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Inaugural IEEE-IES Digital EcoSystems and Technologies Conference, DEST

作者： Joon-Woo Lee Dong-Hyun Lee Ju-Jang Lee Department of Electrical Engineering KAIST Daejeon South Korea Robotics Program KAIST Daejeon South Korea

We proposed the Heterogeneous Ant Colony Optimization (HACO) algorithm to solve the global path planning problem for autonomous mobile robot in the previous paper. The HACO algorithm was modified and optimized to solve the global path planning problem unlike the conventional ACO algorithm which was proposed to solve the Traveling Salesman Problem (TSP) or Quadratic Assignment Problem (QAP). However, there is a common shortcoming in the ACO algorithms for global path planning, including HACO algorithm. Ants carry out the exploration task relatively well around the starting point. On the other hand, they are hindered in their work as they approached the goal point, because they are attracted by the intensity of heuristic value and the accumulated pheromone while the ACO algorithm works. As a result, they have a strong tendency not to explore and most of them follow the path that found in the beginning of the search. This could cause the local optimal solutions. Thus, we propose a way to solve this problem in this paper. It is the Bilateral Cooperative Exploration (BCE) method. The BCE is the idea that performs the search task again by changing the goal point into the starting point and vice versa. The simulation shows the effectiveness of the proposed method.

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Learning to Estimate Centers of Mass of Arbitrary Objects

Learning to Estimate Centers of Mass of Arbitrary Objects

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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Sean McGovern Huitan Mao Jing Xiao Robotics Engineering Program Worcester Polytechnic Institute Department of Computer Science University of North Carolina at Charlotte

This paper introduces a reinforcement learning algorithm with robot manipulation to learn an arbitrary object's center of mass whose physical material composition is unknown. Robot learning is through manipulation of the object in a sequence of actions. The effectiveness of the algorithm is demonstrated in simulation to locate the centers of mass of rocks with complex shapes, with even or uneven mass distributions, and confirmed by vertically stacking the rocks along their learned centers of mass both in simulation and in real experiments.

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Online support vector regression based actor-critic method

Online support vector regression based actor-critic method

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Annual Conference of Industrial Electronics Society

作者： Dong-Hyun Lee Jeong-Jung Kim Ju-Jang Lee Robotics Program KAIST Daejeon South Korea Department of Electrical Engineering KAIST Daejeon South Korea

This paper proposes a new algorithm for actor-critic method using online support vector regression(SVR), which can do incremental learning and automatically track variation of environment with time-varying characteristics. It gives good generalization properties to value function approximation and helps the critic converge fast. In addition, sample vectors in data set of the online SVR are used as center positions of actor's basis functions. Actor updates policy parameters with those functions using policy gradient algorithm. Throughout simulations, the feasibility and usefulness of the proposed method is demonstrated by comparison with other methods.

关键词： Support vector machines Function approximation Learning Mathematical model Equations Approximation algorithms

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Hole filled 3D map using mobile robots in the urban environment

Hole filled 3D map using mobile robots in the urban environm...

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International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

作者： Sang Un Park Inwook Shim Myung Jin Chung Department of Electrical Engineering KAIST Daejeon South Korea Robotics Program KAIST Daejeon South Korea

ISBN: (纸本)9781457707223

3D mapping and expression of the environment are very important with various applications in many different fields. Among the various sensors (vision sensor, infrared sensor, and LiDAR (Light Detection and Ranging) sensor), we used the LiDAR sensor in this paper because its accuracy is good and easy to handle. There exist holes in the result of 3D reconstruction. When these holes are filled, improved results of localization, and path planning are expected. We propose a contour based sampling algorithm to fill holes. It uses the information from regions near the holes for the prediction, and the results confirm that the filled regions look similar to the real environment.

关键词： Filling Three dimensional displays Laser radar Robot sensing systems Vehicles

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Hierarchical on-line boosting based background subtraction

Hierarchical on-line boosting based background subtraction

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Korea-Japan Joint Workshop on Frontiers of Computer Vision

作者： Yongcheol Lee Jiyoung Jung In-So Kweon Robotics Program KAIST Daejeon South Korea Department of Electrical Engineering KAIST Daejeon South Korea

ISBN: (纸本)9781612846774

This paper presents a real-time background subtraction method which handles illumination changes and dynamic backgrounds such as flapping flags and waving trees. Previous approaches based on Gaussian mixture models usually generates models pixelwise, which makes it difficult to operate in realtime due to computational complexity. Moreover, pixelwise models tend to fail in sudden illumination changes or in dynamic backgrounds. In order to solve this problem, we propose an on-line boosting based background subtraction algorithm. Our approach divides the background area into overlapping patches instead of pixels, and learn classifiers with those patches. The main contribution of this paper is to propose a novel training process for classifiers which use block based Opponent Color Local Binary Pattern (OCLBP). Experimental results show that in environments containing illumination changes and/or dynamic backgrounds, our on-line boosting method using block based OCLBP outperforms previous on-line boosting methods or Gaussian mixture model based methods for robust background subtraction.

