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IEEE Open Journal of the computer Society 2025年

作者： Alam, Mahanur Tutul, Md Johirul Islam Wadud, Md. Anwar Hussen Hossen, Md. Jakir Mridha, M.F. Bangladesh University of Business and Technology Department of Computer Science and Engineering Dhaka Bangladesh Sunamgonj Science and Technology University Department of Computer Science and Engineering Sunamganj Bangladesh Multimedia University Department of Robotics and Automation Faculty of Engineering and Technology Melaka Malaysia American International University-Bangladesh Department of Computer Science Dhaka Bangladesh

Farmers in rural areas often struggle to access crucial agricultural information due to language barriers, low literacy rates, and limited exposure to digital tools. While many can write in Bangla, most agricultural resources are available only in English or require navigating complex systems, making it difficult for them to find relevant information. Existing Optical Character Recognition (OCR) technologies, which could help bridge this gap, are primarily designed for printed text and often fail to recognize handwritten Bangla script accurately. Issues such as biased datasets, diverse handwriting styles, and background noise further reduce accuracy, making these systems unreliable for real-world use. To tackle these challenges, we have developed a lightweight and unbiased OCR model specifically for handwritten Bangla text. Our solution integrates a custom Convolutional Neural Network (CNN) with InceptionV3, enhancing recognition accuracy while ensuring efficiency for low-resource devices like smartphones. Additionally, we incorporate a two-way translation feature, enabling seamless Bangla-to-English and English-to-Bangla conversion. This allows farmers to write in Bangla, translate content when needed, and access critical information in a way that best suits them. Our solution empowers rural farmers by enabling them to interact with digital platforms in their native language, bridging the gap between handwritten communication and modern technology. Beyond agriculture, this technology has far-reaching applications in tourism, healthcare, education, and government services, fostering digital inclusion. By advancing OCR for Bangla, our research promotes equitable access to technology, equipping communities with essential tools to improve productivity and quality of life in the digital era. © 2020 IEEE.

关键词： Deep learning

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Design and Evaluation of a UGV-Based Robotic Platform for Precision Soil Moisture Remote Sensing

arXiv

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arXiv 2025年

作者： Tsimpidi, Ilektra Tevetzidis, Ilias Sumathy, Vidya Nikolakopoulos, George Robotics and AI Team Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden

This extended abstract presents the design and evaluation of AgriOne, an automated unmanned ground vehicle (UGV) platform for high precision sensing of soil moisture in large agricultural fields. The developed robotic system is equipped with a volumetric water content (VWC) sensor mounted on a robotic manipulator and utilizes a surface-aware data collection framework to ensure accurate measurements in heterogeneous terrains. The framework identifies and removes invalid data points where the sensor fails to penetrate the soil, ensuring data reliability. Multiple field experiments were conducted to validate the platform’s performance, while the obtained results demonstrate the efficacy of the AgriOne robot in real-time data acquisition, reducing the need for permanent sensors and labor-intensive methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Intelligent robots

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On the 3D trochoidal motion model of LiDAR sensors placed off-centered inside spherical mobile mapping systems

On the 3D trochoidal motion model of LiDAR sensors placed of...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Fabian Arzberger Andreas Nüchter Computer Science XVII – Robotics Julius-Maximilians-Universität Würzburg Am Hubland Germany

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

We study the motion model of a sensor rigidly mounted inside a ball. Due to the rigid placement inside the ball, the geometry of the sensor trajectory resembles a 3D curate trochoid. A new calibration method for spherical systems estimates the extrinsic parameters of the sensor with respect to the balls center of rotation. We deploy the calibration and motion model on our spherical mobile mapping platform to estimate the trajectory of a LiDAR sensor and compare it to trajectories of state-of-the-art LiDAR-Inertial odometry (LIO) methods. The motion model, which is solely based on IMU measurements, produces comparable results to the LIO methods, sometimes even outperforming them in positional accuracy. Although the LIO methods provide better rotational accuracy due to the utilization of LiDAR data, they struggle to reproduce the trochoidal nature of the trajectory and only provide pose estimations at the LiDAR frequency, whereas the motion model produces a more consistent trochoidal trajectory at the much higher IMU frequency. The results demonstrate the difficulty that current LIO methods have on spherical systems and indicate that our motion model is suitable for overcoming these issues.

