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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： He, Ping Lv, Honghao Wu, Haiteng Yang, Geng Polytechnic Institute State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou China Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Enginerring Hangzhou China Hangzhou Shenhao Technology Co. LTD. Zhejiang Key Laboratory of Intelligent Operation and Maintenance Robot Hangzhou China Zhejiang University Zhejiang Key Laboratory of Intelligent Operation and Maintenance Robot State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Enginerring Hangzhou China

ISBN: (纸本)9798350399714

With the increasing of the aging population, the demand for homecare services is escalating and the homecare assistive robots are becoming increasingly popular. In this paper, a differential mobile chassis design for a homecare assistive robot is presented. The mobile chassis is made of aluminum sheet and profile, and adopts a two-wheel differential model as the kinematic model. The STM32 single chip microcomputer is used as the cerebellum to control chassis movement, and the NVIDIA Jetson Nano Developer Kit is employed as the brain for perception and decision-making. The software architecture is based on the back-fore ground system and the ROS. A prototype is developed and experiments on simultaneous localization and mapping (SLAM) as well as autonomous navigation experiments are conducted to verify the practicality of the chassis design. © 2023 IEEE.

关键词： Chassis

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Comprehensive Performance Optimization of a Novel Anthropomorphic Dexterous Hand

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IEEE Robotics and Automation Letters 2025年第7期10卷 6816-6823页

作者： Ma, Yifan Shang, Weiwei Zhang, Fei Pang, Shunxiang Dai, Shuqing Yi, Zhengkun Li, Guotao Cong, Shuang University of Science and Technology of China Department of Automation Jinzhai Road 96 Hefei230027 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen518055 China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China

Grasp and in-hand manipulation of dexterous hands are essential for daily operational tasks. In this study, an optimized design method for a novel anthropomorphic dexterous hand is proposed, which improves the comprehensive performance of grasp and manipulability of the dexterous hand. The constraints of multi-fingered contact force and kinematics are established with full consideration of the applicability to the objects of different sizes and the shape characteristics of finger contact surface. The performance index of in-hand manipulation ability is proposed, and the optimization problem is constructed through the technique for order preference by similarity to ideal solution (TOPSIS) to achieve multi-objective optimization of precision grasp and in-hand manipulation. Finally, simulation and experiment verify the feasibility of our prototype and optimization method. The results show that the in-hand translation space and in-hand rotation range are increased by 20% and 8.5%, respectively. Our dexterous hand has excellent abilities of multi-fingered interaction and in-hand manipulation, which can perform 33 grasp gestures and two/three-fingered in-hand manipulation. This study provides a valuable reference for the design of dexterous hands with anthropomorphic functions. © 2016 IEEE.

关键词： Multiobjective optimization

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A Dynamic Selective Parameter Sharing Mechanism Embedded with Multi-Level Reasoning Abstractions 26

A Dynamic Selective Parameter Sharing Mechanism Embedded wit...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Liu, Yan He, Ying Ming, Zhong Yu, F. Richard Shenzhen Key Lab of Digital & Intelligent Technologies & Systems College of Computer Science and Software Engineering Shenzhen University China State Key Laboratory of Integrated Services Networks Xidian University China Shenzhen China

ISBN: (纸本)9781643684369

Cooperative multi-agent reinforcement learning (Co-MARL) commonly employs different parameter sharing mechanisms, such as full and partial sharing. However, imprudent application of these mechanisms can potentially constrain policy diversity and limit cooperation flexibility. Recent methods that group agents into distinct sharing categories often exhibit poor performance due to challenges in precisely differentiating agents and neglecting the issue of promoting cooperation among these categories. To address these issues, we introduce a dynamic selective parameter sharing mechanism embedded with multi-level reasoning abstractions (DSPS-MA). Our approach uses self-comparison sequences to infer agents' abstract concepts, defining the differences between agents and allowing them to dynamically select partners to share parameters based on these abstract concepts. We also design an intrinsic reward to offer comprehensive collaboration guidance for agents, and introduce a policy cosine similarity regularization term to ensure sufficient policy diversity. Empirical evaluations demonstrate that our approach yields higher returns and faster convergence than state-of-the-art methods. © 2023 The Authors.

