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作者： Kim, HyungTae Lee, Duk-Yeon Choi, Dongwoon Kang, Jaehyeon Lee, Dong-Wook Digital Transformation R&D Department Korea Institute of Industrial Technology Ansan Gyeonggi15588 Korea Republic of Applied Robot R&D Department Korea Institute of Industrial Technology Ansan Gyeonggi15588 Korea Republic of

A digital focus index (DFI) is a value used to determine image focus in scientific apparatus and smart devices. Automatic focus (AF) is an iterative and time-consuming procedure;however, its processing time can be reduced using a general processing unit (GPU) and a multi-core processor (MCP). In this study, parallel architectures of a minimax search algorithm (MSA) are applied to two DFIs: range algorithm (RA) and image contrast (CT). The DFIs are based on a histogram;however, the parallel computation of the histogram is conventionally inefficient because of the bank conflict in shared memory. The parallel architectures of RA and CT are constructed using parallel reduction for MSA, which is performed through parallel relative rating of the image pixel pairs and halved the rating in every step. The array size is then decreased to one, and the minimax is determined at the final reduction. Kernels for the architectures are constructed using open source software to make it relatively platform independent. The kernels are tested in a hexa-core PC and an embedded device using Lenna images of various sizes based on the resolutions of industrial cameras. The performance of the kernels for the DFIs was investigated in terms of processing speed and computational acceleration;the maximum acceleration was 32.6× in the best case and the MCP exhibited a higher performance. Copyright © 2023 KSII.

关键词： Iterative methods

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Workspace Analysis of Cable-Driven Continuum Manipulator Using Universal Joint

Workspace Analysis of Cable-Driven Continuum Manipulator Usi...

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International Conference on Ubiquitous robots (UR)

作者： Myung-oh Kim Dongwoon Choi Duck-Yeon Lee Dong-Wook Lee Department of Mechanical Engineering Hanynag University Seoul Korea Applied Robot R&D Department Korea Institute of Industrial Technology (KITECH) Ansan Korea

This paper presents a comprehensive study on the workspace of a continuum manipulator using a universal joint. The research focuses on analyzing the effect of different parameters such as joint angles and flexible link lengths on the workspace of the manipulator. A mathematical model is developed to determine the workspace and validate the results through simulations. The study also includes a discussion on the limitations and challenges of using a universal joint in continuum manipulators and ways to overcome them. The findings of this research contribute to the understanding of the workspace of continuum manipulators and can be used to design more effective and efficient manipulator systems.

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Fast Correction and Stitching Algorithm for Fisheye Images

Fast Correction and Stitching Algorithm for Fisheye Images

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Image Processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Qian Li Shantao Song Ruixue Liu Eugene Levin Shandong Provincial Key Laboratory of Robot and Manufacturing Automation Technology Institute of Automation Qilu University of Technology (Shandong Academy of Sciences) Jinan China Department of Biomedical Data Science School of Applied Computational Sciences Meharry Medical College Nashville Tenn USA

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Fisheye cameras, with their ultra-wide-angle field of view, can capture a larger scene in a single shot compared to traditional lenses. This capability makes them highly valuable in areas such as security surveillance, panoramic photography, autonomous driving, and robot navigation. However, building an efficient and accurate visual system is crucial for realizing these applications. This paper proposes an improved algorithm based on fisheye image correction and stitching. First, a fast fisheye image distortion correction method is introduced to eliminate the severe distortion caused by the fisheye lens. Then, the o-FAST algorithm is used for feature point detection, ensuring rotational invariance of the feature points, and the KNN algorithm is employed for initial matching. On this basis, the PROSAC algorithm is applied to remove incorrect matches and compute the homography matrix between images. Finally, seamless image stitching is achieved using a gradual weighted fusion method. The research results demonstrate that this method significantly improves both image quality and computational efficiency, making it effective for generating and processing panoramic images. Experimental results further confirm that the method enhances stitching accuracy while also increasing processing speed, showcasing its high practical value.