关键词： Point cloud compression Geometry Laser radar Image color analysis Predictive models Three-dimensional printing Frequency estimation Data models Trajectory Calibration

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Collision-free landing of multiple UAVs on moving ground vehicles using time-varying control barrier functions

arXiv

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arXiv 2025年

作者： Sankaranarayanan, Viswa Narayanan Saradagi, Akshit Satpute, Sumeet Nikolakopoulos, George Robotics and Artificial Intelligence Group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden

In this article, we present a centralized approach for the control of multiple unmanned aerial vehicles (UAVs) for landing on moving unmanned ground vehicles (UGVs) using control barrier functions (CBFs). The proposed control framework employs two kinds of CBFs to impose safety constraints on the UAVs’ motion. The first class of CBFs (LCBF) is a three-dimensional exponentially decaying function centered above the landing platform, designed to safely and precisely land UAVs on the UGVs. The second set is a spherical CBF (SCBF), defined between every pair of UAVs, which avoids collisions between them. The LCBF is time-varying and adapts to the motions of the UGVs. In the proposed CBF approach, the control input from the UAV’s nominal tracking controller designed to reach the landing platform is filtered to choose a minimally-deviating control input that ensures safety (as defined by the CBFs). As the control inputs of every UAV are shared in establishing multiple CBF constraints, we prove that the control inputs are shared without conflict in rendering the safe sets forward invariant. The performance of the control framework is validated through a simulated scenario involving three UAVs landing on three moving targets. © 2025, CC BY-NC-ND.

关键词： Drones

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Resilient Consensus in Opinion Dynamics Under Adversarial Epidemics

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IFAC-PapersOnLine 2023年第2期56卷 1841-1846页

作者： Tatsuya Masada Yuan Wang Hideaki Ishii Department of Computer Science Tokyo Institute of Technology Yokohama Japan Department of Robotics Hunan University Changsha China

In this paper, we discuss the opinion dynamics with bounded confidence in multi-agent systems under an infection spreading environment. The dynamics of the infection spreading processes follows the so-called susceptible-infected-recovered (SIR) model. Here, the infection induces faulty behaviors in the agents whose opinions may deviate from their true opinions. Cooperating with infection suppression policies and the resilient algorithm based on the mean sub-sequence reduced (MSR) approach, resilient consensus can be attained by the regular agents within a safe region. In particular, we establish sufficient conditions for resilient consensus of opinion dynamics with large bounded confidence. A numerical example is provided to verify the effectiveness of our proposed approach.

关键词： Multi-agent systems Opinion dynamics Resilient consensus SIR epidemic model

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Passively Adapting External Force Compensation System for Serial Manipulators 16th

Passively Adapting External Force Compensation System for S...

引用

16th International Federation of Theory of Machines and Mechanisms World Congress, IFToMM WC 2023

作者： Demian, Albert Klimchik, Alexandr Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis420500 Russia School of Computer Science College of Science University of Lincoln Brayford Pool Lincolnshire LincolnLN6 7TS United Kingdom

ISBN: (纸本)9783031457692

This paper proposes a design approach for a spring-based compensator that passively adapts to external loadings. The proposed compensator is based on a combination of spring-lever mechanisms that are adjustable to the robot’s configuration. Springs in this arrangement are mounted on sliding pivots that connected to springs that allow passive adjustment of the value of the counter-torque. While previous research is either concerned with gravity compensation or with variable stiffness actuators, the proposed approach deals with the variation of external loadings. The proposed force compensator can be used in robotics applications where the robot experiences high external loads during operation. The model was tested in simulation for a planar 2-DoF manipulator and a planar redundant 3-DoF manipulator. The presented results show 58.5% torque reduction for 2-DoF and 55.2% reduction for 3-DoF. Robot redundancy can enhance counter-balancing since the compensation level is configuration-dependent. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Robots

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Analysis of Causal Factor of Driver Fatigue in the Driving in Different Driving Conditions

Analysis of Causal Factor of Driver Fatigue in the Driving i...

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International Joint Conference on Neural Networks (IJCNN)

作者： Masataka Adachi Sou Nobukawa Keiichiro Inagaki Department of Robotics Science and Engineering Chubu University Kasugai Japan Department of Computer Science Chiba Institute of Technology Narashino Japan Department of Artificial Intelligence and Robotics Chubu University Kasugai Japan

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Addressing driving fatigue is crucial among the various factors in traffic accidents. The origins of driver fatigue encompass prolonged driving, sleep deprivation, and the induction of stress inherent in driving. The causes of such driver fatigue were categorized into physical factors, including sleep deprivation and prolonged driving, and perceptional-behavioral factors such as perception of driving conditions. Numerous studies have explored technologies to detect driver fatigue in terms of relevance to sleep states, eye movements, electroencephalography (EEG) activation levels, and their functional connectivity. It was also found differences in the occurrence of driver fatigue and related theta and alpha band activity based on driving conditions. However, the factors contributing to these differences remained unexplored. This study seeks to investigate the causal relationship between driver fatigue induced in different driving environments and the corresponding brain activity. We focus on perceptional load, particularly the perception of traffic objects within the driving condition, and physical loads, such as steering, acceleration, and braking, associated with variations in driving environments. In the experiment, we evaluate the relationship between such differences in physical and mental load and the occurrence of fatigue dependent on the driving condition with considering EEG activation patterns. In the results, we found that the amount of load related to driver perception and operation inherent in driving environments plays a crucial role in driver fatigue.

