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arXiv 2021年

作者： Wang, Guangming Ren, Shuaiqi Wang, Hesheng The Key Laboratory of System Control and Information Processing of Ministry of Education Department of Automation Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China The Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China

Optical flow estimation is a fundamental problem of computer vision and has many applications in the fields of robot learning and autonomous driving. This paper reveals novel geometric laws of optical flow based on the insight and detailed definition of non-occlusion. Then, two novel loss functions are proposed for the unsupervised learning of optical flow based on the geometric laws of non-occlusion. Specifically, after the occlusion part of the images are masked, the flowing process of pixels is carefully considered and geometric constraints are conducted based on the geometric laws of optical flow. First, neighboring pixels in the first frame will not intersect during the pixel displacement to the second frame. Secondly, when the cluster containing adjacent four pixels in the first frame moves to the second frame, no other pixels will flow into the quadrilateral formed by them. According to the two geometrical constraints, the optical flow non-intersection loss and the optical flow non-blocking loss in the non-occlusion regions are proposed. Two loss functions punish the irregular and inexact optical flows in the non-occlusion regions. The experiments on datasets demonstrated that the proposed unsupervised losses of optical flow based on the geometric laws in non-occlusion regions make the estimated optical flow more refined in detail, and improve the performance of unsupervised learning of optical flow. In addition, the experiments training on synthetic data and evaluating on real data show that the generalization ability of optical flow network is improved by our proposed unsupervised approach. Copyright © 2021, The Authors. All rights reserved.

关键词： Computer vision

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Towards Collision Detection, Localization and Force Estimation for a Soft Cable-driven Robot Manipulator

Towards Collision Detection, Localization and Force Estimati...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Yuxin Wang Hesheng Wang Fan Xu Junzhi Yu Weidong Chen Yun-Hui Liu Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Institute of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai China College of Engineering Peking University Beijing China The Chinese University of Hong Kong Hong Kong

Soft robots have been applied widely to various constrained scenarios due to the advantages over traditional rigid manipulators such as softness, deformability and adaptability to constrained surroundings. To make full use of this merit, this paper proposes a method that integrates collision detection, localization and force estimation for a cable-driven soft manipulator without any prior geometrical knowledge of its surroundings. First of all, a collision detection algorithm is presented based upon Cosserat-rod statics by a threshold method through using the cable tension and the shape information, which are obtained by the load cells and the Vicon system, respectively. Secondly, a collision localization and force estimation method is proposed through optimizing the discrepancy between the actual and the theoretical shapes. Finally, experiments are carried out to validate these algorithms. The experimental results demonstrate that the site, the magnitude as well as the direction can be estimated.

关键词： Location awareness Shape Conferences Force Estimation Soft robotics Manipulators

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Research on fault feature extraction of rotating machinery based on adaptive VMD and optimized CYCBD

Research on fault feature extraction of rotating machinery b...

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Chinese control and Decision Conference, CCDC

作者： Ran Xie Ling Xiong Binbin Dan Zeyu Ren Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan China Key Laboratory of Metallurgical Equipment and Control of Ministry of Education Wuhan China School of Physics and Electronic Science Hunan Institute of Science and Technology Yueyang China

ISBN: (纸本)9781665478977

To solve the problem that early fault features of rotating machinery are difficult to extract, an adaptive k-value hierarchical variational mode decomposition (H-VMD) combined with optimized maximum second-order cyclostationarity blind deconvolution (CYCBD) fault feature extraction method is proposed in this paper. Mode decomposition of the vibration signal is performed with H-VMD. and then the noise dominant component is denoised by wavelet threshold denoising (WTD). Furthermore, the improved autocorrelation-weighted correlated kurtosis (ACK) when CYCBD enhances the periodic shock component of the denoised signal is the fitness function of ChOA, and the envelope demodulation analysis of the feature-enhanced signal is performed using the teager energy operator (TEO). Simulation analysis and experimental results show that the interference of background noise can be effectively removed and the periodic shock component of the vibration signal be enhanced by the proposed method, which is a new feature extraction method for the fault diagnosis of rotating machinery.

关键词： Vibrations Fault diagnosis Electric shock Noise reduction Interference Feature extraction Linear programming

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Multimodal Information-Based Broad and Deep Learning Model for Emotion Understanding

Multimodal Information-Based Broad and Deep Learning Model f...

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第40届中国控制会议

作者： Min Li Luefeng Chen Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Department of Electrical and Computer Engineering University of Alberta Tokyo Institute of Technology

Multimodal information-based broad and deep learning model（MIBDL） for emotion understanding is proposed, in which facial expression and body gesture are used to achieve emotional states recognition for emotion understanding. It aims to understand coexistence multimodal information in human-robot interaction by using different processing methods of deep network and broad network, which obtains the features of depth and width dimensions. Moreover, random mapping in the initial broad learning network could cause information loss and its shallow layer network is difficult to cope with complex tasks. To address this problem, we use principal component analysis to generate the nodes of the broad learning, and the stacked broad learning network is adapted to make it easier for the existing broad learning networks to cope with complex tasks by creating deep variations of the existing network. To verify the effectiveness of the proposal, experiments completed on benchmark database of spontaneous emotion expressions are developed, and experimental results show that the proposal outperforms the state-of-theart methods. According to the simulation experiments on the FABO database, by using the proposed method, the multimodal recognition rate is 17,54%, 1.24%, and 0.23% higher than those of the temporal normalized motion and appearance features（TN）,the multi-channel CNN（MCCNN）, and the hierarchical classification fusion strategy（HCFS）, respectively.

