specific actions from the user. This solution mainly focuses on the shipbuilding industry and the various tasks the workers have to perform in which exoskeletons can aid them. Finally, a welding use case is presented demonstrating the various functionalities.

关键词： Machine vision Human action perception Gesture recognition Wearable robotics Exoskeletons

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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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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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