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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jing Zeng Yanxu Li Jiahao Sun Qi Ye Yunlong Ran Jiming Chen College of Control Science and Engineering Zhejiang University Hangzhou China College of Control Science and Engineering the State Key Laboratory of Industrial Control Technology Zhejiang University and the Key Key Lab of CS&AUS of Zhejiang Province

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Implicit neural representations have demonstrated significant promise for 3D scene reconstruction. Recent works have extended their applications to autonomous implicit reconstruction through the Next Best View (NBV) based method. However, the NBV method cannot guarantee complete scene coverage and often necessitates extensive viewpoint sampling, particularly in complex scenes. In the paper, we propose to 1) incorporate frontier-based exploration tasks for global coverage with implicit surface uncertainty-based reconstruction tasks to achieve high-quality reconstruction. and 2) introduce a method to achieve implicit surface uncertainty using color uncertainty, which reduces the time needed for view selection. Further with these two tasks, we propose an adaptive strategy for switching modes in view path planning, to reduce time and maintain superior reconstruction quality. Our method exhibits the highest reconstruction quality among all planning methods and superior planning efficiency in methods involving reconstruction tasks. We deploy our method on a UAV and the results show that our method can plan multi-task views and reconstruct a scene with high quality.

关键词： Surface reconstruction Uncertainty Three-dimensional displays Pose estimation Switches Path planning Planning

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Masked Visual-Tactile Pre-training for Robot Manipulation

Masked Visual-Tactile Pre-training for Robot Manipulation

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Qingtao Liu Qi Ye Zhengnan Sun Yu Cui Gaofeng Li Jiming Chen College of Control Science and Engineering Zhejiang University Hangzhou China College of Control Science and Engineering and the State Key Laboratory of Industrial Control Technology Zhejiang University Key Key Lab of CS&AUS of Zhejiang Province

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Recent works on the pretraining for robot manipulation have demonstrated that representations learning from large human manipulation data can generalize well to new manipulation tasks and environments. However, these approaches mainly focus on human vision or natural language, neglecting tactile feedback. In this article, we make an attempt to explore how to pre-train a representation model for robotic manipulation using both human manipulation visual and tactile data. We develop a system for collecting visual and tactile data, featuring a cost-effective tactile glove to capture human tactile data and Hololens2 for capturing visual data. With this system, we collect a dataset of turning bottle caps. Furthermore, we introduce a novel visual-tactile fusion network and learning strategy M 2 VTP, with one key module to tokenize 20 sparse binary tactile signals sensing touch states for the learning of tactile context and the other key module applying the attention and mask mechanism to the interaction of visual and tactile tokens for visual-tactile representation learning. We utilize our dataset to pre-train the fusion model and embed the pre-trained model into a reinforcement learning framework for downstream tasks. Experimental results demonstrate that our pre-trained model significantly aids in learning manipulation skills. Compared to methods without pre-training, our approach achieves a success rate increase of over 60%. Additionally, when compared to current visual pre-training methods, our success rate exceeds them by more than 50%.

关键词： Representation learning Visualization Natural languages Tactile sensors Reinforcement learning Turning Data models

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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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InterRep: A Visual Interaction Representation for Robotic Grasping

InterRep: A Visual Interaction Representation for Robotic Gr...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yu Cui Qi Ye Qingtao Liu Anjun Chen Gaofeng Li Jiming Chen College of Control Science and Engineering Zhejiang University Hangzhou China College of Control Science and Engineering The State Key Laboratory of Industrial Control Technology Zhejiang University and the Key Key Lab of CS&AUS of Zhejiang Province

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Recently, pre-trained vision models have gained significant attention in motor control, showcasing impressive performance across diverse robotic learning tasks. While previous works predominantly concentrate on the significance of the pre-training phase, the equally important task of extracting more effective representations based on existing pre-trained visual models remains unexplored. To better leverage the representation capabilities of pre-trained models for robotic grasping, we propose InterRep, a novel interaction representation method that possesses not only the strengths of pre-trained models, known for their robustness in noisy environments and their proficiency in recognizing essential features, but also the capacity of capturing dynamic interaction details and local geometric features during the grasping process. Based on the novel representation, we introduce a deep reinforcement learning method to learn generalizable grasping policies. The experimental results demonstrate that our proposed representation outperforms the baselines in terms of both training speed and generalization. For the generalized grasping tasks with dexterous robotic hands, our method boasts a success rate nearly 20% higher than methods using the global features of the entire image from pre-trained models. In addition, our proposed representation method demonstrates promising performance when applied to a different robotic hand and task. It also exhibits excellent performance on real robots with a success rate of 70%.

