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9th International Conference on Interactive Collaborative robotics, ICR 2024

作者： Imamov, Niyaz Abbyasov, Bulat Tsoy, Tatyana Martínez-García, Edgar A. Magid, Evgeni Laboratory of Intelligent Robotic Systems Intelligent Robotics Department Institute of Information Technology and Intelligent Systems Kazan Federal University 35 Kremlyovskaya St. Kazan420111 Russia Tikhonov Moscow Institute of Electronics and Mathematics HSE University Tallinn Street 34 Moscow Russia Institute of Engineering and Technology Department of Industrial Engineering and Manufacturing Autonomous University of Ciudad Juarez Manuel Díaz H. No. 518-B Zona Pronaf Condominio Chihuahua Cuidad Juárez32315 Mexico

ISBN: (纸本)9783031713590

Unmanned aerial vehicles (UAVs) are utilized in a wide range of real-world applications including search and rescue missions, 3D mapping, building inspection and reconstruction. In a real-world environment, severe weather conditions significantly impact UAV operations. Wind is one of the most dangerous weather factors that can destabilize and even destroy a UAV. Preparing UAV flight algorithms for real-world conditions in order to minimize risks of damage and malfunctioning is a crucial part of a UAV system development. This paper analyses a wind influence on UAV virtual model performance within the Gazebo simulator. The paper focuses on gazebo_wind_plugin package used for creating a windy virtual environment. PX4-LIRS package is used for simulating a UAV within the Gazebo. Three types of experiments were run to evaluate capabilities of the plugin to simulate typical issues that arise in real-world scenarios: a UAV wind resistance, a wind effect on a UAV tilt angle and on a flight velocity. Virtual experiments demonstrated that gazebo_wind_plugin could serve for PX4 UAV flight scenarios modeling in Gazebo simulation with a satisfactory level of realism. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Unmanned aerial vehicles (UAV)

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A novel robotic visual perception framework for underwater operation

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Frontiers of Information Technology & Electronic engineering 2022年第11期23卷 1602-1619页

作者： Yue LU Xingyu CHEN Zhengxing WU Junzhi YU Li WEN State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Ytech Kuaishou TechnologyBeijing 100085China State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China School of Mechanical Engineering and Automation Beihang UniversityBeijing 100191China

Underwater robotic operation usually requires visual perception(e.g.,object detection and tracking),but underwater scenes have poor visual quality and represent a special domain which can affect the accuracy of visual *** addition,detection continuity and stability are important for robotic perception,but the commonly used static accuracy based evaluation(i.e.,average precision)is insufficient to reflect detector performance across *** response to these two problems,we present a design for a novel robotic visual perception ***,we generally investigate the relationship between a quality-diverse data domain and visual restoration in detection *** a result,although domain quality has an ignorable effect on within-domain detection accuracy,visual restoration is beneficial to detection in real sea scenarios by reducing the domain ***,non-reference assessments are proposed for detection continuity and stability based on object ***,online tracklet refinement is developed to improve the temporal performance of ***,combined with visual restoration,an accurate and stable underwater robotic visual perception framework is ***-overlap suppression is proposed to extend video object detection(VID)methods to a single-object tracking task,leading to the flexibility to switch between detection and *** experiments were conducted on the ImageNet VID dataset and real-world robotic tasks to verify the correctness of our analysis and the superiority of our proposed *** codes are available at https://***/yrqs/VisPerception.

关键词： Underwater operation Robotic perception Visual restoration Video object detection

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Distributed Event-Triggered Formation Control for a Multi-robotic Fish System

Distributed Event-Triggered Formation Control for a Multi-ro...

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International Conference on Guidance, Navigation and Control, ICGNC 2022

作者： Dai, Shijie Wu, Zhengxing Tan, Min Yu, Junzhi School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China State Key Laboratory for Turbulence and Complex System Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Peking University Beijing100871 China

ISBN: (纸本)9789811966125

This paper investigates the formation control problem for a multi-robotic fish system and a distributed event-triggered-based formation control framework is proposed. The framework consists of a communication topology, a distributed formation control law, and a dynamic event-triggered communication mechanism. In particular, a position-based formation control law is presented to drive the multi-robotic fish system to an anticipated configuration based on local measurements while keeping pace with a moving target. Meanwhile, a dynamic event-triggered mechanism is utilized to measure importance of the interactive information and decide the communication timing. The proposed formation control method shows its merits to greatly reduce communication consumption with a limited loss of formation control performance. Finally, simulations with quantitative comparisons are presented to verify the effectiveness of the proposed framework. This formation control framework provides a solid foundation for future marine cooperative control of the multi-robotic fish system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multi agent systems

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Optimizing Route Efficiency in Formula One (F1) Vehicles Using Reinforcement Learning Algorithms

Optimizing Route Efficiency in Formula One (F1) Vehicles Usi...

