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An Inertial Measurement Method of Ship Deformation Angle Under the Condition of Uncertain Model Parameters

An Inertial Measurement Method of Ship Deformation Angle Und...

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international conference on control, automation, robotics and vision (ICARCV)

作者： Yuhuai Wang Jiazhou He Taotao Wu Jin An Jiangsu Automation Research Institute Lianyungang China

the matching filtering method based on the inertial measurement unit has become the development trend of ship deformation measurement. In the matching filtering method, the Kalman filter used to estimate the deformation angle requires certain dynamic deformation model parameters. Traditionally, these dynamic deformation parameters are empirically set according to previous experience or determined from previously collected experimental data, and all the parameters in the filter are certain. However, the actual deformation model parameters are changing and uncertain, and the Kalman filter will perform poorly when the current application environment is different from that utilized in the filter design. To overcome this difficulty, an inertial deformation measurement method under the condition of uncertain model parameters is proposed in this paper. Specifically, the interactive multi-model(IMM) Kalman filter method is introduced to solve the uncertainty problem of model parameters. According to our simulation results, the proposed method can estimate the ship deformation angle with a fast convergence and a high degree of accuracy, To be specific, the accuracy of the IMM-based method is improved by 5%~10% in comparison with traditional single model KF method. Besides, a further advantage of the proposed method is that it enhances the adaptability of the deformation measurement system to the external environment changes.

关键词： Strain Marine vehicles Deformable models Sea measurements Kalman filters Angular velocity Velocity measurement

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Manifold ranking-based kernel propagation for saliency estimation

Manifold ranking-based kernel propagation for saliency estim...

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international conference on control, automation and robotics (ICCAR)

作者： Meng Jian Lifang Wu Xiangyin Zhang Yonghao He Faculty of Information Technology Beijing University of Technology Beijing China

Saliency estimation becomes a hot research topic due to its wide and successful application in almost all vision related problems. However, it is still far from satisfactory in saliency estimation techniques due to the complex visual content and various requirements. In this paper, we propose a manifold ranking based kernel propagation (MRKP) approach for visual saliency estimation. MRKP begins to work on background seeds for manifold ranking on four image boundaries individually and select representative salient seeds. Pairwise constraints of must-link and cannot-link are formed with the boundary background seeds and selected salient seeds. then, pairwise constraints guided saliency seed kernel learning and saliency kernel propagation are sequentially conducted in MRKP to estimate visual saliency of images. Experimental results demonstrate that the proposed MRKP has a good ability of learning discriminative kernel structure for saliency estimation.

关键词： Estimation Kernel Visualization Manifolds Principal component analysis Databases Computer vision

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Application of Improved GSA Algorithm and Time Series Method in Bad Data Identification in Power System

Application of Improved GSA Algorithm and Time Series Method...

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international conference on control, automation, robotics and vision

作者： Keyan Liu Weiguo Lin Yuling Bai Lijuan Hu Yunhua Li Liman Yang Kai Xiong China Electric Power Research Institute Beijing Qinghe China School of Automation Science and Electrical Engineering Beihang University Hai Dian District Beijing China

In the process of load forecasting under normal operation condition, there may be some of bad data in observing data of the power distributing systems, which will affect the reliability and accuracy of the processing result. therefore, to detect and identify these bad data is particularly important. Fuzzy clustering analysis is a common method of bad data detection and recognition in power system, but its extreme sensitivity to the initial cluster center will lead to the inaccuracy of the classification results. In this paper, the power data is excavated on the basis of the hierarchical clustering algorithm, the gap statistical algorithm (GSA) and autoregressive integrated moving average model (ARMA), so as to complete bad data detection and recognition of the power system. In order to verify the correctness and effectiveness of the algorithm, the algorithm program is written in MATLAB, and the simulation analysis is carried out on the basis of massive power data in XIAMEN. the results show that the algorithm can effectively identify and reject bad data in power system, and therefore laying foundation for state estimation and medium and long term load forecasting of the power system.

关键词： Clustering algorithms Load modeling Data models Load forecasting Mathematical model Time series analysis

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Camera Based Decision Making at Roundabouts for Autonomous Vehicles

Camera Based Decision Making at Roundabouts for Autonomous V...

