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IEEE International Conference on robotics and Automation (ICRA)

作者： Hanjing Ye Jieting Zhao Yaling Pan Weinan Cherr Li He Hong Zhang Department of Electronic and Electrical Engineering Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology (SUSTech) SUSTech Biomimetic and Intelligent Robotics Lab Guangdong University of Technology

Robot person following (RPF) is a capability that supports many useful human-robot-interaction (HRI) applications. However, existing solutions to person following often as-sume full observation of the tracked person. As a consequence, they cannot track the person reliably under partial occlusion where the assumption of full observation is not satisfied. In this paper, we focus on the problem of robot person following under partial occlusion caused by a limited field of view of a monocular camera. Based on the key insight that it is possible to locate the target person when one or more of hislher joints are visible, we propose a method in which each visible joint contributes a location estimate of the followed person. Experiments on a public person-following dataset show that, even under partial occlusion, the proposed method can still locate the person more reliably than the existing SOTA methods. As well, the application of our method is demonstrated in real experiments on a mobile robot.

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Adaptive Soft Pneumatic Robot Inspired by Locomotion of Snake

Adaptive Soft Pneumatic Robot Inspired by Locomotion of Snak...

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IEEE International Conference on robotics and Biomimetics

作者： Jinxuan Lu Xinshu Hu Xiaoyu Zhu Cheng Chen Hongqiang Wang Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems Southern University of Science and Technology Shenzhen China Department of Mechanical and Energy Engineering and Guangdong Provincial Key Laboratory of Human Augmentation and Rehabilitation Robotics in Universities Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Southern University of Science and Technology Shenzhen China Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) Guangzhou China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Pneumatic soft robots have garnered widespread attention for their flexibility and adaptability in various applications. However, existing soft robots generally struggle to effectively navigate diverse terrains, especially when it comes to climbing obstacles. This paper introduces an adaptive pneumatic soft robot designed based on snake-like locomotion, which effectively addresses this issue. The robot utilizes four corrugated tubes as its soft actuators, complemented by silicone-made feet with bidirectional friction anisotropy, enabling it to adapt to various terrains and exhibit climbing capabilities. Experiments demonstrate that the robot can traverse different surfaces such as sand, gravel, and slopes, and can climb obstacles up to 20 centimeters high. Additionally, it features a self-regulation mechanism that allows it to recover from accidental overturns. This study presents a soft robot design with multi-terrain adaptability and self-recovery capabilities.

关键词： Corrugated surfaces Actuators Anisotropic magnetoresistance Navigation Friction Biomimetics MIMICs Soft robotics Pneumatic systems Environmental monitoring

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DiffTimb: Diffusion Models for Many-to-Many Timbre Transfer 10th

DiffTimb: Diffusion Models for Many-to-Many Timbre Transfer

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10th Conference on Sound and Music Technology, CSMT 2023

作者： Gan, Xu Wu, Yifei Liu, Xinlu Duan, Ruilei Li, Wei School of Computer Science and Technology Fudan University Shanghai200438 China Department of Music Engineering ZheJiang Conservatory of Music Hangzhou310024 China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai200433 China

ISBN: (纸本)9789819779611

The goal of the timbre transfer task is to apply the auditory characteristics of one instrument to another instrument. Past timbre transfer methods faced numerous challenges due to the limitations of the selected generation model. These challenges include difficulties in training, ambiguous results, poor diversity, and slow generation speed. To address these issues, we propose a diffusion model for timbre transfer, DiffTimb, which can be combined with vocoder to achieve single-note timbre transfer. Unlike traditional models that require training a specific model for each pair of timbre transfers, DiffTimb enables the conversion of various musical instrument timbres received in a single model, namely, many-to-many timbre transfer. We compare two Unet-based denoising frameworks and propose two feature fusion methods to fuse timbre and pitch features. We conduct unconditional and conditional generation pre-experiments to verify the generation ability of DiffTimb. To improve the generation effect, we clean the Nsynth dataset and get two subsets for our task. Experimental results show that DiffTimb has outperformed previous methods in both subjective and objective evaluations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Audio acoustics

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A Client Selection Method Based on Loss Function Optimization for Federated Learning

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computer Modeling in Engineering & sciences 2023年第10期137卷 1047-1064页

作者： Yan Zeng Siyuan Teng Tian Xiang Jilin Zhang Yuankai Mu Yongjian Ren Jian Wan School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China Key Laboratory of Complex System Modeling and Simulation Ministry of EducationHangzhou310018China Zhejiang Engineering Research Center of Data Security Governance Hangzhou310018China Intelligent Robotics Research Center Zhejiang LabHangzhou311100China HDU-ITMO Joint Institute Hangzhou Dianzi UniversityHangzhou310018China

Federated learning is a distributedmachine learningmethod that can solve the increasingly serious problemof data islands and user data privacy,as it allows training data to be kept locally and not shared with other *** trains a globalmodel by aggregating locally-computedmodels of clients rather than their ***,the divergence of local models caused by data heterogeneity of different clients may lead to slow convergence of the global *** this problem,we focus on the client selection with federated learning,which can affect the convergence performance of the global model with the selected local *** propose FedChoice,a client selection method based on loss function optimization,to select appropriate local models to improve the convergence of the global *** firstly sets selected probability for clients with the value of loss function,and the client with high loss will be set higher selected probability,which can make them more likely to participate in ***,it introduces a local control vector and a global control vector to predict the local gradient direction and global gradient direction,respectively,and calculates the gradient correction vector to correct the gradient direction to reduce the cumulative deviationof the local gradient causedby *** experiments to verify the validity of FedChoice on CIFAR-10,CINIC-10,MNIST,EMNITS,and FEMNIST datasets,and the results show that the convergence of FedChoice is significantly improved,compared with FedAvg,FedProx,and FedNova.

