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International Conference on Guidance, Navigation and Control, ICGNC 2022

作者： Huang, Jingwen Yu, Hai Liang, Xiao Fang, Yongchun Han, Jianda College of Artificial Intelligence Tianjin Key Laboratory of Intelligent Robotics Institute of Robotics and Automatic Information Systems Nankai University Tianjin300350 China

ISBN: (纸本)9789811966125

Unmanned quadcopter transportation systems present extremely strong underactuated characteristics. Due to the unmodeled dynamics and unknown disturbances in practical flight, existing control schemes may lead to poor control effects or even control failure. This paper presents a robust tracking control for unmanned quadcopter transportation systems, which can achieve accurate trajectory tracking and eliminate unexpected payload swings effectively. Simulations are provided to verify the effectiveness of the control scheme against unknown uncertainties. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Robust control

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A Directed Jump Point Search with Improved Preprocess for Path Planning 18

A Directed Jump Point Search with Improved Preprocess for Pa...

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Xie, Zongquan Cheng, Lei Li, Xiang Chen, Xiuqi Wuhan University of Science and Technology School of Information Science and Engineering Wuhan China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9798350312201

We introduce a new algorithm called directed JPS+(Jump Point Search Plus) that addresses the large memory overheads of JPS+ and improves its performance in cases where the obstacle scale is small. Our approach involves two main modifications. First, we reduce memory usage by recording only jump points in the preprocessing stage instead of eight labels for each nodes in JPS+. And we introduce a technique to connect these precomputed jump points into jump point set. Second, we apply an optimization to JPS+ that speeds up path planning in maps with small obstacle scale and it has no negative impact on other cases. We implement our algorithm in Python and evaluate its performance in terms of memory usage and planning time. Our results demonstrate that it saves memory and speeds up the path planning, particularly in maps with small obstacle scale. © 2023 IEEE.

关键词： Motion planning

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Cervical cancer classification using sparse stacked autoencoder and fuzzy ARTMAP

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Neural Computing and Applications 2024年第22期36卷 13895-13913页

作者： Liaw, Lawrence Chuin Ming Tan, Shing Chiang Goh, Pey Yun Lim, Chee Peng Faculty of Information Science and Technology Multimedia University Melaka75450 Malaysia Institute for Intelligent Systems Research and Innovation Deakin University Geelong Waurn PondsVIC3216 Australia

Cervical cancer (CC) is affecting women predominantly, and early diagnosis could cure this cancer. This study aims to design and develop an effective deep learning-based classification model to detect early CC stages using clinical data. The proposed method is a combination of an unsupervised deep learning and a supervised neural network, i.e. sparse stacked autoencoder (SSAE) and fuzzy adaptive resonance theory MAP (FAM), respectively, and is denoted as SSAE-FAM. Specifically, SSAE is applied to tackle the data sparsity problem. It extracts the representative features from a data set through feature transformation. The transformed features are then classified by FAM. In this study, a CC data set obtained from the University of California Irvine (UCI) machine learning repository is utilised for evaluation. Owing to missing data in the original CC data set, two data sets are generated from the original CC data samples using two data preprocessing techniques. Both generated CC data sets with four target classes (i.e. Schiller, Cytology, Biopsy, and Hinselmann) are evaluated as four independent binary-class problems. We improve the classification performance of FAM by mitigating the data sparsity problem. Based on a series of experimental studies, SSAE-FAM outperforms other state-of-art methods by achieving 99.47%, 99.34%, 99.48%, and 99.81% mean accuracy rates, respectively, with the first CC data set, and 99.74%, 99.86%, 99.77%, and 99.80% mean accuracy rates, respectively, with the second CC data set. The results positively indicate the usefulness of SSAE-FAM for early CC diagnosis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Cytology

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Research on Workpiece Corner Detection Method based on Combination Method Image Feature Points 16

Research on Workpiece Corner Detection Method based on Combi...

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16th IEEE International Conference on Electronic Measurement and Instruments, ICEMI 2023

作者： Han, Dongsheng Li, Tianjian Huang, Min Hu, Huan Wang, Wensheng Institute of Intelligent Robotics Beijing Information Science and Technology University Beijing China Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9798350327144

To improve the workpiece localization accuracy, the paper proposes an improved image alignment optimization method by random sample consensus (RANSAC) and selecting some point pairs with high confidence from them, and then calculating the affine transformation matrix separately using the combination method to filter the optimal affine transformation matrix algorithm. The image is preprocessed. First, the grayscale image is filtered with a median to remove the noise, and the scale-invariant feature transform (SIFT) detection algorithm is used to obtain the image feature points and perform feature point matching;the more decisive matching points are coarsely selected by the distance threshold between the matched feature point pairs, and the more decisive matching points are chosen from Several teams of issues are selected from the more decisive matching points. The base image transformation matrix is calculated respectively, specific methods filter the better matrix, and the better rotation angle is calculated. The accuracy and stability of the algorithm are verified through experiments, and the experimental results show that the error of the rotation angle of the workpiece is within 0.1° and the algorithm's running time is within 1 ms. © 2023 IEEE.