关键词： Sleep Human factors Fatigue Electroencephalography Biological neural networks Accidents

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Simulation of a Differential Robot Drive in Gazebo

Simulation of a Differential Robot Drive in Gazebo

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2023 International Conference "Quality Management, Transport and Information Security, Information Technologies", IT and QM and IS 2023

作者： Tseeva, Fatimat M. Shogenova, Mariyana M. Senov, Khamisha M. Nogerov, Ibragim A. Bozieva, Asiyat M. Institute of Computer Science Electronics and Robotics Federal State Budgetary Educational Institution of Higher Education Kabardino-Balkarian State University named after H.M. Berbekov Nalchik Russia

ISBN: (纸本)9798350340532

A differential drive is one of the most common drives in robotics that drives a robot by rotating two wheels at different speeds. This article discusses the features of modeling a robot differential drive in the Gazebo simulator. In particular, the key challenges that designers face when modeling differential drive, such as motion control and wheel inertia modeling, will be presented, as well as solutions that can be applied to achieve the desired results. The article will also describe how to use ROS (Robot Operating System) to simulate differential drive in Gazebo. ROS provides tools for controlling a robot in a simulation and provides a convenient way to exchange data between the components of a robot system. Finally, several examples will be presented showing how Gazebo can be used to simulate various types of differential drive robots, including wheeled robots and mobile robots with wide tracks. These examples demonstrate how proper simulation of a differential drive can be useful for developing and testing robot control algorithms in simulation, which can greatly speed up the development process and reduce the cost of experimental work. © 2023 IEEE.

关键词： Robot Operating System

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Detection of Flying Objects Using the YOLOv4 Convolutional Neural Network 6th

Detection of Flying Objects Using the YOLOv4 Convolutional N...

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6th International conference on Software Testing, Machine Learning and Complex Process Analysis, TMPA 2021

作者： Tkachev, Semen Markov, Nikolay School of Computer Science and Robotics Tomsk Polytechnic University 30 Lenin Avenue Tomsk634050 Russia

ISBN: (纸本)9783031504228

The efficiency of the YOLOv4 convolutional neural network (CNN) in detection of objects moving in airspace is investigated. Video materials of two classes of flying objects (FO) were used as the initial data for training and testing of the CNN: helicopter-type unmanned aerial vehicles and gliders. Video materials were obtained in the optical and infrared (IR) wavelength ranges. Datasets are formed from them in the form of a set of images. Large-scale studies of the detection efficiency of the YOLOv4 CNN on such images have been conducted. It is shown that the accuracy of detection of FO in optical images is higher than in images obtained in the IR wavelength range. © 2024, Springer Nature Switzerland AG.

关键词： Convolution

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Federated Learning in Competitive EV Charging Market

Federated Learning in Competitive EV Charging Market

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Sun, Chenxi Huang, Chao Shou, Biying Huang, Jianwei Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Department of Computer Science University of California Davis United States Department of Management Science City University of Hong Kong Hong Kong Hong Kong Shenzhen China

ISBN: (纸本)9798350396782

Federated Learning (FL) has demonstrated a significant potential to improve the quality of service (QoS) of EV charging stations. While existing studies have primarily focused on developing FL algorithms, the effect of FL on the charging stations’ operation in terms of price competition has yet to be fully understood. This paper aims to fill this gap by modeling the strategic interactions between two charging stations and EV owners as a multi-stage game. Each station first decides its FL participation strategy and charging price, and then individual EV owners decide their charging strategies. The game analysis involves solving a non-concave problem and by decomposing it into a piece-wise concave program we manage to fully characterize the equilibrium. Based on real-world datasets, our numerical results reveal an interesting insight: even if FL improves QoS, it can lead to smaller profits for both stations. The key reason is that FL intensifies the price competition between charging stations by improving stations’ QoS to a similar level. We further show that the stations will participate in FL when their data distributions are mildly dissimilar. © 2023 IEEE.

关键词： charging stations competitive pricing Electric vehicles (EV) federated learning (FL) game theory

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