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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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A Comprehensive Analysis of IoT and IoT Security Attacks: Understanding the Threat Landscape

A Comprehensive Analysis of IoT and IoT Security Attacks: Un...

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computational intelligence, Communication Technology and Networking (CICTN), international Conference on

作者： Himanshi Dhayal Vikas Siwach Harkesh Sehrawat Renu Dalai Department of Computer Science and Engineering UIET Maharshi Dayanand University Rohtak Haryana India University School of Automation and Robotics (USAR) GGSIPU Guru Gobind Singh Indraprastha University Delhi India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

IoT has remarkably changed the internet landscape since its introduction. Today, even an isolated physical device is provided with the internet, which allows it to exchange information with other devices. As much flexibility as these devices offer, they also have numerous security issues, causing threats to its users. Adversaries increasingly exploit the vulnerabilities in Internet of Things devices to execute attacks that can inflict significant harm on individuals, communities, and organizations. Consequently, prioritizing the detection, prevention and mitigation of the attacks is imperative to ensure safety and security. The paper provides an in-depth examination of internet of things (IoT) taxonomy, highlighting the associated challenges and security attacks within the IoT ecosystem, which subsequently gives insights into the vulnerabilities of the IoT ecosystem. Additionally, it includes a comparative analysis of various security attacks along with their impacts.

关键词： Prevention and mitigation Ecosystems Taxonomy Organizations Communications technology Safety Security Internet of Things computational intelligence

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Optimization of Crop Yields in Sustainable Agriculture: Application of Big Data Analytics and Artificial intelligence

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Remote Sensing in Earth Systems Sciences 2025年第2期8卷 375-386页

作者： Nimma, Divya Dhanke, Jyoti A. Murthy, G.S.N. Khandekar, Sachin Dadu Hymavathi, J. Jangir, Pradeep Singh, Mahesh Department of Computational Science University of Southern Mississippi Hattiesburg 39406 MS United States Department of Engineering Science (Mathematics) Bharati Vidyapeeth’s College of Engineering Lavale Maharashtra Pune India Department of CSE Aditya University Surampalem India Department of Civil Engineering Sinhgad College of Engineering Maharashtra Pune India Department of CSE Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan Department of Robotics and Automation Symbiosis Institute of Technology Pune Campus Symbiosis International (Deemed University) Pune India

Making informed economic decisions based on agricultural data is challenging without proper crop management. Data has become the single most important part of modern farming, and its rapid evolution is a major contributor to the rise of “smart farming.” Using sensors to gather objective data with the end aim of increasing productivity and sustainability has several advantages. Information that might boost productivity while decreasing resource waste and pollution levels is essential for managed farms. The cornerstone of future sustainable farming will be data-driven agriculture using robotic solutions based on AI-based techniques. This paper examines the current state of advanced farm management systems, discussing topics like data acquisition in crop fields and variable rate applications, to assist farmers in saving money, protecting the environment, and transforming food production so that it can sustainably keep up with the expected increase in population. The advent of machine learning, big data technologies, and high-performance computers has opened up new avenues for data-intensive research in the interdisciplinary subject of agro technology. These avenues of exploration have been aided by the development of more capable computers. A comprehensive literature review on the issue of the applications of machine learning in agricultural manufacturing is provided in this research. Machine learning has shown its utility in agriculture via two applications: filtering and categorizing products. The use of machine learning on sensor data is transforming conventional farm management systems into AI-enabled programs. These services provide farmers instantaneous, complete access to ideas and insights that may guide their decisions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Artificial intelligence Big data analytics Optimize crop yields Sustainable agriculture

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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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Integrating Trajectory Optimization in Quality-Diversity for Kinodynamic Motion Planning

Integrating Trajectory Optimization in Quality-Diversity for...

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international Conference on Autonomous Robots and Agents, ICARA

作者： Konstantinos A. Asimakopoulos Konstantinos I. Chatzilygeroudis Department of Electrical & Computer Engineering Laboratory of Automation & Robotics (LAR) Patras Greece Department of Mathematics Computational Intelligence Laboratory (CILab) University of Patras Patras Greece

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Kinodynamic planning is a critical component of robotics, enabling robots to generate dynamically feasible trajectories while adhering to geometric constraints. Existing methods for kinodynamic planning primarily fall into two categories: (a) stochastic planners, such as Rapidly-Exploring Random Trees (RRT) and Probabilistic Roadmaps (PRM), which excel in state-space exploration due to their directed randomness, and (b) optimization-based approaches, which are well-suited for structured environments, producing smooth and optimal solutions. In this work, we present Hybrid Trajectory Exploration for Kinodynamic Planning (HyTraX), a hybrid framework that integrates the strengths of these two paradigms. Specifically, HyTraX enhances the effectiveness of the Go-Explore algorithm by incorporating trajectory optimization with random target selection as its core exploration strategy. We evaluate HyTraX on two challenging kinodynamic motion planning tasks: a car-like agent and a planar quadrotor navigating through maze environments. Our results demonstrate significant performance improvements with minimal task-specific tuning, highlighting the robustness and versatility of the proposed approach.