关键词： Dynamics

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Edge-aware Feature Aggregation Network for Polyp Segmentation

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Machine Intelligence Research 2025年第1期22卷 101-116页

作者： Tao Zhou Yizhe Zhang Geng Chen Yi Zhou Ye Wu Deng-Ping Fan PCA Lab Key Laboratory of Intelligent Perception and Systems for High-dimensional Information of Ministry of EducationSchool of Computer Science and EngineeringNanjing University of Science and TechnologyNanjing210094China School of Computer Science and Engineering Northwestern Polytechnical University(NPU)Xi’an710129China School of Computer Science and Engineering Southeast UniversityNanjing211189China Computer Vision Lab ETH ZürichZürich8092Switzerland

Precise polyp segmentation is vital for the early diagnosis and prevention of colorectal cancer(CRC)in clinical ***,due to scale variation and blurry polyp boundaries,it is still a challenging task to achieve satisfactory segmentation performance with different scales and *** this study,we present a novel edge-aware feature aggregation network(EFA-Net)for polyp segmentation,which can fully make use of cross-level and multi-scale features to enhance the performance of polyp ***,we first present an edge-aware guidance module(EGM)to combine the low-level features with the high-level features to learn an edge-enhanced feature,which is incorporated into each decoder unit using a layer-by-layer ***,a scale-aware convolution module(SCM)is proposed to learn scale-aware features by using dilated convolutions with different ratios,in order to effectively deal with scale ***,a cross-level fusion module(CFM)is proposed to effectively integrate the cross-level features,which can exploit the local and global contextual ***,the outputs of CFMs are adaptively weighted by using the learned edge-aware feature,which are then used to produce multiple side-out segmentation *** results on five widely adopted colonoscopy datasets show that our EFA-Net outperforms state-of-the-art polyp segmentation methods in terms of generalization and *** implementation code and segmentation maps will be publicly at https://***/taozh2017/EFANet.

关键词： Colorectal cancer polyp segmentation edge-aware guidance module scale-aware convolution module cross-level fusion module

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GOR-PCR: Graph-based Outlier Removal via Global-to-Local Consistency for Point Cloud Registration

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IEEE Transactions on Instrumentation and Measurement 2024年

作者： Zhao, Guiyu Guo, Zhentao Ma, Hongbin Beijing Institute of Technology National Key Lab of Autonomous Intelligent Unmanned Systems School of Automation Beijing100081 China

In point cloud registration, the point cloud with much noise or low overlap will cause more outliers, consequently leading to registration failure. This paper introduces a graph-based outlier removal method with global-to-local consistency for robust point cloud registration. To address the issue of global ambiguity inherent in second-order consistency, we introduce the concept of global-to-local outlier removal. This approach preserves the outlier filtering capability of global consistency while mitigating the ambiguity associated with global consistency. Furthermore, local consistency enhances the detection of local overlapping regions. To bolster the robustness of local consistency in identifying outliers, we propose a dual criterion based on both distance and angle. Finally, robust global registration is achieved by overlap-aware hypothesis verification. Our training-free method outperforms other outlier removal methods, achieving state-of-the-art results of 96.80% RR and 93.79% IR on the 3DMatch dataset. In the outdoor KITTI dataset, our method demonstrates the capability to achieve robust and accurate registration using the less informative FPFH descriptor, attaining the highest RR of 99.82% and the lowest RE of 8.11. Additionally, this method can be effectively integrated with learning-based point cloud registration, which significantly improves their registration recall without introducing much time and space overhead. Moreover, our method improves the feature-based methods by 1.0 ∼ 29.9 pp on RR and 8.0 ∼ 51.3 pp on IR. The codes will be released soon. © 1963-2012 IEEE.

关键词： geometric consistency outlier removal Point cloud registration

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Dual-Layer Collaborative Hunting Algorithm Using Virtual Wolf Pack 43

Dual-Layer Collaborative Hunting Algorithm Using Virtual Wol...

