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arXiv 2024年

作者： Liu, Yifan Wang, You Li, Guang State Key Laboratory of Industrial Control Technology Institute of Cyber Systems and Control Zhejiang University Hangzhou310027 China

Model Predictive control (MPC)-based trajectory planning has been widely used in robotics, and incorporating control Barrier Function (CBF) constraints into MPC can greatly improve its obstacle avoidance efficiency. Unfortunately, traditional optimizers are resource-consuming and slow to solve such non-convex constrained optimization problems (COPs) while learning-based methods struggle to satisfy the non-convex constraints. In this paper, we propose SOMTP algorithm, a self-supervised learning-based optimizer for CBF-MPC trajectory planning. Specifically, first, SOMTP employs problem transcription to satisfy most of the constraints. Then the differentiable SLPG correction is proposed to move the solution closer to the safe set and is then converted as the guide policy in the following training process. After that, inspired by the Augmented Lagrangian Method (ALM), our training algorithm integrated with guide policy constraints is proposed to enable the optimizer network to converge to a feasible solution. Finally, experiments show that the proposed algorithm has better feasibility than other learning-based methods and can provide solutions much faster than traditional optimizers with similar optimality. Copyright © 2024, The Authors. All rights reserved.

关键词： Model predictive control

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Drift-free localisation using prior cross-source map for indoor low-light environments

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IET cyber-systems and Robotics 2023年第1期5卷 122-131页

作者： Junyi Tao Weichen Dai Da Kong Jiayan Wan Bin He Yu Zhang State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhouChina School of Computer Science Hangzhou Dianzi UniversityHangzhouChina Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province HangzhouChina

In this study,a localisation system without cumulative errors is ***,depth odometry is achieved only by using the depth information from the depth *** the point cloud cross-source map registration is realised by 3D particle filtering to obtain the pose of the point cloud relative to the ***,we fuse the odometry results with the point cloud to map registration results,so the system can operate effectively even if the map is *** effectiveness of the system for long-term localisation,localisation in the incomplete map,and localisation in low light through multiple experiments on the self-recorded dataset is *** with other methods,the results are better than theirs and achieve high indoor localisation accuracy.

关键词： indoor positioning robots

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Attention Markets of Blockchain-Based Decentralized Autonomous Organizations

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1370-1380页

作者： Juanjuan Li Rui Qin Sangtian Guan Wenwen Ding Fei Lin Fei-Yue Wang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China IEEE

The attention is a scarce resource in decentralized autonomous organizations(DAOs),as their self-governance relies heavily on the attention-intensive decision-making process of“proposal and voting”.To prevent the negative effects of pro-posers’attention-capturing strategies that contribute to the“tragedy of the commons”and ensure an efficient distribution of attention among multiple proposals,it is necessary to establish a market-driven allocation scheme for DAOs’***,the Harberger tax-based attention markets are designed to facilitate its allocation via continuous and automated trading,where the individualized Harberger tax rate(HTR)determined by the pro-posers’reputation is ***,the Stackelberg game model is formulated in these markets,casting attention to owners in the role of leaders and other competitive proposers as *** equilibrium trading strategies are also discussed to unravel the intricate dynamics of attention ***,utilizing the single-round Stackelberg game as an illustrative example,the existence of Nash equilibrium trading strategies is ***,the impact of individualized HTR on trading strategies is investigated,and results suggest that it has a negative correlation with leaders’self-accessed prices and ownership duration,but its effect on their revenues varies under different *** study is expected to provide valuable insights into leveraging attention resources to improve DAOs’governance and decision-making process.

关键词： Attention decentralized autonomous organizations Harberger tax Stackelberg game.

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Application Design of Virtual Immersion Command Information System 9

Application Design of Virtual Immersion Command Information ...