关键词： Accuracy Surveillance robot vision systems Visual systems Distortion Feature extraction Cameras Security Image stitching Lenses

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Meal intention recognition system based on gaze direction estimation using deep learning for the user of meal assistant robot

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Journal of Institute of Control, robotics and Systems 2021年第5期27卷 334-341页

作者： Park, Hyunji Jang, Inhoon Ko, Kwangeun Applied Robot R&D Department Korea Institute of Industrial Technology Korea Republic of Department of Computer Science & Engineering Hanyang University Korea Republic of Industry Academic Cooperation Foundation Hankyong National University Korea Republic of

Care robots that provide dietary supplements to the elderly and the disabled should be able to intelligently interact with the users’ intention to eat in real-time and provide the food they want. To this end, based on deep learning, we have developed a user interface that facilitates the selection of food to be eaten according to six user gaze directions. A single-stage object-detection deep learning model has been implemented as a lightweight, single-stage object-detection deep learning model that can be installed on commercial tablets, and the accuracy of this model was 0.9857. © ICROS 2021.

关键词： Human robot interaction

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Faster Optimal Coalition Structure Generation via Offline Coalition Selection and Graph-Based Search

arXiv

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arXiv 2024年

作者： Taguelmimt, Redha Aknine, Samir Boukredera, Djamila Changder, Narayan Sandholm, Tuomas Univ Lyon UCBL CNRS INSA Lyon Centrale Lyon Univ Lyon 2 LIRIS UMR5205 Lyon France Laboratory of Applied Mathematics Faculty of Exact Sciences University of Bejaia Bejaia Algeria TCG Centres for Research and Education in Science and Technology Kolkata India Computer Science Department Carnegie Mellon University Pittsburgh United States Strategy Robot Inc. United States Strategic Machine Inc. United States Optimized Markets Inc. United States

Coalition formation is a key capability in multi-agent systems. An important problem in coalition formation is coalition structure generation: partitioning agents into coalitions to optimize the social welfare. This is a challenging problem that has been the subject of active research for the past three decades. In this paper, we present a novel algorithm, SMART, for the problem based on a hybridization of three innovative techniques. Two of these techniques are based on dynamic programming, where we show a powerful connection between the coalitions selected for evaluation and the performance of the algorithms. These algorithms use offline phases to optimize the choice of coalitions to evaluate. The third one uses branch-and-bound and integer partition graph search to explore the solution space. Our techniques bring a new way of approaching the problem and a new level of precision to the field. In experiments over several common value distributions, we show that the hybridization of these techniques in SMART is faster than the fastest prior algorithms (ODP-IP, BOSS) in generating optimal solutions across all the value distributions. Copyright © 2024, The Authors. All rights reserved.

关键词： Dynamic programming

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Instance segmentation based recognition system tracking tomatoes by ripeness in natural light conditions

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Journal of Institute of Control, robotics and Systems 2020年第11期26卷 940-948页

作者： Lee, Woo-Young Ko, Kwang Eun Kang, Jaehyeon Park, Hyun Ji Jang, Inhoon Applied Robot R&D Department Korea Institute of Industrial Technology Korea Republic of Department of computer Science & Engineering Hanyang University Korea Republic of

This paper describes a tomato recognition system to be applied to a harvesting robot used in farms under natural light conditions. The tomato recognition system is composed of a stereo camera, light, deep learning based instance segmentation model and Kalman filter based object tracking algorithm. In order to train the instance segmentation model, a large-scale dataset of the actual smartfarm background is made, and performance verification is performed on the test dataset. The average precision of the deep learning based instance segmentation model achieved 96.8% of performance when the threshold of the prediction score for classifying ripeness is over 0.75 (Table. 3), and the multiple object tracking accuracy of the Kalman filter based tracking method is approximately 74.3%. © ICROS 2020.