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AI in Cybersecurity (ICAIC), International Conference on

作者： Sanam Narejo Muhammad Taimoor Khan Muhammad Zakir Shaikh Lubna Luxmi Dhirani Bhawani Shankar Chowdhery Department of Computer Systems Mehran Univerity of Engineering and Technology Jamshoro Pakistan National Centre for Robotics Automation and Artificial Intelligence Mehran Univerity of Engineering and Technology Jamshoro Pakistan Mechanical Engineering and energy efficiency School of Industrial Engineering University of Malaga Malaga Spain Department of Electronic and Computer Engineering University of Limerick Limerick Ireland

ISBN: (数字)9798331518882

ISBN: (纸本)9798331518899

This research explores the application of reinforcement learning (RL) to enhance route efficiency and performance of a Formula One (F1) car within a simulation environment. The simulation is implemented using Python, NEAT (NeuroEvolution of Augmenting Topologies), and PyGame to create a dynamic system where neural networks control the car’s navigation. RL enables the F1 car, acting as an agent, to learn optimal decisions through a fitness-based reward mechanism by interacting with its environment. Equipped with radar sensors to detect obstacles and measure distances, the virtual car adjusts its speed and steering to avoid collisions and optimize movement. Over successive generations, the RL algorithm refines the car’s driving ability, improving speed and directional control to maximize distance covered and minimize lap times. A fitness-based evaluation system tracks progress, providing metrics such as best and average fitness scores, which highlight the car’s evolving performance. Results demonstrate the effectiveness of RL in enhancing autonomous driving capabilities, enabling the car to navigate complex environments and improve decision-making across generations.

关键词： Navigation Tracking Neural networks Decision making Reinforcement learning Radar tracking Topology Sensors Automobiles Vehicle dynamics

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Decentralized Reinforcement Learning for Multiple Robotic Fish in Cooperative Pursuit Task

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IEEE Transactions on Cognitive and Developmental systems 2025年

作者： Feng, Yukai Wu, Zhengxing Wang, Jian Li, Sijie Huang, Yupei Yu, Junzhi Tan, Min Institute of Automation Chinese Academy of Sciences Key Laboratory of Cognition and Decision Intelligence for Complex Systems Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China College of Engineering Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics Beijing100871 China

The control of multi-robot systems, particularly in the pursuit-evasion (PE) with multiple robots, has gained significant attention in both academic and non-academic settings. However, the collaborative operation of multi-robotic fish systems encounters substantial challenges due to the complex underwater environment and unique movement mode. In this paper, we propose a multi-agent reinforcement learning (MARL) approach to develop a viable strategy for underwater cooperative pursuit. Initially, considering the hydrodynamic model and motion characteristics of robotic fish, we construct a specific simulation environment with multiple fish-like agents, which provides a highly realistic state transition model. Next, we develop a MARL-based strategy learning framework that incorporates appropriate reward functions and agent actions for policy learning. Finally, a series of comprehensive simulations and practical experiments are conducted to validate the effectiveness of the proposed method and confirm its successful application in underwater pursuit scenarios. These findings offer valuable insights for further research in underwater multiple robot systems. © 2016 IEEE.

关键词： Chatbots

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Enhanced EEG-fNIRS Classification through Concatenated Convolutional Neural Network with Band Analysis

Enhanced EEG-fNIRS Classification through Concatenated Convo...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Bunterngchit, Chayut Wang, Jiaxing Chearanai, Thanaphon Hou, Zeng-Guang Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China The University of Chinese Academy of Sciences Beijing100049 China King Mongkut's University of Technology North Bangkok Division of Industrial and Logistics Engineering Technology Department of Engineering and Technology Rayong Campus Rayong21120 Thailand

ISBN: (纸本)9798350325706

Brain-computer interfaces (BCIs) have emerged as a groundbreaking technology enabling direct communication between the human brain and external devices. Yet, the attainment of higher accuracy rates while making classifications remains a problem and demands the development of more sophisticated machine learning architectures and the exploration of novel feature domains. To address these issues, we have presented a novel approach for enhanced EEG-fNIRS (electroencephalography-functional near-infrared spectroscopy) classification through a concatenated convolutional neural network (CNN) with band analysis. Our proposed methodology leverages the power spectral density (PSD) of EEG signals in specific frequency bands and captures metabolic changes in the brain through fNIRS data. Then it employs a concatenated CNN architecture for improved feature extraction and classification in EEG-fNIRS data. Through an experimental evaluation on a public dataset containing three mental workload (MWL) tasks, our proposed model demonstrates superiority compared to previous studies by achieving higher accuracy rates of 93.8%, 97.2%, and 94.5% for n-back, discrimination/selection response (DSR), and word generation (WG) tasks, respectively. Furthermore, our results emphasize the advantages of utilizing a hybrid EEG-fNIRS approach over unimodal EEG or fNIRS methods. These outcomes underscore the potential of our multimodal EEG-fNIRS approach and its applicability in BCI, neurorehabilitation, and human-computer interaction contexts. © 2023 IEEE.