关键词： Legged locomotion Scalability Performance gain Probabilistic logic Robustness Planning Trajectory optimization Robots Tuning Quadrotors

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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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computational, Communication and Information Technology (ICCCIT), international Conference on

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

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

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.

关键词： Fabrication Remotely guided vehicles automation Solids Sensors Maintenance Information and communication technology Servomotors Logistics

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Multi-Classes Object Detection and Manipulation for Industrial Robots Using Deep Learning

Multi-Classes Object Detection and Manipulation for Industri...

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international Conference on Autonomous Robots and Agents, ICARA

作者： Anawin Pechbooranin Thanatsorn Chaisirithungnaklang Suradet Tantrairatn Kontorn Chamniprasart School of Mechatronics Engineering Institute of Engineering Suranaree University of Technology Nakhon Ratchasima Thailand

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Artificial intelligence (AI) has significantly enhanced the accuracy and efficiency of various industrial sectors by automating tasks and reducing manual labor. Notably, the integration of AI, particularly deep learning, with computer vision has enabled the detection of objects within images. This capability has proven valuable in robotics applications, as demonstrated in this study. The present study utilizes the YOLOv8s model to achieve multi-class object detection and localization from webcam footage. The identified objects are then manipulated by the SCARA robot DobotM1, which picks them up and sorts them into designated piles based on their classification. The model achieved an impressive mean average precision of 99.5 percents on the validation set data. Furthermore, its integration with the robot arm facilitated accurate and reliable object handling.

关键词： Deep learning Computer vision Accuracy Service robots Webcams computational modeling Manipulators Data models Artificial intelligence Robots

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Efficient Transformer-Based Visual Tracking for Edge Computing Devices 9th

Efficient Transformer-Based Visual Tracking for Edge Computi...

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

作者： Xu, Yixin Xu, Tianyang Zhang, Wenkang Wu, Jinhui Cheng, Zinan Yang, Wankou School of Automation Southeast University Nanjing210096 China School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China School of Cyber Science and Engineering Southeast University Nanjing210096 China Jiangsu Automation Research Institute Lianyungang222002 China Advanced Ocean Institute of Southeast University Nantong226010 China

ISBN: (纸本)9789819629138

In recent years, transformer-based visual tracking models have shown significant advancements by leveraging global feature representations to improve information interaction. However, their high computational demands hinder efficient deployment on resource-constrained platforms like UAVs and robotic devices. To address this, we propose OneStar, a template-branch separable vision transformer that balances efficiency and accuracy. Unlike one-stream trackers that process the template in every frame, OneStar performs template feature extraction and fusion only during initialization, reducing redundant computations. A tracking token is designed to guide the use of prior positional and size information from the initial frame, enhancing fusion accuracy. We also provide two model variants optimized for different edge devices, ensuring efficient resource use while maintaining strong tracking performance. Extensive evaluations show that OneStar delivers competitive accuracy with state-of-the-art models while achieving excellent real-time performance on edge platforms. For example, OneStar achieves a 4.9% AO improvement on GOT-10k compared to other real-time trackers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Machine vision

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Spatially Nonuniform Filtering and Encoding of Visual Information in a Retinomorphic Hardware System

Spatially Nonuniform Filtering and Encoding of Visual Inform...

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international Conference on Autonomous Robots and Agents, ICARA

作者： Tatsuya Yamada Hiroyuki Yokota Yuki Hayashida Shinsuke Yasuakwa Dept. Human Intelligence Syst. Kyushu Institute of Technology Kitakyushu Fukuoka JAPAN Dept. Inform. Eng. Grad. Eng. Mie University Tsu Mie JAPAN

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Efficient information processing in biological early-vision systems is thought to rely on spatially nonuniform computations in the central and peripheral visual fields. Inspired by this functional segregation, we developed a digital hardware vision system that integrates two distinct spatiotemporal filters and spike encoders for the central and peripheral regions on a single image sensor plane. The system employs digital processing techniques to emulate graded potential signals and point-process spike outputs, mimicking the functionality of vertebrate retinas. System-level testing on actual hardware equipment verified region-specific response characteristics for the central and peripheral regions. This proposed approach advances the realization of retinomorphic efficient computations in future robotics and embedded vision systems.

关键词： Visualization Image coding Robot vision systems Optimization methods Retina Hardware Spatiotemporal phenomena computational efficiency Robots Testing

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Model of Research for Engineering Related Investigations

Model of Research for Engineering Related Investigations

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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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