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5th World Robot Conference Symposium on Advanced Robotics and Automation, WRC SARA 2023

作者： Zhou, Linqi Chen, Zhihua Liu, Jun Liu, Zhi Chen, Yumeng Zhang, Liting Nanchang Hangkong University Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Moe Key Lab of Nondestructive Testing Technology Nanchang330063 China Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing100081 China

ISBN: (纸本)9798350307320

Gait planning of quadruped robots plays an important role in achieving less walking, including dynamic and static gait. In this article, a static and dynamic gait control method based on center of gravity stability margin is proposed. Firstly, the robot model and kinematics modeling are introduced. Secondly, the robot's foot static and dynamic gait were planned and the foot trajectory was designed. Finally, two types of gait of the robot were simulated using Vrep simulation software, and the differences in stability and speed between the coordinated gait with speed and stability in the static and dynamic gait of a 12 degree of freedom robot were analyzed, verifying the effectiveness of the gait control method proposed in this paper. © 2023 IEEE.

关键词： Robot programming

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Gait Planning and Motion Control Based on Vrep Simulation for Quadruped Robot

Gait Planning and Motion Control Based on Vrep Simulation fo...

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Linqi Zhou Zhihua Chen Jun Liu Zhi Liu Yumeng Chen Liting Zhang key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition and MOE Key Lab of Nondestructive Testing Technology Nanchang Hangkong University Nanchang China State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing China

Gait planning of quadruped robots plays an important role in achieving less walking, including dynamic and static gait. In this article, a static and dynamic gait control method based on center of gravity stability margin is proposed. Firstly, the robot model and kinematics modeling are introduced. Secondly, the robot’s foot static and dynamic gait were planned and the foot trajectory was designed. Finally, two types of gait of the robot were simulated using Vrep simulation software, and the differences in stability and speed between the coordinated gait with speed and stability in the static and dynamic gait of a 12 degree of freedom robot were analyzed, verifying the effectiveness of the gait control method proposed in this paper.

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An Active Landing Recovery Method for Quadrotor UAV: Localization, Tracking and Buffering Landing

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IFAC-PapersOnLine 2023年第2期56卷 3366-3372页

作者： Yongkang Xu Zhihua Chen Shoukun Wang Junzheng Wang National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology Beijing CO 100081 P.R.China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition and MOE Key Lab of Nondestructive Testing Technology School of Information Engineering Nanchang Hangkong University Nanchang CO 330063 P.R. China

This paper proposes a principle of fully autonomous ground mobile landing recovery of Unmanned Aerial Vehicles (UAV) for the problems of relatively fixed landing point, passive recovery, poor flexibility, and environmental adaptability, which mainly includes localization, landing point tracking, and buffering landing for quadrotor UAV. Firstly, aiming at the problem that it is difficult to accurately obtain the position of a UAV in dynamic mobile landing recovery, a target location method based on Asynchronous Multisensor Information Fusion(AMIF) and servo turntable focus tracking is proposed. Secondly, to achieve fast and high-precision tracking of UAVs, a tracking control strategy of an independently driven landing recovery system and a Stewart six-degree of freedom platform is proposed. Then, to solve the problems of large impact force and center of gravity instability in the landing process of UAV, a stationarity control algorithm based on model prediction and a compliance control algorithm based on adaptive variable impedance are designed to achieve active compliance control while adjusting the position and attitude of the receiving surface in real-time. Finally, a quadrotor unmanned landing and recovery experimental platform is built to verify the feasibility of the ground mobile landing and recovery strategy proposed in this paper and the effectiveness of the control algorithm.

关键词： Quadrotor UAV Mobile Autonomous recovery Target localization Falling point tracking Buffering landing

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MiSiM:Microstructured Fibers' Simulator

MiSiM:Microstructured Fibers' Simulator

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Latin American Electron Devices Conference (LAEDC)

作者： Paola Montero-Sánchez Ricardo Román-Brenes F. Siles Jaime Cascante-Vindas LAFTLA: Laboratorio de Fotónica y Tecnología Láser Aplicada Universidad de Costa Rica San José Costa Rica PRIS-Lab: Pattern Recognition and Intelligent Systems Laboratory Universidad de Costa Rica San José Costa Rica

ISBN: (数字)9781728110448

ISBN: (纸本)9781728110455

In this work we present MISIM: a free and open software tool for the simulation of microstructured fibers, that can be used for the calculation of their dispersion profiles, for its design and posterior manufacturing. The software is based on the numerical model of Saitoh and Koshiba, and it is able to simulate chromatic dispersion, effective index and group index for a given spectral range. MISIM generates output text files as well as line charts of the simulated parameters. As far as we known, it is the only open and free software with a GUI that can visualize its output directly. For the validation of the tool, results from MISIM were compared with those reported in the literature using different methods to calculate their profiles, obtaining, for example effective indexes within one standard deviation of those reported. Even smaller errors (2.61%) were obtained for the chromatic dispersion using our proposed tool.