关键词： Training Visualization Motor drives Shape Grasping Feature extraction Robustness

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The impact of vacancy defective MgH_(2)(001)/(110)surface on the dehydrogenation of MgH_(2)@Ni-CNTs:A mechanistic investigation

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Journal of Materials science & technology 2024年第22期189卷 77-85页

作者： Congwen Duan Xinya Wang Haimei Wang Mengmeng Wu Yuchen Fan Jinhui Wu Ting Qu Bogu Liu Lianxi Hu Poqian Liang Fei Wang Ying Wu Hebei Key Lab of Power Plant Flue Gas Multi-Pollutants Control Department of Environmental Science and EngineeringNorth China Electric Power UniversityBaoding 071003China Institute of Energy Power Innovation North China Electric Power UniversityNo.2 Beinong RoadChangping DistrictBeijing 102206China School of Materials Science and Engineering Harbin Institute of TechnologyHarbin 150001China College of Materials Science and Engineering Sichuan UniversityChengdu 610065China

The vacancy defect exhibits a remarkable improvement in the dehydriding property of MgH_(2)@***,the corresponding mechanism is still not fully ***,the impact of vacancy defects on the dehydrogenation properties of MgH_(2)@Ni-CNTs was studied by DFT simulation,and the corresponding models were constructed based on *** dehydrogenation process of MgH_(2)can be regarded as the dissociation of Mg-H and desorption of H_(2)from the MgH_(2)*** view of the whole dehydrogenation,the dissociation of H^(−)is the rate-determining step,which is the main reason for restricting the dehydrogenation *** with vacancy vacancy-defective MgH_(2)(001)surface,the appearance of vacancy defects on the(110)surface substantially reduces the energy barrier required for H dissociation to 0.070 *** reason is that vacancy defects accelerate the transition of electrons from the H^(−)s orbit to the Mg^(2+)3s orbit,resulting in a decrement of the Mg-H bond strength,which makes H atoms more easily dissociated from the MgH_(2)(110)***,the existence of vacancy defects improves the dehydriding kinetic of MgH_(2).Most importantly,this research offers crucial directions for developing hydrogen storage materials as well as a potential fix for the slow dehydrogenation kinetics of nano-confined MgH_(2).

关键词： MgH_(2) Vacancy defect Hydrogen storage Hydrogen dissociation and desorption

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Research on the maintenance model for launch site ground equipment

Research on the maintenance model for launch site ground equ...

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2022 Chinese Automation Congress, CAC 2022

作者： Yang, Wen Zhang, XiaoBo Fu, YunWei Xu, DongSheng Zhang, JiangPeng Xichang Satellite Launch Center Department of Technology Sichuan Xichang China Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China

ISBN: (纸本)9781665465335

Space launch site is a key infrastructure to support rocket launch. An overview of the characteristics of ground equipment maintenance at the launch site is provided in this paper, along with an analysis of the status of ground equipment maintenance and a discussion of the requirements for equipment maintenance as viewed from the perspective of maintenance staff. The equipment maintenance mode is proposed from the stages of equipment design, development and operation. The preliminary design of the maintenance flow in the operation stage is addressed, and it can be used as a guideline for constructing the ground equipment maintenance information system for the launch site. © 2022 IEEE.

关键词： Maintenance

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state Estimation for Discrete-Time Semi-Markovian Jump Piecewise-Affine Systems with Complex Semi-Markovian Kernel 63

State Estimation for Discrete-Time Semi-Markovian Jump Piece...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Tang, Qi Liu, Tao Sun, Xi-Ming University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Shenzhen Institute of Research and Innovation The University of Hong Kong Hong Kong Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian116024 China Dalian University of Technology School of Control Science and Engineering Dalian116024 China

ISBN: (纸本)9798350316339

This paper addresses state estimation for discretetime semi-Markovian jump piecewise-affine (PWA) systems. To handle uncertainties caused by unknown disturbances, we propose an analysis method that considers all transfer paths between different regions within the same mode. We then examine the mean-square exponential stability and L∞ performance of the error dynamic system by constructing Lyapunov functions that depend on mode, region, and sojourn time. The related criteria are derived using ellipsoidal outer approximation. Based on these criteria, we design L∞ observers. Finally, we demonstrate the applicability of our method through a numerical example. © 2024 IEEE.