关键词： Boosting Training Pixel Image color analysis Lighting Computational modeling Heuristic algorithms

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Line as a visual sentence: Context-aware line descriptor for visual Localization

arXiv

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arXiv 2021年

作者： Yoon, Sungho Kim, Ayoung The Robotics Program KAIST Daejeon Korea Republic of The Department of Mechanical Engineering SNU Seoul Korea Republic of

Along with feature points for image matching, line features provide additional constraints to solve visual geometric problems in robotics and computer vision (CV). Although recent convolutional neural network (CNN)-based line descriptors are promising for viewpoint changes or dynamic environments, we claim that the CNN architecture has innate disadvantages to abstract variable line length into the fixed-dimensional descriptor. In this paper, we effectively introduce Line-Transformers dealing with variable lines. Inspired by natural language processing (NLP) tasks where sentences can be understood and abstracted well in neural nets, we view a line segment as a sentence that contains points (words). By attending to well-describable points on a line dynamically, our descriptor performs excellently on variable line length. We also propose line signature networks sharing the line's geometric attributes to neighborhoods. Performing as group descriptors, the networks enhance line descriptors by understanding lines' relative geometries. Finally, we present the proposed line descriptor and matching in a Point and Line Localization (PL-Loc). We show that the visual localization with feature points can be improved using our line features. We validate the proposed method for homography estimation and visual localization. © 2021, CC BY.

关键词： Convolutional neural networks

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PrimA6D: Rotational Primitive Reconstruction for Enhanced and Robust 6D Pose Estimation

arXiv

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arXiv 2020年

作者： Jeon, Myung-Hwan Kim, Ayoung Department of the Robotics Program KAIST Daejeon Korea Republic of Department of Civil and Environmental Engineering KAIST Daejeon Korea Republic of

— In this paper, we introduce a rotational primitive prediction based 6D object pose estimation using a single image as an input. We solve for the 6D object pose of a known object relative to the camera using a single image with occlusion. Many recent state-of-the-art (SOTA) two-step approaches have exploited image keypoints extraction followed by PnP regression for pose estimation. Instead of relying on bounding box or keypoints on the object, we propose to learn orientation-induced primitive so as to achieve the pose estimation accuracy regardless of the object size. We leverage a Variational AutoEncoder (VAE) to learn this underlying primitive and its associated keypoints. The keypoints inferred from the reconstructed primitive image are then used to regress the rotation using PnP. Lastly, we compute the translation in a separate localization module to complete the entire 6D pose estimation. When evaluated over public datasets, the proposed method yields a notable improvement over the LINEMOD, the Occlusion LINEMOD, and the YCB-Video dataset. We further provide a synthetic-only trained case presenting comparable performance to the existing methods which require real images in the training phase. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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Real-time isometric pinch force prediction from sEMG

Real-time isometric pinch force prediction from sEMG

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IEEE/RAS-EMBS International Conference on Biomedical robotics and Biomechatronics (BioRob)

作者： Changmok Choi Suncheol Kwon Wonil Park Mihye Shin Jung Kim Department of Mechanical Engineering KAIST Daejeon South Korea Robotics Program KAIST Daejeon South Korea

This paper describes a real-time isometric pinch force prediction algorithm using surface electromyogram (sEMG). The activities of seven muscles related to the movements of the thumb and index finger joints, which are observable using surface electrodes, were recorded during pinch force experiments. For the successful implementation of the real-time prediction algorithm, an off-line analysis was performed using the recorded activities. From the seven muscles, four muscles were selected for monitoring using the Fisher linear discriminant paradigm in an off-line analysis, and the recordings from these four muscles provided the most effective information for mapping sEMG to the pinch force. An ANN structure was designed to perform efficient training and to avoid both under-fitting and over-fitting problems. Finally, the pinch force prediction algorithm was tested with five volunteers and the results were evaluated using two criteria: normalized root mean squared error (NRMSE) and correlation (CORR). The training time for the subjects was only 2 min 29 sec, but the prediction results were successful with NRMSE = 0.093 plusmn0.047 and CORR = 0.957 plusmn0.031. These results imply that the proposed algorithm is useful to measure the generated pinch force without force sensors. The possible applications of the proposed method include controlling bionic finger robot systems to overcome finger paralysis or amputation.