关键词： brain-computer interface convolutional neural network multimodal neuroimaging power spectral density

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Design of a Novel Autonomous Docking System With a Supporting Robotic Fish

Design of a Novel Autonomous Docking System With a Supportin...

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2023 IEEE International Conference on Real-Time Computing and robotics, RCAR 2023

作者： Dong, Huijie Zhao, Chunyang Qiao, Tiezhu Wang, Jian Chen, Di Meng, Yan Yu, Junzhi Taiyuan University of Technology Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan030024 China Chinese Academy of Sciences Laboratory of Cognitive and Decision Intelligence for Complex System Institute of Automation Beijing100190 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Beijing100871 China Guangdong Ocean University College of Electronics and Information Engineering Zhanjiang524088 China

ISBN: (纸本)9798350327182

Underwater supporting robots serving as a relay of energy supplements and communication for other underwater equipment are promising for ocean exploration, development, and protection. This paper proposes a novel autonomous docking system centered on a designed supporting robotic fish named 'CourierFish'. Specifically, CourierFish is capable of docking with a surface dock station for supplying itself and docking with a seafloor platform for supporting equipment in the platform. A visual navigation scheme integrating LED and ArUco markers is presented for accurate localization. The control approach for docking motion is also illustrated. Simulations and aquatic experiments are performed to verify the feasibility of the proposed docking system. The obtained results lay a solid foundation for the construction of various underwater equipment and robot networks. © 2023 IEEE.

关键词： Fish

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An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

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Computers, Materials & Continua 2023年第2期74卷 2905-2925页

作者： Walid El-Shafai Noha A.El-Hag Ahmed Sedik Ghada Elbanby Fathi E.Abd El-Samie Naglaa F.Soliman Hussah Nasser AlEisa Mohammed E.Abdel Samea Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr El-SheikhEgypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Medical Imaging and Interventional Radiology National Liver InstituteMenoufia UniversityEgypt

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and *** learning provides a high performance for several medical image analysis *** paper proposes a deep learning model for the medical image fusion *** model depends on Convolutional Neural Network(CNN).The basic idea of the proposed model is to extract features from both CT and MR ***,an additional process is executed on the extracted *** that,the fused feature map is reconstructed to obtain the resulting fused ***,the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching(HM),Histogram Equalization(HE),fuzzy technique,fuzzy type,and Contrast Limited Histogram Equalization(CLAHE).The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement *** realistic datasets of different modalities and diseases are tested and ***,real datasets are tested in the simulation analysis.

关键词： Image fusion CNN deep learning feature extraction evaluation metrics medical diagnosis

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Problems in Control systems of Electric Drives of Metalworking Machines

Problems in Control Systems of Electric Drives of Metalworki...

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IEEE NW Russia Young Researchers in Electrical and Electronic engineering Conference (EIConRusNW)

作者： Nikolay V. Lazovskiy Oleg A. Kundyukov Kirill K. Ershov Department of Robotics and Automation of Production Systems St. Petersburg state electrotechnical University “LETI” Saint Petersburg Russia Department of Automated Electric Drive and Electrical Engineering Saint Petersburg state University of industrial technologies and design Saint Petersburg Russia

The types of errors of machine tools are considered. Methods for increasing the productivity of machine tools and the accuracy of processing workpieces are analyzed. Promising directions for the development of electri...

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Markerless Visual Servoing Using Virtual Face Tag and Image Moment Invariants 4

Markerless Visual Servoing Using Virtual Face Tag and Image ...

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4th International Conference on robotics and Computer Vision, ICRCV 2022

作者： Zubair, Arif Fu, Yili Sohail, Amjad Khadija, Anwar Harbin Institute of Technology State Key Laboratory of Robotics and Systems Mechanical Engineering Department Harbin City China Space and Upper Atmosphere Research Commission Divisional Head TandD Hr Directorate General Karachi Pakistan Ned University of Engineering and Technology Industrial and Manufacturing Department Karachi Pakistan

ISBN: (数字)9781665481700

ISBN: (纸本)9781665481700

A novel normalized-centered image moment invariant-based visual servoing scheme is presented. A fresh combination of the moments invariants is proposed for planar symmetrical objects that are parallel to the image plane at the desired position. The output of the face detection module in the image plane, that is a rectangle around the face is used as a virtual target, treated as a planar symmetrical object which always remains parallel to the image plane. The proposed method completely decouples the DoF and is 2D translation, rotation, and scaling invariant. It was experimentally validated on a real robotic arm using a haar cascade-based face detection system to accomplish visual servoing tasks for both 2D printed faces and actual 3D faces. Unlike traditional deep learning based visual servoing schemes, the proposed markerless visual servoing technique does not require a 3D model of the face or a reference image/pose at the desired position and it can also work with unfamiliar faces without prior training. © 2022 IEEE.

关键词： Face recognition

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