关键词： Convolution neural network broad learning emotion understanding facial expression body gesture

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Effective Throughput Optimization of SAG Milling Process Based on BPNN and Genetic Algorithm

Effective Throughput Optimization of SAG Milling Process Bas...

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Industrial Cyber-Physical systems (ICPS)

作者： Zhenhong Liao Ce Xu Wen Chen Qifu Chen Feng Wang Jinhua She School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Department of Mineral Resources Development and Utilization Changsha Research Institute of Mining and Metallurgy CO. LTD. Chagnsha China Hunan Valin Lianyuan Iron And Steel Co. Ltd. Loudi China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

Grinding is an energy-consuming process in mineral processing industry. Improving grinding processing capacity per unit power consumption is an effective means to reduce grinding production cost. In this paper, a new index for evaluating the effective processing throughput of SAG milling is proposed. The production process model is established by BP neural network (BPNN). Through combining the process mechanism and production constraints, the genetic algorithm is adopted to optimize the operating parameters of the SAG milling process to maximize the effective throughput, thus improving the grinding efficiency. The experimental results showed that through optimization of effective throughput function proposed in this paper, the SAG mill processing capacity has been increased by 4% and the operating power drawn reduced by 1.12%. It has important guiding significance for the actual production process.

关键词： Industries Power demand Neural networks Milling Throughput Minerals Materials processing

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Hair Direction Detection*

Hair Direction Detection*

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WRC Symposium on advanced robotics and Automation (WRC SARA)

作者： Peng Ba Pengyi Wang Hongde Wu Qian Yang Yongqiang Feng Junchen Wang Yida David Hu Changsheng Li Wenyong Liu Shaolong Kuang Bai-Quan Su Medical Robotics Laboratory School of Intelligent Engineering and Automation Beijing University of Posts and Telecommunications Beijing China Plastic Surgery Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China School of Mechanical Engineering and Automation Beihang University Beijing China Brigham and Women’s Hospital Harvard Medical School Boston MA USA School of Mechatronical Engineering Beijing Institute of Technology Beijing China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China Key Laboratory for Biomechanics and Mechanobiology of Chinese Education Ministry Beijing Advanced Innovation Centre for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing China School of Health Science and Environmental Engineering Shenzhen University of Technology Shenzhen China

ISBN: (数字)9798331506100

ISBN: (纸本)9798331506117

Hair direction is an important external feature of hair, and recognising hair direction is a prerequisite for processing hair. In this paper, a new algorithm is proposed and systematically verified experimentally for the problem of recognising hair direction. The main goal of this paper is to develop an algorithm that can identify hair direction in a complex image environment. A curve segment analysis method based on image skeletonisation is adopted, which is based on skeleton extraction, intersection identification, curve segmentation and direction prediction. In addition, this paper combines the technique of non-maximal value suppression and PCA analysis to improve the accuracy and stability of the estimation. In the experimental design, this paper chooses a representative image dataset to verify the effectiveness of this paper's algorithm. The experimental process includes the steps of image preprocessing, skeletonisation processing, intersection detection and merging, and direction prediction. The experimental results show that the method in this paper can accurately and effectively identify the hair direction. The main contribution of this paper is to propose a new hair direction recognition method and experimentally verify its effectiveness and accuracy in complex backgrounds.

关键词： Hair Image segmentation Accuracy Three-dimensional displays Merging Prediction algorithms Stability analysis

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Brain-inspired feedback for spatial frequency aware artificial networks

Brain-inspired feedback for spatial frequency aware artifici...

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Asilomar Conference on Signals, systems & Computers

作者： Ramin Toosi Mohammad Ali Akhaee Mohammad-Reza A. Dehaqani School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Cognitive Systems Laboratory Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran School of Cognitive Sciences Institute for Research in Fundamental Sciences (IPM) Tehran Iran

Spatial frequency (SF) is a characteristic of an image that could dissociate course and fine shape information. Physiological and psychophysical studies widely investigated the role of various SF contents in image processing. Inspired by the primate brain structure, deep neural networks improved various computer vision tasks such as image classification. Physiological studies show that low SF (LSF) contents of an image could be processed faster to provide feedback to facilitate object recognition. However, this knowledge has not been considered in designing neural network structures. This study introduces SFNet, a new neural network structure that employs an LSF-based feedback mechanism. SFNet is a two-stream structure where one stream is used for LSF processing to provide feedback for image classification. The other stream combines the LSF-based feedback and the HSF processing to form the final decision. The role of the proposed LSF-based feedback in image classification is investigated utilizing the CIFAR100 dataset. The results show that SFNet improves the performance in the presence of SF filtering compared to the equivalent structures.