关键词： Muscles Prediction algorithms Fingers Force measurement Force sensors Thumb Electrodes Performance analysis Algorithm design and analysis Monitoring

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Development of Detection Lung Disease Stand Alone System Based on Convolutional Neural Network (CNN)

Development of Detection Lung Disease Stand Alone System Bas...

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International Conference of Computer and Informatics engineering (IC2IE)

作者： Edgar Caesar Liewarnata Yosica Mariana Sofyan Tan Suryadiputra Liawatimena Winda Astuti Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Product Design Program Industrial Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Automotive & Robotics Program Computer Engineering Department BINUS ASO School of Engineering Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

Lung disease, especially Tuberculosis (TBC), placed the highest death rate in Indonesia. Tuberculosis (TB) in Indonesia is ranked second after India. Therefore, it is important to reduce or early detection of the lung disease, to prevent this disease and speed up handling. The system can recognize the disease lung identification, and the system applied as standalone system. In this work, the Convolutional Neural Network (CNN) approach for identifying diseases lung identification is proposed. The Mel Frequency Cepstral Coefficient (MFCC) applied to process the stethoscope sounds which will used as input to the CNN. The performance of the proposed system has been investigated and resulted. The accuracy of 99% and 98%, for training and testing accuracy respectively. Furthermore, the system accurately detects lung diseases identification.

关键词： Training Accuracy Tuberculosis Pulmonary diseases Lungs Stethoscope Recording Convolutional neural networks Mel frequency cepstral coefficient Testing

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Microengineered platforms for characterizing the contractile function of in vitro cardiac models

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Microsystems & Nanoengineering 2022年第1期8卷 29-50页

作者： Wenkun Dou Manpreet Malhi Qili Zhao Li Wang Zongjie Huang Junhui Law Na Liu Craig A.Simmons Jason T.Maynes Yu Sun Department of Mechanical and Industrial Engineering University of TorontoTorontoON M5S 3G8Canada Program in Molecular Medicine The Hospital for Sick ChildrenTorontoON M5G 1X8Canada Department of Biochemistry University of TorontoTorontoON M5S 1A8Canada Institute of Robotics and Automatic Information System and the Tianjin Key Laboratory of Intelligent Robotics Nankai UniversityTianjin 300350China School of Mechanical&Automotive Engineering Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China School of Mechatronics Engineering and Automation Shanghai UniversityShanghai 200444China Institute of Biomedical Engineering University of TorontoTorontoON M5S 3G9Canada Translational Biology&Engineering Program Ted Rogers Centre for Heart ResearchTorontoON M5G 1M1Canada Department of Anesthesiology and Pain Medicine University of TorontoTorontoON M5S 1A8Canada Department of Anesthesia and Pain Medicine The Hospital for Sick ChildrenTorontoON M5G 1X8Canada Department of Electrical and Computer Engineering University of TorontoTorontoON M5S 3G4Canada Department of Computer Science University of TorontoTorontoON M5T 3A1Canada

Emerging heart-on-a-chip platforms are promising approaches to establish cardiac cell/tissue models in vitro for research on cardiac physiology,disease modeling and drug cardiotoxicity as well as for therapeutic *** still exist in obtaining the complete capability of in situ sensing to fully evaluate the complex functional properties of cardiac cell/tissue *** to contractile strength(contractility)and beating regularity(rhythm)are particularly important to generate accurate,predictive *** new platforms and technologies to assess the contractile functions of in vitro cardiac models is essential to provide information on cell/tissue physiologies,drug-induced inotropic responses,and the mechanisms of cardiac *** this review,we discuss recent advances in biosensing platforms for the measurement of contractile functions of in vitro cardiac models,including single cardiomyocytes,2D monolayers of cardiomyocytes,and 3D cardiac *** characteristics and performance of current platforms are reviewed in terms of sensing principles,measured parameters,performance,cell sources,cell/tissue model configurations,advantages,and *** addition,we highlight applications of these platforms and relevant discoveries in fundamental investigations,drug testing,and disease ***,challenges and future outlooks of heart-on-a-chip platforms for in vitro measurement of cardiac functional properties are discussed.

关键词： cardiac contractile regularity

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