关键词： Image enhancement

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Disturbance rejection for biped robots during walking and running using control moment gyroscopes

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IET Cyber-systems and robotics 2022年第4期4卷 268-282页

作者： Haochen Xu Zhangguo Yu Xuechao Chen Chencheng Dong Huanzhong Chen Qiang Huang School of Mechatronical Engineering Beijing Institute of TechnologyBeijingChina Beijing Advanced Innovation Center for Intelligent Robotics and Systems Beijing Institute of TechnologyBeijingChina

Keeping balance in movement is an important premise for biped robots to complete various ***,the balance control of biped robots mainly depends on the cooperation of various joints of the robot's *** robots move faster,the adjustment allowance of joints is reduced,and the robot's anti-disturbance ability will inevitably *** solve this problem,the control moment gyroscope(CMG)is creatively used as an auxiliary stabilisation device for fully actuated biped robots and the CMG assistance strategy,which can be integrated into the biped's balance control framework,is *** strategy includes model predictive control module,distribution module,and CMG precession *** the command of it,CMGs can effectively assist the robot in resisting impact and returning to initial positions in *** results of anti-impact simulation on the walking and running biped robot prove that,with the help of CMGs,the robot's ability to resist disturbance and remain stable is significantly *** cover image is based on the Original Article Disturbance rejection for biped robots during walking and running using control moment gyroscopes by Haochen Xu et al.,https://***/10.1049/csy2.12070.

关键词： anti-impact biped robot control moment gyroscope model predictive control

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Dual-modal Motion of Magnetically Controlled Soft Microrobots with Environmentally Responsive Reversible Deformation 24

Dual-modal Motion of Magnetically Controlled Soft Microrobot...

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2nd International Conference on Frontiers of intelligent Manufacturing and Automation, CFIMA 2024

作者： Xin, Zhengyuan Wu, Anping Bai, Kailun Yang, Haotian Lin, Kaijun Shi, Qing Wang, Huaping Hou, Yaozhen Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing China School of Medical Technology Beijing Institute of Technology Beijing China Science and Technology on Space Physics Laboratory Beijing China 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

ISBN: (纸本)9798400710681

Soft microrobots have garnered attention in the biomedical field due to their ability to access hard-to-reach regions within living organisms for precise manipulation. However, the inability to alter the stable morphology after design and fabrication remains a pressing issue for existing soft microrobots. This limitation results in a fixed range of locomotion modes, thereby restricting the diversity of operational environments. Addressing this challenge, this study proposes and designs a biocompatible X-shaped soft microrobot that achieves alterable stable morphologies through an innovative mechanism. We developed a deformable soft hydrogel robot based on alginate gel’s ability to reversibly swell in response to ion concentration changes. Based on the deformation characteristics of this microrobot, we have designed undulating and rolling locomotion modes that are suitable for different morphologies, along with their corresponding magnetic field manipulation systems, enabling multi-morphology locomotion of the robot. Experimental validations demonstrate that the designed flexible microrobot can achieve a deformation deflection angle of up to 150° under manipulation. This robot not only performs forward undulating locomotion in its expanded state but also executes rolling motion in its spherical configuration, exhibiting high locomotion flexibility and adaptability. © 2024 Copyright held by the owner/author(s).

关键词： Microrobots

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Robust Counting in Overcrowded Scenes Using Batch-Free Normalized Deep ConvNet

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Computer systems Science & Engineering 2023年第9期46卷 2741-2754页

作者： Sana Zahir Rafi Ullah Khan Mohib Ullah Muhammad Ishaq Naqqash Dilshad Amin Ullah Mi Young Lee Institute of Computer Sciences and Information Technology(ICS/IT) The University of AgriculturePeshawar25130Khyber PakhtunkhwaPakistan Department of Convergence Engineering for Intelligent Drone Sejong UniversitySeoul05006Korea Collaborative Robotics and Intelligent Systems(CoRIS)Institute Oregon State UniversityORUSA Department of Software Sejong UniversitySeoul05006Korea

The analysis of overcrowded areas is essential for flow monitoring,assembly control,and *** counting’s primary goal is to calculate the population in a given region,which requires real-time analysis of congested scenes for prompt reactionary *** crowd is always unexpected,and the benchmarked available datasets have a lot of variation,which limits the trained models’performance on unseen test *** this paper,we proposed an end-to-end deep neural network that takes an input image and generates a density map of a crowd *** proposed model consists of encoder and decoder networks comprising batch-free normalization layers known as evolving normalization(EvoNorm).This allows our network to be generalized for unseen data because EvoNorm is not using statistics from the training *** decoder network uses dilated 2D convolutional layers to provide large receptive fields and fewer parameters,which enables real-time processing and solves the density drift problem due to its large receptive *** benchmark datasets are used in this study to assess the proposed model,resulting in the conclusion that it outperforms conventional models.