关键词： Federated learning model aggregation Non-IID

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Optimal Linear Deception Attacks on Remote State Estimation with Constrained Alarm Rates: A Low-Dimensional Case 63

Optimal Linear Deception Attacks on Remote State Estimation ...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Shang, Jun Zhang, Hanwen Zhou, Jing Chen, Tongwen Tongji University Shanghai Research Institute for Intelligent Autonomous Systems National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Department of Control Science and Engineering Shanghai200092 China University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering Beijing100083 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

ISBN: (纸本)9798350316339

This study addresses linear attacks on remote state estimation within the context of a constrained alarm rate. Smart sensors, which are equipped with local Kalman filters, transmit innovations instead of raw measurements through a wireless communication network. This transmission is vulnerable to malicious data interception and manipulation by attackers. The aim of this research is to identify the optimal attack strategy that degrades the system performance while adhering to stealthiness constraints. A notable innovation of this paper is the direct association of the attack's stealthiness with the alarm rate, diverging from traditional approaches that rely on the covariance of the innovation or the Kullback-Leibler divergence, which are conventional metrics that have been extensively explored in previous studies. Our findings reveal that the optimal attack strategy exhibits some structural characteristics in systems of low dimensions. The performance of the proposed attack strategy is demonstrated through numerical examples. © 2024 IEEE.

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MSMB-GCN: Multi-scale Multi-branch Fusion Graph Convolutional Networks for 3D Human Pose Estimation

MSMB-GCN: Multi-scale Multi-branch Fusion Graph Convolutiona...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Ji, Shanshan Meng, Qiwei Wang, Wen Lin, Zheyuan Li, Te Wan, Minhong Zhang, Chunlong Gu, Jason Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Research Center for Intelligent Robotics Hangzhou311100 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9798350325706

In human-robot interaction (HRI), human pose estimation is a necessary technology for the robot to perceive the dynamic environment and make interactive actions. Recently, graph convolutional networks (GCNs) have been increasingly used for 2D to 3D pose estimation tasks since the skeleton topologies can be viewed as graph structures. In this paper, we propose a novel graph convolutional network architecture, Multi-scale Multi-branch Fusion Graph Convolutional Networks (MSMB-GCN), for 3D Human Pose Estimation(3D HPE) task. The proposed model consists of multiple GCN blocks with a multi-branch architecture. This multi-branch architecture enables the model to get multi-scale features for human skeletal representations. The group of GCN blocks, which has strong multi-level feature extraction capabilities, allows the model to learn global and local features, lower-level and higher-level features. Experiment results on the HumanPose benchmark demonstrate that our model outperforms the state-of-the-art and ablation studies validate the effectiveness of our approach. © 2023 IEEE.

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FoundationGrasp: Generalizable Task-Oriented Grasping with Foundation Models

arXiv

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arXiv 2024年

作者： Tang, Chao Huang, Dehao Dong, Wenlong Xu, Ruinian Zhang, Hong Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Institute for Robotics and Intelligent Machines Georgia Institute of Technology Atlanta United States

Task-oriented grasping (TOG), which refers to synthesizing grasps on an object that are configurationally compatible with the downstream manipulation task, is the first milestone towards tool manipulation. Analogous to the activation of two brain regions responsible for semantic and geometric reasoning during cognitive processes, modeling the intricate relationship between objects, tasks, and grasps necessitates rich semantic and geometric prior knowledge about these elements. Existing methods typically restrict the prior knowledge to a closed-set scope, limiting their generalization to novel objects and tasks out of the training set. To address such a limitation, we propose FoundationGrasp, a foundation model-based TOG framework that leverages the open-ended knowledge from foundation models to learn generalizable TOG skills. Extensive experiments are conducted on the contributed Language and Vision Augmented TaskGrasp (LaViA-TaskGrasp) dataset, demonstrating the superiority of FoundationGrasp over existing methods when generalizing to novel object instances, object classes, and tasks out of the training set. Furthermore, the effectiveness of FoundationGrasp is validated in real-robot grasping and manipulation experiments on a 7-DoF robotic arm. Our code, data, appendix, and video are publicly available at https://***/view/foundationgrasp. Copyright © 2024, The Authors. All rights reserved.