关键词： Software Indexes Chromatic dispersion Mathematical model Tools Graphical user interfaces

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Edge-Aware Graph Attention Network for Ratio of Edge-User Estimation in Mobile Networks

Edge-Aware Graph Attention Network for Ratio of Edge-User Es...

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International Conference on pattern recognition

作者： Jiehui Deng Sheng Wan Xiang Wang Enmei Tu Xiaolin Huang Jie Yang Chen Gong PCA Lab the Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Hong Kong Polytechnic University Hong Kong SAR China

Estimating the Ratio of Edge-Users (REU) is an important issue in mobile networks, as it helps the subsequent adjustment of loads in different cells. However, existing approaches usually determine the REU manually, which are experience-dependent and labor-intensive, and thus the estimated REU might be imprecise. Considering the inherited graph structure of mobile networks, in this paper, we utilize a graph-based deep learning method for automatic REU estimation, where the practical cells are deemed as nodes and the load switchings among them constitute edges. Concretely, Graph Attention Network (GAT) is employed as the backbone of our method due to its impressive generalizability in dealing with networked data. Nevertheless, conventional GAT cannot make full use of the information in mobile networks, since it only incorporates node features to infer the pairwise importance and conduct graph convolutions, while the edge features that are actually critical in our problem are disregarded. To accommodate this issue, we propose an Edge-Aware Graph Attention Network (EAGAT), which is able to fuse the node features and edge features for REU estimation. Extensive experimental results on two real-world mobile network datasets demonstrate the superiority of our EAGAT approach to several state-of-the-art methods.

关键词： Deep learning Fuses Estimation Switches pattern recognition

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NTIRE 2023 Image Shadow Removal Challenge Report

NTIRE 2023 Image Shadow Removal Challenge Report

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2023 IEEE/CVF Conference on Computer Vision and pattern recognition Workshops, CVPRW 2023

作者： Vasluianu, Florin-Alexandru Seizinger, Tim Timofte, Radu Cui, Shuhao Huang, Junshi Tian, Shuman Fan, Mingyuan Zhang, Jiaqi Zhu, Li Wei, Xiaoming Wei, Xiaolin Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Dong, Xiaoyi Zhang, Xi Sheryl Li, Chenghua Leng, Cong Yeo, Woon-Ha Oh, Wang-Taek Lee, Yeo-Reum Ryu, Han-Cheol Luo, Jinting Jiang, Chengzhi Han, Mingyan Wu, Qi Lin, Wenjie Yu, Lei Li, Xinpeng Jiang, Ting Fan, Haoqiang Liu, Shuaicheng Xu, Shuning Song, Binbin Chen, Xiangyu Zhang, Shile Zhou, Jiantao Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Wang, Bo Ren, Jiahuan Luo, Yan Kondo, Yuki Miyata, Riku Yasue, Fuma Naruki, Taito Ukita, Norimichi Chang, Hua-En Yang, Hao-Hsiang Chen, Yi-Chung Chiang, Yuan-Chun Huang, Zhi-Kai Chen, Wei-Ting Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Xianwei, Li Fu, Huiyuan Liu, Chunlin Ma, Huadong Fu, Binglan He, Huiming Wang, Mengjia She, Wenxuan Liu, Yu Nathan, Sabari Kansal, Priya Zhang, Zhongjian Yang, Huabin Wang, Yan Zhang, Yanru Phutke, Shruti S. Kulkarni, Ashutosh Khan, Md Raqib Murala, Subrahmanyam Vipparthi, Santosh Kumar Ye, Heng Liu, Zixi Yang, Xingyi Liu, Songhua Wu, Yinwei Jing, Yongcheng Yu, Qianhao Zheng, Naishan Huang, Jie Long, Yuhang Yao, Mingde Zhao, Feng Zhao, Bowen Ye, Nan Shen, Ning Cao, Yanpeng Xiong, Tong Xia, Weiran Li, Dingwen Xia, Shuchen Computer Vision Lab Ifi Caidas University of Würzburg Germany Computer Vision Lab Eth Zürich Switzerland Meituan Group China Department of Information Technology Uppsala University Sweden Institute of Automation Chinese Academy of Sciences Beijing China Nanjing China Maicro Nanjing China Department of Artificial Intelligence Convergence Sahmyook University Seoul Korea Republic of Megvii Technology China University of Electronic Science and Technology of China China University of Macau China China Toyota Technological Institute Japan Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan ServiceNow United States Beijing University of Post and Teleconmunication Beijing China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education China Couger Inc. Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar Punjab Rupnagar India Research Institute Singapore National University of Singapore Singapore Research Institute Singapore University of Sydney Australia Brain-Inspired Vision Laboratory Information Science and Technology Institution University of Science and Technology of China China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province School of Mechanical Engineering Zhejiang University Hangzhou310027 China South China University of Technology China

ISBN: (纸本)9798350302493

This work reviews the results of the NTIRE 2023 Challenge on Image Shadow Removal. The described set of solutions were proposed for a novel dataset, which captures a wide range of object-light interactions. It consists of 1200 roughly pixel aligned pairs of real shadow free and shadow affected images, captured in a controlled environment. The data was captured in a white-box setup, using professional equipment for lights and data acquisition sensors. The challenge had a number of 144 participants registered, out of which 19 teams were compared in the final ranking. The proposed solutions extend the work on shadow removal, improving over the performance level describing state-of-the-art methods. © 2023 IEEE.