关键词： Artificial intelligence deep learning crowd counting scene understanding

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Can Transformers Smell Like Humans? 38

Can Transformers Smell Like Humans?

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38th Conference on Neural information Processing systems, NeurIPS 2024

作者： Taleb, Farzaneh Vasco, Miguel Ribeiro, Antônio H. Björkman, Mårten Kragic, Danica Department of Intelligent Systems KTH Royal Institute of Technology Stockholm Sweden Department of Information Technology Uppsala University Uppsala Sweden

The human brain encodes stimuli from the environment into representations that form a sensory perception of the world. Despite recent advances in understanding visual and auditory perception, olfactory perception remains an under-explored topic in the machine learning community due to the lack of large-scale datasets annotated with labels of human olfactory perception. In this work, we ask the question of whether pre-trained transformer models of chemical structures encode representations that are aligned with human olfactory perception, i.e., can transformers smell like humans? We demonstrate that representations encoded from transformers pre-trained on general chemical structures are highly aligned with human olfactory perception. We use multiple datasets and different types of perceptual representations to show that the representations encoded by transformer models are able to predict: (i) labels associated with odorants provided by experts;(ii) continuous ratings provided by human participants with respect to pre-defined descriptors;and (iii) similarity ratings between odorants provided by human participants. Finally, we evaluate the extent to which this alignment is associated with physicochemical features of odorants known to be relevant for olfactory decoding. © 2024 Neural information processing systems foundation. All rights reserved.

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Cascaded Optimization-Based Motion Planning for Dual-UAV Transportation System

Cascaded Optimization-Based Motion Planning for Dual-UAV Tr...

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International Conference on Guidance, Navigation and Control, ICGNC 2024

作者： Gao, Jinjiang Zhang, Zhaopeng Pei, Tengfei Liang, Xiao Han, Jianda Institute of Robotics and Automatic Information Systems College of Artificial Intelligence Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin300350 China

ISBN: (纸本)9789819622115

Aerial cooperative system with suspended cables plays an important role in material transportation, disaster relief and other related fields. To achieve safe and rapid conveyance, a motion planning method for dual-UAV system is proposed in this paper. Utilizing the representation of piecewise polynomials, a nonlinear optimization problem considering various constraints is formulated. Subsequently, a feasible payload trajectory is obtained, based on which the cable force allocation is conducted by solving two quadratic programming problems. Finally, with the help of differential flatness, the entire system state is attained. Simulation results demonstrate the effectiveness of the proposed scheme. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Disaster prevention

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Person Re-Identification Based on Spatial Feature Learning and Multi-Granularity Feature Fusion

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上海交通大学学报（英文版） 2025年第2期30卷 363-374页

作者：刁子健曹帅李文威梁佳楠文桂林黄伟溪张寿明 Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Robotic Laboratory South China Robotics Innovation Research Institute Foshan China Robotic Laboratory Institute of Intelligent Manufacturing Guangdong Academy of Sciences Guangzhou China Foshan Zhiyouren Technology Co. Ltd. Foshan China

In view of the weak ability of the convolutional neural networks to explicitly learn spatial invariance and the probabilistic loss of discriminative features caused by occlusion and background interference in pedestrian re-identification tasks,a person re-identification method combining spatial feature learning and multi-granularity feature fusion was ***,an attention spatial transformation network(A-STN)is proposed to learn spatial features and solve the problem of misalignment of pedestrian spatial *** the network was divided into a global branch,a local coarse-grained fusion branch,and a local fine-grained fusion branch to extract pedestrian global features,coarse-grained fusion features,and fine-grained fusion features,*** them,the global branch enriches the global features by fusing different pooling *** local coarse-grained fusion branch uses an overlay pooling to enhance each local feature while learning the correlation relationship between multi-granularity *** local fine-grained fusion branch uses a differential pooling to obtain the differential features that were fused with global features to learn the relationship between pedestrian local features and pedestrian global ***,the proposed method was compared on three public datasets:Market1501,DukeMTMC-RelD and *** experimental results were better than those of the comparative methods,which verifies the effectiveness of the proposed method.

关键词： pedestrian re-identification spatial features attention spatial transformation network multi-branch network relation features

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