关键词： Band-pass filters Visualization Shape Filtering Streaming media Visual systems Physiology

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Adaptive model predictive control of mobile robot with local path refitting

Adaptive model predictive control of mobile robot with local...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Liquan Jiang Yuanlong Xie Zhihui Jiang Jie Meng Wenbin Li State Key Laboratory of New Textile Materials and Advanced Processing Technologies Wuhan Textile University Wuhan China School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan China School of Computer Science Yangtze University Jingzhou China Intelligent Transportation Systems Research Center Wuhan University of Technology Wuhan China

ISBN: (纸本)9781665409858

Path planning and tracking control are important modules of mobile robots, which have outstanding advantages in improving the flexibility of mobile robots. However, in the existing applications, these two modules are usually processed separately, which makes the obstacle avoidance process of the mobile robot uncontrollable and reduces the adaptability of the application scenario. In order to realize collision-free high-precision trajectory tracking control of mobile robots in dynamic scenes, this paper proposes an adaptive MPC control method based on curvature constraints, which can improve the safety of operation and reduce the chattering caused by unreasonable paths in the process of operation. Firstly, according to the kinematic constraints of general mobile robots, the constraint model of mobile robot path planning is constructed. At the same time, the adaptive parameter adjustment rules are adopted to realize the balance between the calculation efficiency and the control cycle. Finally, a smooth safe path is obtained by re-fitting the planned path and reprocessing the nodes with excessive curvature. Compared with traditional methods, the proposed method has better advantages.

关键词： Industrial electronics Electric potential Trajectory tracking Kinematics Safety Planning Mobile robots

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Research on the method of path planning for slag-removing on the surface of zinc pot

Research on the method of path planning for slag-removing on...

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第33届中国控制与决策会议

作者： Kebo LI Ling XIONG Zhenzhou ZHANG Gang CHEN Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Key Laboratory of Metallurgical Equipment and Control Technology Wuhan University of Science and Technology

During the production process of cold-rolled hot-dip galvanizing,the slag on the surface of the zinc pot will affect the quality of the *** zinc slag is mostly collected manually or by robots at *** effect of manual salvage is unstable and the operation is *** robot cannot identify the location of the zinc slag and can only salvage it according to a fixed rule,resulting in low *** response to the above problems,a zinc pot slag path planning method based on the greedy algorithm is proposed in this *** zinc pot area is divided,the image processing method is used to identify and locate the zinc slag to be *** the priority of the zinc pot,the obstacle avoidance problem and the restriction of the weight limit of the slag-removing tool are proposed in combination with the process *** three salvage strategies based on the greedy algorithm,the shortest distance,the maximum weight,and the minimum weight are proposed *** results show that the greedy algorithm strategy based on the shortest distance performs best,consumes the least energy,and the algorithm runs for a shorter *** can significantly improve the efficiency of zinc pot slag removal,realize intelligent control of slag removal technology,and can be applied to the actual production of zinc pot slag removal.

关键词： Slag from zinc pot intelligent control Greedy algorithm Path planning Shortest distance strategy

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A Space Crawling Robotic Bio-Paw (Scrbp) Enabled by Triboelectric Sensors for Surface Identification

SSRN

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SSRN 2022年

作者： Hou, Xuyan Zhang, Li Gao, Guowei Liu, Yuhui Na, Zhonglai Xu, QingZhang Ding, Tianxiang Li, Long Chen, Tao Research Center of Aerospace Mechanism and Control School of Mechatronics Engineering Harbin Institute of Technology Harbin150080 China Department of MECHANICAL ENGINEERING FACULTY OF ENGINEERING Hong Kong Polytechnic University Hong Kong Shanghai Key Laboratory of Intelligent Manufacturing and Robotics School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China Jiangsu Provincial Key Laboratory of Advanced Robotics School of Mechanical and Electric Engineering Soochow University Suzhou215123 China

With the rapid development of space crawling robotics technology, tactile perception, a significant source for the robot to sense the external environment, has become the preferred solution to gather information in space. Sensors embedded in the robotic end-effector unit can collect and encode the large tactile data, allowing the robot to feel and perceive the real surroundings. Therefore, a space crawling robotic bio-paw (SCRBP) inspired by a cat paw, which performs as a compliant device and can flat the impulse force during the robot’s contact process to the target surface, is proposed in this paper. Meanwhile, a touch-sensing system embedded on SCRBP with self-powered sensors based on the triboelectric nanogenerator (TENG) technology is proposed which can provide the multi-dimensional sensation information in real time. By combining with machine learning (ML), the sensory system can be used for surface identification from the footfall process to the robot’s controller hub. In conclusion, SCRBP device proposed in this paper has obvious advantages in surface information acquisition, space adaptability, power consumption, cost, reliable signal and minimized data. Accordingly, SCRBP system shows fabulous potential in space robotics. © 2022, The Authors. All rights reserved.

关键词： End effectors

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