关键词： Lyapunov functions

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Learning MPC for Process Dynamic Working Condition Change Tasks under Model Mismatch

Learning MPC for Process Dynamic Working Condition Change Ta...

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American control Conference (ACC)

作者： Guanghui Yang Rui Wang Zuhua Xu Zhijiang Shao State Key Lab of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China

In this study, a learning model predictive control (MPC) algorithm for process dynamic working condition change (DWCC) tasks is proposed. The algorithm continuously compensates for model–plant mismatch (MPM) and improves dynamic performance by predicting multi-step-ahead disturbance from similar DWCC tasks. First, a state-space model augmented by disturbance variables ensures offset-free control for MPM. Second, a dynamic autoencoder is constructed to extract private features from process sequences based on long short-term memory and fully connected networks. DWCC scenarios similar to the current scenario are located from the historical database by calculating the distance between extracted features. Finally, the multi-step-ahead disturbance and its uncertainty representation are predicted through multi-output Gaussian process regression based on the located scenarios. The obtained multi-step-ahead disturbance is incorporated into the state-space MPC framework. A nonlinear case is conducted to demonstrate the effectiveness of the proposed method.

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Efficient learning of robust quadruped bounding usingpretrained neural networks

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IET Cyber-Systems and Robotics 2022年第4期4卷 331-338页

作者： Zhicheng Wang Anqiao Li Yixiao Zheng Anhuan Xie Zhibin Li Jun Wu Qiuguo Zhu Institute of Cyber-Systems and Control Zhejiang UniversityZhejiangChina Intelligent Robot Research Center Zhejiang LabZhejiangChina Department of Computer Science University College LondonLondonUK State Key Laboratory of Industrial Control Technology Zhejiang UniversityZhejiangChina

Bounding is one of the important gaits in quadrupedal locomotion for negotiating *** authors proposed an effective approach that can learn robust bounding gaits more efficiently despite its large variation in dynamic body *** authors first pretrained the neural network(NN)based on data from a robot operated by conventional model-based controllers,and then further optimised the pretrained NN via deep reinforcement learning(DRL).In particular,the authors designed a reward function considering contact points and phases to enforce the gait symmetry and periodicity,which improved the bounding *** NN-based feedback controller was learned in the simulation and directly deployed on the real quadruped robot Jueying Mini successfully.A variety of environments are presented both indoors and outdoors with the authors’*** authors’approach shows efficient computing and good locomotion results by the Jueying Mini quadrupedal robot bounding over uneven *** cover image is based on the Research Article Efficient learning of robust quadruped bounding using pretrained neural networks by Zhicheng Wang et al.,https://***/10.1049/csy2.12062.

关键词： legged locomotion reinforcement learning robot learning

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Torsional Vibration Suppression Method Design for Variable-Speed Wind Turbine Based on UIO-SMC 22

Torsional Vibration Suppression Method Design for Variable-S...

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22nd IEEE International Conference on industrial Informatics, INDIN 2024

作者： Yu, Jiajun Xia, Jinhui Su, Jinya Li, Ze Huang, Wentao The School of Automation Southeast University Nanjing210096 China The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310058 China The College of Electronic and Information Engineering Hebei University Baoding071002 China The School of Internet of Things Engineering Jiangnan University Wuxi214122 China

ISBN: (纸本)9798331527471

With the continuous development of the wind power industry, variable-speed wind turbines (VSWT) are moving towards large-scale, and their applications have been expanded to offshore areas. However, the operating environment of offshore wind farm is even more harsh, and the potential torsional vibration of the drivetrain caused by disturbances in the offshore wind farm may introduce fatigue damage to the VSWT, resulting in unexpected faults that may lead to prolonged shutdown of the VSWT and loss of power generation efficiency. This study proposes a torsional vibration suppression strategy based on unknown input observer and sliding mode control. The proposed suppression strategy provides an appropriate electromagnetic torque compensation for the generator to mitigate disturbances in the whole control system. The effectiveness of the proposed strategy is validated by extensive simulation studies. © 2024 IEEE.

关键词： Sliding mode control

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