关键词： Data acquisition

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Teleoperation of a Humanoid Robot Using an Optical Motion Capture System

Teleoperation of a Humanoid Robot Using an Optical Motion Ca...

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2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018

作者： Nunez, L. Dajles, D. Siles, F. Department of Electrical Engineering School of Engineering Universidad de Costa Rica PRIS-Lab: Pattern Recognition and Intelligent Systems Laboratory San Jose Costa Rica

ISBN: (纸本)9781538675069

Given the resemblance between the human body and that of a humanoid robot, a very intuitive control system might use motion capture;the system would track the motion of different key joints along the body of the human operator and the robot should mimic the action. To mimic the human's actions, a structure that supports and solves the issues related to motion tracking, human-robot figure relation, motion stability, and other issues is needed. This project consist of the development of a teleoperation system, which would allow a NAO humanoid robot to behave as a human motion imitator, using an optical motion capture system. © 2018 IEEE.

关键词： Remote control

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Goniometry-based Glitch-Correction Algorithm for Optical Motion Capture Data

Goniometry-based Glitch-Correction Algorithm for Optical Mot...

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2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018

作者： Castresana, M. Siles, F. Department of Electrical Engineering School of Engineering Universidad de Costa Rica PRIS-Lab: Pattern Recognition and Intelligent Systems San José Costa Rica

ISBN: (纸本)9781538675069

Nowadays , motion capture technology is used in productions of all levels in 3D Animation. The concept on which this technology is based on, consists of the elaboration of 3D models from numerical data taken by a set of sensors (for example infrared cameras) interpreted by a software. The problem with this technology is that the processing of data generated by these sensors is not always accurate, causing loss of positional data which results in errors called glitches, that produce corrupted 3D models. In this work, a goniometry-based algorithm to detect, locate and correct the glitches generated from optical motion capture data is presented. Based on the classification of angular measures of the articular physiology in humans, a pattern recognition approach was used to construct the algorithm. The proposed algorithm produces average F1-scores of 0.956 using synthetical data, and produces natural results in most of the cases for real data. © 2018 IEEE.

关键词： pattern recognition

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M-Phase Feature Extraction Algorithm for Phenotype Classification from Cancer Brightfield Microscopy

M-Phase Feature Extraction Algorithm for Phenotype Classific...

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2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018

作者： Mora-Zuniga, A. Quiros-Barrantes, Steve Siles, F. Department of Electrical Engineering School of Engineering PRIS-Lab: Pattern Recognition and Intelligent Systems Laboratory Costa Rica School of Microbiology Universidad de Costa Rica San Jose Costa Rica

ISBN: (纸本)9781538675069

In this paper a workflow to extract cell features from brightfield microscopy image sequences is proposed. An event driven approach, combined with a forward and backward tracking limited by the cell's circularity was proven enough to extract relevant features that can be used to classify the cells into four phenotypes related to chemosensitivity studies: cell cycle arrest, apoptotic, damage proliferation and cells that have repaired their DNA damage. An average F1-Score greater than 0.7 was achieved in the detection and follow up of the events on images that present characteristics that impede the use of classic image segmentation and methods. © 2018 IEEE.

关键词： Cells

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Actor and Action Modular Network for Text-based Video Segmentation

arXiv

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arXiv 2020年

作者： Yang, Jianhua Huang, Yan Niu, Kai Huang, Linjiang Ma, Zhanyu Wang, Liang The Pattern Recognition and Intelligent Systems Lab. School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China The School of Computer Science Northwestern Polytechnical University Xi’an China The Multimedia Laboratory Chinese University of Hong Kong Hong Kong Beijing China Beijing China

Text-based video segmentation aims to segment an actor in video sequences by specifying the actor and its performing action with a textual query. Previous methods fail to explicitly align the video content with the textual query in a fine-grained manner according to the actor and its action, due to the problem of semantic asymmetry. The semantic asymmetry implies that two modalities contain different amounts of semantic information during the multi-modal fusion process. To alleviate this problem, we propose a novel actor and action modular network that individually localizes the actor and its action in two separate modules. Specifically, we first learn the actor-/action-related content from the video and textual query, and then match them in a symmetrical manner to localize the target tube. The target tube contains the desired actor and action which is then fed into a fully convolutional network to predict segmentation masks of the actor. Our method also establishes the association of objects cross multiple frames with the proposed temporal proposal aggregation mechanism. This enables our method to segment the video effectively and keep the temporal consistency of predictions. The whole model is allowed for joint learning of the actor-action matching and segmentation, as well as achieves the state-of-the-art performance for both single-frame segmentation and full video segmentation on A2D Sentences and J-HMDB Sentences datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantics

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