关键词： Kalman filters

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A Multiagent Path Search Algorithm for Large-Scale Coalition Structure Generation

arXiv

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arXiv 2025年

作者： Taguelmimt, Redha Aknine, Samir Boukredera, Djamila Changder, Narayan Sandholm, Tuomas Clermont Auvergne University Clermont Auvergne INP CNRS LIMOS Clermont-FerrandF-63000 France Univ Lyon UCBL CNRS INSA Lyon Centrale Lyon Univ Lyon 2 LIRIS UMR5205 Lyon France Laboratory of Applied Mathematics Faculty of Exact Sciences University of Bejaia Bejaia Algeria TCG Centres for Research and Education in Science and Technology Kolkata India Carnegie Mellon University Computer Science Department Pittsburgh United States Strategy Robot Inc. Strategic Machine Inc. Optimized Markets Inc.

Coalition structure generation (CSG), i.e. the problem of optimally partitioning a set of agents into coalitions to maximize social welfare, is a fundamental computational problem in multiagent systems. This problem is important for many applications where small run times are necessary, including transportation and disaster response. In this paper, we develop SALDAE, a multiagent path finding algorithm for CSG that operates on a graph of coalition structures. Our algorithm utilizes a variety of heuristics and strategies to perform the search and guide it. It is an anytime algorithm that can handle large problems with hundreds and thousands of agents. We show empirically on nine standard value distributions, including disaster response and electric vehicle allocation benchmarks, that our algorithm enables a rapid finding of high-quality solutions and compares favorably with other state-of-the-art *** Codes 93A16, 68T01 Copyright © 2025, The Authors. All rights reserved.

关键词： Heuristic algorithms

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A new calibration method and experimental study for kinematic parameters of industrial robot

A new calibration method and experimental study for kinemati...

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2020 International Conference on Artificial Intelligence and Electromechanical Automation, AIEA 2020

作者： Zhao, Honghua Zhao, Jian Lin, Yifei Han, Qifei Chen, Mianpeng University of Jinan School of Mechanical Engineering Jinan China Shandong University School of Control Science and Engineering Jinan China Shandong Institute of Intelligent Robot and Applied Technology Jining China GSK CNC Equipment Co. Ltd. CNC System R and D Department Guangzhou China

ISBN: (纸本)9781728182889

The accuracy of industrial robot kinematics parameters directly affects its absolute positioning accuracy. Based on Modify Denavit-Hartenberg (MD-H) kinematics theory, a more accurate calibration method for kinematic parameters of industrial robots is proposed in this paper. It takes the kinematics parameters, deceleration ratio and coupling coefficient of the robot as error parameters to establish the error model. The calibration experiment and accuracy verification experiment are designed by laser tracker. After the introduction of the reduction ratio and the coupling coefficient calibration, the absolute positioning error of the robot in all directions has been greatly improved, and the overall average accuracy of the robot is increased by 91.42% compared with the uncalibrated, and is increased by 81.02% compared with the without introducing the deceleration ratio and coupling coefficient. © 2020 IEEE.

关键词： Kinematics

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LimeSoDa: A Dataset Collection for Benchmarking of Machine Learning Regressors in Digital Soil Mapping