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43rd Chinese Control Conference, CCC 2024

作者： Miao, Zhiqiang Zhang, Jianlei Zhang, Chunyan Wang, Chen Xie, Guangming Nankai University College of Artificial Intelligence Department of Automation Tianjin300071 China Peking University State Key Laboratory for Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Beijing100871 China

ISBN: (纸本)9789887581581

Collaborative hunting, also known as Multi-Agent Pursuit Evasion (MAPE), is a crucial aspect of swarm intelligence. The main obstacles in MAPE research involve making real-time decisions in dynamic environments, efficient task allocation, collision avoidance among multiple agents, planning and tracking prey in unknown environments, and information sharing under communication constraints. This paper presents the Dual-Layer Collaborative Hunting Algorithm Using Virtual Wolf Pack (DLCH-VWP), which utilizes biomimetic principles, specifically leveraging the advantages and hunting strategies of wolf packs, to enhance collaborative efficiency and adaptability in complex environments within MAPE. The algorithm employs a two-tier strategy, with the strategy planning center using biomimetic wolf pack algorithm to manage environment perception, state assessment, prey movement prediction, and inter-robot communication. The local response center treats each wolf as an independently operating sub-pack, utilizing a combination of Grey Wolf Optimizer (GWO) and the local A-star algorithm to provide each wolf with individual autonomy. This allows for efficient dynamic path planning by determining the path for each wolf based on joint decision-making of the group and individual. Simulation experiments have confirmed the effectiveness and feasibility of this algorithm for collaborative hunting in multi-agent systems, presenting a novel strategy for addressing MAPE challenges. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Multi agent systems

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A flexible and wearable NO_(2)gas detection and early warning device based on a spraying process and an interdigital electrode at room temperature

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Microsystems & Nanoengineering 2022年第2期8卷 97-106页

作者： Fuzheng Zhang Qijing Lin Feng Flan Zuowei Wang Bian Tian Libo Zhao Tao Dong Zhuangde Jiang State Key Laboratory for Mechanical Manufacturing Systems Engineering Xi'an Jiaotong University710049 Xi'anChina Chongqing Key Laboratory of Micro-Nano Systems and Intelligent Sensing Chongqing Academician WorkstationChongqing 2011 Collaborative Innovation Center of Micro/Nano Sensing and Intelligent Ecological Internet of ThingsChongqing Technology and Business University400067 ChongqingChina Department of Microsystems Faculty of TechnologyNatural Sciences and Maritime SciencesUniversity of South-Eastern Norway(USN)Raveien 2153184 BorreNorway

Flexible sensors used to detect NO_(2) gas generally have problems such as poor repeatability,high operating temperature,poor selectivity,and small detection *** this work,a new spraying platform with a simple structure,low cost,and good film-forming consistency was designed and built to make a sensitive film(rGO/SnO_(2))for NO_(2) gas *** relationship between the solid content of rGO and SnO_(2)nanoparticles,annealing temperature,and sensor performance was *** results show that the interdigital electrode-sensitive film formed by spraying 0.25 ml of a 0.4 wt%rGO/SnO_(2)mixture and annealing at 250℃exhibited the best comprehensive performance for NO_(2) *** sensor's response value for 100 ppm NO_(2) gas was 0.2640 at room temperature(25℃),and the response time and recovery time were 412.4 s and 587.3 s,*** the range of 20-100 ppm,the relationship between the response and NO_(2) concentration was linear,and the correlation coefficient was *** addition,a soft-monitoring node module with an overlimit warning function for NO_(2) gas was designed and manufactured based on flexible ***,the flexible sensor and node module were embedded into woven fabric that could be used to make a mask or a watch that could detect NO_(2) gas,realizing the practical application of flexible NO_(2) gas sensors in the field of wearable electronics.

关键词： wear electrode spraying

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A RankNet-Inspired Surrogate-Assisted Hybrid Metaheuristic for Expensive Coverage Optimization

arXiv

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arXiv 2025年

作者： Wu, Tong-Yu Miao, Chang-Hao Zhang, Yun-Tian Deng, Fang Chen, Chen National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology Beijing100081 China