关键词： Robotic arms

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OPTIMIZING FOREST FIRE PREVENTION: intelligent SCHEDULING ALGORITHMS FOR DRONE-BASED SURVEILLANCE SYSTEM

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arXiv 2023年 225卷 1562-1571页

作者： Jemmali, Mahdi Melhim, Loai Kayed B. Boulila, Wadii Amdouni, Hajer Alharbi, Mafawez T. Department of Computer Science and Information College of Science at Zulfi Majmaah University Al-Majmaah11952 Saudi Arabia MARS Laboratory University of Sousse Sousse Tunisia Department of Computer Science Higher Institute of Computer Science and Mathematics Monastir University Monastir5000 Tunisia Department of Health Information Management and Technology College of Applied Medical Sciences University of Hafr Al Batin Hafr Al Batin39524 Saudi Arabia Robotics and Internet-of-Things Laboratory Prince Sultan University Riyadh12435 Saudi Arabia RIADI Laboratory National School of Computer Sciences University of Manouba Manouba Tunisia Higher School of Economic and Commercial Sciences Tunis University of Tunis Tunisia Unit of Scientific Research Applied College Qassim University Buraydah51452 Saudi Arabia

Given the importance of forests and their role in maintaining the ecological balance, which directly affects the planet, the climate, and the life on this planet, this research presents the problem of forest fire monitoring using drones. The forest monitoring process is performed continuously to track any changes in the monitored region within the forest. During fires, drones’ capture data is used to increase the follow-up speed and enhance the control process of these fires to prevent their spread. The time factor in such problems determines the success rate of the fire extinguishing process, as appropriate data at the right time may be the decisive factor in controlling fires, preventing their spread, extinguishing them, and limiting their losses. Therefore, this research presented the problem of monitoring task scheduling for drones in the forest monitoring system. This problem is solved by developing several algorithms with the aim of minimizing the total completion time required to carry out all the drones’ assigned tasks. System performance is measured by using 990 instances of three different classes. The performed experimental results indicated the effectiveness of the proposed algorithms and their ability to act efficiently to achieve the desired goal. The algorithm RID achieved the best performance with a percentage rate of up to 90.3% with a time of 0.088 seconds. © 2023, CC BY.

关键词： Fires

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Multi-Objective Optimization Algorithm for Grouping Decision Variables Based on Extreme Point Pareto Frontier

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computers, Materials & Continua 2024年第4期79卷 1281-1299页

作者： JunWang Linxi Zhang Hao Zhang Funan Peng Mohammed A.El-Meligy Mohamed Sharaf Qiang Fu College of Computer Science and Technology Shenyang University of Chemical TechnologyShenyang110142China Key Laboratory of Intelligent Technology for Chemical Process Industry of Liaoning Province Shenyang University of Chemical TechnologyShenyang110142China Industrial Engineering Department College of EngineeringKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia

The existing algorithms for solving multi-objective optimization problems fall into three main categories:Decomposition-based,dominance-based,and *** multi-objective optimization problemsmainly focus on objectives,treating decision variables as a total variable to solve the problem without consideringthe critical role of decision variables in objective *** seen,a variety of decision variable groupingalgorithms have been ***,these algorithms are relatively broad for the changes of most decisionvariables in the evolution process and are time-consuming in the process of finding the Pareto *** solvethese problems,a multi-objective optimization algorithm for grouping decision variables based on extreme pointPareto frontier(MOEA-DV/EPF)is *** algorithm adopts a preprocessing rule to solve the Paretooptimal solution set of extreme points generated by simultaneous evolution in various target directions,obtainsthe basic Pareto front surface to determine the convergence effect,and analyzes the convergence and distributioneffects of decision *** the later stages of algorithm optimization,different mutation strategies are adoptedaccording to the nature of the decision variables to speed up the rate of evolution to obtain excellent individuals,thusenhancing the performance of the *** validation of the test functions shows that this algorithmcan solve the multi-objective optimization problem more efficiently.

关键词： Multi-objective evolutionary optimization algorithm decision variables grouping extreme point pareto frontier

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Outage Analysis of RSMA Enabled Integrated Satellite-Terrestrial Networks

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IEEE Transactions on Vehicular Technology 2025年第6期74卷 10023-10028页

作者： Hu, Chenbo Yang, Hongjuan Zhou, Zhiquan Li, Bo Jiang, Xu Zhao, Nan Nallanathan, Arumugam Weihai264209 China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian116024 China School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Department of Electronic Engineering Kyung Hee University Yongin-si Gyeonggi-do 17104 China

Rate splitting multiple access (RSMA) has emerged in the integrated satellite-terrestrial networks (ISTNs). However, the satellite-terrestrial channel suffers serious shadow fading and cannot maintain stable communication links effectively. From the perspective of reliability, this correspondence takes the lead in focusing on the outage probability (OP) of ISTNs based on the combination of RSMA and hybrid automatic repeat request with chase combining (HARQ-CC). The lower and upper bounds of OP are derived firstly according to the inclusion-exclusion principle. Then, the diversity order is deduced, pointing out the conditions for full time diversity. Monte Carlo simulations confirm the accuracy of the theoretical results, and superior outage performance of HARQ-CC-aided RSMA is present compared with benchmarks. © 1967-2012 IEEE All rights reserved.

关键词： Satellite links

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