arXiv

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arXiv 2025年

作者： Schmidinger, Jonas Vogel, Sebastian Barkov, Viacheslav Pham, Anh-Duy Gebbers, Robin Tavakoli, Hamed Correa, Jose Tavares, Tiago R. Filippi, Patrick Jones, Edward J. Lukas, Vojtech Boenecke, Eric Ruehlmann, Joerg Schroeter, Ingmar Kramer, Eckart Paetzold, Stefan Kodaira, Masakazu Wadoux, Alexandre M.J.-C. Bragazza, Luca Metzger, Konrad Huang, Jingyi Valente, Domingos S.M. Safanelli, Jose L. Bottega, Eduardo L. Dalmolin, Ricardo S.D. Farkas, Csilla Steiger, Alexander Horst, Taciara Z. Ramirez-Lopez, Leonardo Scholten, Thomas Stumpf, Felix Rosso, Pablo Costa, Marcelo M. Zandonadi, Rodrigo S. Wetterlind, Johanna Atzmueller, Martin Osnabrück University Joint Lab Artificial Intelligence and Data Science Osnabrück Germany Department of Agromechatronics Potsdam Germany Piracicaba Brazil The University of Sydney Sydney Institute of Agriculture Sydney Australia Mendel University in Brno Department of Agrosystems and Bioclimatology Brno Czech Republic Leibniz Institute of Vegetable and Ornamental Crops Next Generation Horticultural Systems Grossbeeren Germany Eberswalde University for Sustainable Development Landscape Management and Nature Conservation Eberswalde Germany Soil Science and Soil Ecology Bonn Germany Tokyo University of Agriculture and Technology Institute of Agriculture Tokyo Japan LISAH Univ. Montpellier AgroParisTech INRAE IRD L'Institut Agro Montpellier France Agroscope Field-Crop Systems and Plant Nutrition Nyon Switzerland University of Wisconsin-Madison Department of Soil Science Madison United States Federal University of Viçosa Department of Agricultural Engineering Viçosa Brazil Woodwell Climate Research Center Falmouth United States Academic Coordination Santa Maria Brazil Soil Department Santa Maria Brazil Division of Environment and Natural Resources Aas Norway University of Rostock Chair of Geodesy and Geoinformatics Rostock Germany Federal Technological University of Paraná Dois Vizinhos Brazil BÜCHI Labortechnik AG Data Science Department Flawil Switzerland Imperial College London Imperial College Business School London United Kingdom University of Tübingen Department of Geosciences Tübingen Germany University of Tübingen DFG Cluster of Excellence Machine Learning for Science’ Germany Bern University of Applied Sciences Competence Center for Soils Zollikofen Switzerland Simulation and Data Science Müncheberg Germany Federal University of Jataí Institute of Agricultural Sciences Jatai Brazil Federal University of Mato Grosso Instute of Agricultural and Environmental Scinces Sinop Brazil Department of Soil and Environment Skara

Digital soil mapping (DSM) relies on a broad pool of statistical methods, yet determining the optimal method for a given context remains challenging and contentious. Benchmarking studies on multiple datasets are needed to reveal strengths and limitations of commonly used methods. Existing DSM studies usually rely on a single dataset with restricted access, leading to incomplete and potentially misleading conclusions. To address these issues, we introduce an open-access dataset collection called Precision Liming Soil Datasets (LimeSoDa). LimeSoDa consists of 31 field- and farm-scale datasets from various countries. Each dataset has three target soil properties: (1) soil organic matter or soil organic carbon, (2) clay content and (3) pH, alongside a set of features. Features are dataset-specific and were obtained by optical spectroscopy, proximal- and remote soil sensing. All datasets were aligned to a tabular format and are ready-to-use for modeling. We demonstrated the use of LimeSoDa for benchmarking by comparing the predictive performance of four learning algorithms across all datasets. This comparison included multiple linear regression (MLR), support vector regression (SVR), categorical boosting (CatBoost) and random forest (RF). The results showed that although no single algorithm was universally superior, certain algorithms performed better in specific contexts. MLR and SVR performed better on high-dimensional spectral datasets, likely due to better compatibility with principal components. In contrast, CatBoost and RF exhibited considerably better performances when applied to datasets with a moderate number ( © 2025, CC BY-SA.

关键词： Adversarial machine learning

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A New Calibration Method and Experimental Study for Kinematic Parameters of Industrial robot

A New Calibration Method and Experimental Study for Kinemati...

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Artificial Intelligence and Electromechanical Automation (AIEA), International Conference on

作者： Honghua Zhao Jian Zhao Yifei Lin Qifei Han Mianpeng Chen University of Jinan School of Mechanical Engineering Jinan China Shandong University School of Control Science and Engineering Jinan China Shandong Institute of Intelligent Robot & Applied Technology Jining China CNC System R&D Department GSK CNC Equipment Co. Ltd. Guangzhou China

ISBN: (数字)9781728182889

ISBN: (纸本)9781728182896

关键词： component industrial robot Denavit-Hartenberg kinematics error model calibration experiment accuracy verification experiment industrial robots error model CALIBRATION METHODS robot kinematics deceleration rate absolute location laser trackers

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