Coverage optimization generally involves deploying a set of facilities to best satisfy the demands of specified points, with broad applications in fields such as location science and sensor networks. Recent applications reveal that the subset site selection coupled with continuous angular parameter optimization can be formulated as Mixed-Variable Optimization Problems (MVOPs). Meanwhile, high-fidelity discretization and visibility analysis significantly increase computational cost and complexity, evolving the MVOP into an Expensive Mixed-Variable Optimization Problem (EMVOP). While canonical Evolutionary Algorithms have yielded promising results, their reliance on numerous fitness evaluations is too costly for our problem. Furthermore, most surrogate-assisted methods face limitations due to their reliance on regression-based models. To address these issues, we propose the RankNet-Inspired Surrogate-assisted Hybrid Metaheuristic (RI-SHM), an extension of our previous work. RI-SHM integrates three key components: (1) a RankNet-based pairwise global surrogate that innovatively predicts rankings between pairs of individuals, bypassing the challenges of fitness estimation in discontinuous solution space;(2) a surrogate-assisted local Estimation of Distribution Algorithm that enhances local exploitation and helps escape from local optima;and (3) a fitness diversity-driven switching strategy that dynamically balances exploration and exploitation. Experiments demonstrate that our algorithm can effectively handle large-scale coverage optimization tasks of up to 300 dimensions and more than 1,800 targets within desirable runtime. Compared to state-of-the-art algorithms for EMVOPs, RI-SHM consistently outperforms them by up to 56.5% across all tested instances. Copyright © 2025, The Authors. All rights reserved.

关键词： Site selection

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Improving the Interpretability through Maximizing Mutual Information for EEG Emotion Recognition

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IEEE Transactions on Affective Computing 2024年第2期16卷 744-757页

作者： Yang, Hua Philip Chen, C.L. Chen, Bianna Zhang, Tong North Carolina State University Department of Computer Science United States South China University of Technology Guangdong Provincial Key Laboratory of AI Large Model and Intelligent Cognition The School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center The Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Trustworthy Graph Neural Networks (GNNs) for EEG emotion recognition should identify emotions accurately and elucidate corresponding rationales. Current GNNs have achieved notable performance by dynamically modeling emotional connections between EEG channels. However, these GNNs lack interpretability due to the absence of explicit rationale behind their predictions. This paper conducts a comprehensive identification of important EEG channels to enhance the interpretability of EEG emotion recognition from the perspective of mutual information. Specifically, an Adjacency-Explainable Graph Neural Network (AEG) for ante-hoc interpretability is proposed to capture genuine EEG emotional connections, which gives a theoretical guarantee to remove spurious connections. Moreover, a Channel-wise Adaptive Class Activation Mapping Explainer (CACA) for post-hoc interpretability is developed to locate the EEG channels that contribute most to predictions. Experimental results on three datasets, i.e., SEED, SEED-IV, and DREAMER, prove that imbuing training processes with enhanced interpretability ensures significant performance improvements in emotion recognition. Quantitative comparisons of post-hoc interpretability also demonstrate the superiority of CACA. Furthermore, this paper illustrates two potential applications of the proposed methodologies, showing their broader utility and significance. © 2010-2012 IEEE.

关键词： Graph neural networks

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Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection 24

Dual-Modeling Decouple Distillation for Unsupervised Anomaly...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Liu, Xinyue Wang, Jianyuan Leng, Biao Zhang, Shuo School of Computer Science and Engineering Beihang University Beijing China Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China Beijing Key Lab of Traffic Data Analysis and Mining School of Computer & Technology Beijing Jiaotong University Beijing China

ISBN: (纸本)9798400706868

Knowledge distillation based on student-teacher network is one of the mainstream solution paradigms for the challenging unsupervised Anomaly Detection task, utilizing the difference in representation capabilities of the teacher and student networks to implement anomaly localization. However, over-generalization of the student network to the teacher network may lead to negligible differences in representation capabilities of anomaly, thus affecting the detection effectiveness. Existing methods address the possible over-generalization by using differentiated students and teachers from the structural perspective or explicitly expanding distilled information from the content perspective, which inevitably results in an increased likelihood of underfitting of the student network and poor anomaly detection capabilities in anomaly center or edge. In this paper, we propose Dual-Modeling Decouple Distillation (DMDD) for the unsupervised Anomaly Detection. In DMDD, a Decouple Student-Teacher Network is proposed to decouple the initial student features into normality and abnormality features. We further introduce Dual-Modeling Distillation based on normal-anomalous image pairs, fitting normality features of anomalous image and the teacher features of the corresponding normal image, widening the distance between abnormality features and the teacher features in anomalous regions. Synthesizing these two distillation ideas, we achieve anomaly detection which focuses on both edge and center of anomaly. Finally, a Multi-perception Segmentation Network is proposed to achieve focused anomaly map fusion based on multiple attention. Experimental results on MVTec AD show that DMDD surpasses SOTA localization performance of previous knowledge distillation-based methods, reaching 98.85% on pixel-level AUC and 96.13% on PRO. © 2024 ACM.

关键词： Students

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