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international conference on control, automation, robotics and vision (ICARCV)

作者： Weichao Wang Qinggang Meng Paul Wai Hing Chung Department of Civil and Environmental Engineering Southern Methodist University Dallas TX USA Singapore-MIT Alliance for Research and Technology Singapore Department of Electrical Engineering Southern Methodist University Dallas TX USA

Being able to join roundabouts correctly is crucial for an autonomous vehicle to maintain not only its own safety but also a normal traffic order for others. In order to know the right time and speed for entering roundabouts, the location, speed and direction of the approaching vehicles need to be taken into consideration. this study investigated the feasibility of leveraging computer vision and machine learning to help autonomous vehicles decide to wait or to enter when reaching roundabouts. A grid-based image processing approach with a single camera at normal roundabouts (GBIPA-SC-NR) is proposed in this paper to characterize traffic situations that can be used for machine learning algorithms to learn the roundabout joining criteria. Video road clips recorded when human drivers reach and then join various roundabouts at different locations were utilised for this learning process, with a selection of four supervised classification algorithms (i.e. the Support Vector Machines, Random Forests, K-Nearest Neighbours, and Decision Tree). the trained classifiers using the proposed approach were evaluated on 507 test videos captured at roundabouts, where the SVM showed the best performance with a 90.28% classification accuracy. this result suggests that the proposed grid-based image processing method can be applied to effectively help autonomous vehicles made the right decision when reaching a roundabout.

关键词： conferences automation Robots

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Flooded Area Segmentation from UAV Images Based on Generative Adversarial Networks

Flooded Area Segmentation from UAV Images Based on Generativ...

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international conference on control, automation, robotics and vision (ICARCV)

作者： Dan Popescu Loretta Ichim Florin Stoican Faculty of Automatic Control and Computer Science University Politehnica of Bucharest Romania Faculty of Automatic Control and Computers University Politehnica of Bucharest Bucharest

the detection, localization and evaluation of small flooded areas can contribute to decrease the economical damages of such disasters. the cheapest and most accurate method is to segment the aerial images taken from UAV. In this paper, we propose a new method for detection of regions of interest, like flooding in rural areas, using Generative Adversarial Networks (GAN) and Graphics Processing Units (GPU). the classical GPU is used to create, by parallel calculation of textural features, extracted from the co-occurrence matrix, the supervised mask of flood segmentation in the images from the learning set. Based on these images and their associated real masks, the weights of the generator and discriminator are established. A set of 40 images were used for the learning phase and another set of 60 images were used for method validation. the results demonstrate that the proposed method provide a high accuracy and robustness, comparing with other papers for flooding evaluation. Even if it is a relative long time to learn the GAN, in the operational phase the time for image segmentation process is very short.

关键词： Image segmentation Gallium nitride Floods Instruction sets Graphics processing units Generative adversarial networks Generators

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Center of Mass based Walking Pattern Generator with Gravity Compensation for Walking control on Bioloid Humanoid Robot

Center of Mass based Walking Pattern Generator with Gravity ...

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international conference on control, automation, robotics and vision (ICARCV)

作者： Riyanto Widyawardana Adiprawita Hilwadi Hindersah Carmadi Machbub School of Electrical Engineering and Informatics Bandung Institute of Technology Indonesia

this research discusses the development of simulation and generator of bipedal robot walking pattern on the sagittal, frontal and transversal planes by setting the trajectory of the sole and the pelvis of the robot with respect to the position of the robot center of mass changes to the biped robot support polygon. the robot center of mass is assumed to lie in the pelvis, and an adjustment is made when the structure of the robot is reconstructed with an additional load. Adjustments made in the form of converting angle of robot torso. the simulation program and the motion generator run out data-driven in the form of simulation data along with the correction data which is the gravity compensation for the reconstructed humanoid Bioloid robot. Data-driven becomes input for RoboPlus program from Robotis to adjust the angular position of each joint, joint rotation speed, robot posture, and static robot balance control. With the data-driven given, the Bioloid robot can achieve walking motion without falling on a flat and even floor without the overlap of a swinging foot to the stance foot, and without significantly affecting the step length and the step height of the initial planning. the robot reaches a speed of 0.423 m/s.

关键词： Legged locomotion Generators Foot Mathematical model Torso Trajectory

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Spike-inspired Deep Neural Network Design Using Binary Weight

Spike-inspired Deep Neural Network Design Using Binary Weigh...

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international conference on control, automation and Systems ( ICCAS)

作者： Seunghee Lee Kyukwang Kim Jinki Kim Yeeun Kim Hyun Myung Department of Civil and Environmental Engineering Korean Advanced Institute for Science and Technology Daejeon Korea Robotics Program Korean Advanced Institute for Science and Technology Daejeon Korea

ISBN: (纸本)9781538670804;9788993215168

Recently, deep learning has achieved great results in many fields such as image classification. However, most general artificial neural networks with deep learning require graphics processing unit (GPUs) because of hard workload and it requires a large amount of power consumption. When we think about humans, we can do a lot of things without consuming a lot of power compared to computers or electric appliances. Human beings or organisms transmit and recognize information through signal transmission between neurons. this study aims to develop a novel deep neural network architecture which simulates the signaling system between biological neurons, unlike conventional neural networks. We propose a novel spike-inspired deep neural network structure with the spike-inspired block using binary weight motivated by spike's on and off mechanism. We have also designed a modified DenseNet architecture consisting of spike-inspired blocks. Our proposed method was tested and validated with MNIST datasets. the obtained results show the potential of a spike-inspired deep neural network.

关键词： Biological neural networks Computer architecture Training Power demand Neurons

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Maximum Contrast Imaging Using Chromatic Lighting and Fuzzy Logic: Analyses of Old Handwritings

Maximum Contrast Imaging Using Chromatic Lighting and Fuzzy ...

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international conference on control, automation and Systems ( ICCAS)

作者： Hyungtae Kim Namkyeong Kim Kyeongnan Nam Kwon-Hee Nam Smart Manufacturing Technology Group KITECH CheonAn ChungNam Korea Department of Korean Language and Literature Deagu Catholic University Gyeongsan Kyungpook Korea Department of Library and Information Science Kyungpook National University Daegu Korea

ISBN: (纸本)9781538670804;9788993215168

Machine vision has been applied to many industrial areas. It has also become popular in heritage science. this study proposes maximum contrast imaging to determine the appropriate light color and intensity to increase the discriminating ability for image analyses. the contrast of an image was evaluated in accordance to various digital focus indices (DFIs), and their formulations were thus investigated. Optimal conditions of focus, light color, and intensity were determined based on the maxima of the DFIs. Fuzzy logic was applied to verify the determined owing to the different DFI maxima. the proposed method was implemented subject to the assumption of an arbitrary number of light colors. In the experiment, the DFIs were constructed using C/C++ libraries compatible with OpenCV and the decision methods were verified using an automated optical inspection (AOI) machine. Test targets were old handwritings in a xylograph book, and optimal conditions were determined in the case of a multi-color light source. the handwritings were initially illegible but became readable using maximum contrast images. this study showed that maximum contrast imaging will be useful in industrial applications, such as automatic optical inspection, and in the analyses of historical objects.

关键词： Image color analysis Cameras Lighting Fuzzy logic Optical imaging Machine vision

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A Preliminary Evaluation of Static Tests to Detect Slip Using Tyre Strain Sensing System in Robotic Platforms

A Preliminary Evaluation of Static Tests to Detect Slip Usin...

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international conference on control, automation, robotics and vision

作者： Ali Farooq Lutfi Lutfi Tobias Low Andrew Maxwell Control and Systems Engineering Department University of Technology Baghdad Iraq Faculty of Health Engineering and Sciences University of Southern Queensland Toowoomba Australia

Wheel slip estimation is a determinant factor in improving performance of wheeled mobile robots. Conventional wheel slip estimation methods relied upon sensors which are far from the tyre-ground interface;hence, they required the use of approximated models and complex transformations. A modern method which directly measures tyre-ground contact quantities utilising in-tyre sensors helps develop accurate and direct wheel slip estimation techniques. In this paper, an optimum sensing system for small, non-pressurised robotic tyres is presented. Data processing algorithms are proposed to show possible techniques for deploying the readings of this system to estimate tyre forces and slip variables. these algorithms analyse the sensing system's measurements in both longitudinal and lateral directions with respect to the tyre direction of motion. Performance of the system is evaluated with static tests using a custom-built, bench-top rig. In these tests, different angular positions, vertical forces, and horizontal forces were tested. Results of these tests demonstrated that the system is capable of detecting tyre slip with a suffcient degree of consistency and robustness.

关键词： Tires Robot sensing systems Wheels Strain Mobile robots

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Semantic Scene Recognition and Zone Labeling for Mobile Robot Benchmark Datasets based on Category Maps

Semantic Scene Recognition and Zone Labeling for Mobile Robo...

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international conference on control, automation and Systems ( ICCAS)

作者： Ryoma Fukushi Hirokazu Madokoro Kazuhito Sato Department of Machine Intelligence and Systems Engineering Akita Prefectural University Akita Japan

ISBN: (纸本)9781538670804;9788993215168

For this study, we focus on autonomous locomotion based on visual landmarks that recognizes surrounding environments based on saliency characteristics. this paper presents a feature extraction method combined with saliency maps (SMs), histograms of oriented gradients (HOG) features, and accelerated KAZE (AKAZE) descriptors to describe image features as visual landmarks without removing human regions as dynamic objects. As semantic scene recognition, we used a method combined with self-organizing maps (SOMs) based on bag of features for creating codebooks as visual words and counter propagation networks (CPNs) based on topological learning of neighborhood and competition for creating a category maps (CMs) that converts input features into a low dimensional space. We used a mobile robot for obtaining clockwise datasets (CWDs) and counter CW datasets (CCWDs). the experimental obtained results revealed that recognition accuracies (RAs) for CWDs and CCWDs, were, respectively 70.76% for 26 categories and 72.24% for 25 categories. Based on this result as an original ground truth (GT) pattern, we change label patterns (LPs) of five types according to mapping results on CMs for selection.

关键词： Feature extraction Visualization Mobile robots Semantics Cameras Robot vision systems

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