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9th International Conference on Big Data and Information Analytics, BigDIA 2023

作者： Liu, Renjie Chen, Hongli Wang, Mingyang Zhu, Jian Zhang, Zhenqian Zhang, Ming Beijing Institute of Control and Electronic Technology Key Laboratory of Information System and Technology Beijing China China Industrial Control Systems Cyber Emergency Response Team Laboratory of Artificial Intelligence Integration Development and Security Application Beijing China

ISBN: (纸本)9798350330076

Based on the existing resources of general command information system, the virtually immersive display system is constructed. In this paper, the application design of virtual and augmented reality display system was discussed. The main contents included the overall system design, business requirements, application design of virtual reality (VR) and augmented reality (AR) table display system, expected results, and equipment selection. The deployment and application of VR and AR display system were introduced. Therefore, the hardware selection of VR and AR were HTC Vive and XLOOG X200 products, respectively. The Unreal Engine 4 was selected as the software development platform. This solution meets the current immersive display requirements. The system integrates VR and AR display application capabilities and can fully serve the command and control requirements in the future virtual field. © 2023 IEEE.

关键词： Virtual reality

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SOPA-GA-CNN: Synchronous optimisation of parameters and architectures by genetic algorithms with convolutional neural network blocks for securing industrial Internet-of-Things

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IET cyber-systems and Robotics 2023年第1期5卷 51-66页

作者： Jia-Cheng Huang Guo-Qiang Zeng Guang-Gang Geng Jian Weng Kang-Di Lu College of Cyber Security and the National Joint Engineering Research Center of Network Security Detection and Protection Technology Jinan UniversityGuangzhouChina National-Local Joint Engineering Research Center of Digitalize Electrical Design Technology Wenzhou UniversityWenzhouChina National Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang UniversityHangzhouChina

In recent years,deep learning has been applied to a variety of scenarios in industrial Internet of Things(IIoT),including enhancing the security of ***,the existing deep learning methods utilised in IIoT security are manually designed by heavily relying on the experience of the *** authors have made the first contribution concerning the joint optimisation of neural architecture search and hyper-parameters optimisation for securing IIoT.A novel automated deep learning method called synchronous optimisation of parameters and architectures by GA with CNN blocks(SOPA-GA-CNN)is proposed to synchronously optimise the hyperparameters and block-based architectures in convolutional neural networks(CNNs)by genetic algorithms(GA)for the intrusion detection issue of *** efficient hybrid encoding strategy and the corresponding GA-based evolutionary operations are designed to characterise and evolve both the hyperparameters,including batch size,learning rate,weight optimiser and weight regularisation,and the architectures,such as the block-based network topology and the parameters of each CNN *** experimental results on five intrusion detection datasets in IIoT,including secure water treatment,water distribution,Gas Pipeline,Botnet in Internet of Things and Power System Attack Dataset,have demonstrated the superiority of the proposed SOPA-GA-CNN to the state-of-the-art manually designed models and neuron-evolutionary methods in terms of accuracy,precision,recall,F1-score,and the number of parameters of the deep learning models.

关键词： cyber-systems deep learning deep neural network Internet of things swarm intelligence

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MOVE: Multi-skill Omnidirectional Legged Locomotion with Limited View in 3D Environments

arXiv

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arXiv 2024年

作者： Li, Songbo Luo, Shixin Wu, Jun Zhu, Qiuguo Institute of Cyber-Systems and Control Zhejiang University 310027 China State Key Laboratory of Industrial Control Technology 310027 China

Legged robots possess inherent advantages in traversing complex 3D terrains. However, previous work on low-cost quadruped robots with egocentric vision systems has been limited by a narrow front-facing view and exteroceptive noise, restricting omnidirectional mobility in such environments. While building a voxel map through a hierarchical structure can refine exteroception processing, it introduces significant computational overhead, noise, and delays. In this paper, we present MOVE, a one-stage end-to-end learning framework capable of multi-skill omnidirectional legged locomotion with limited view in 3D environments, just like what a real animal can do. When movement aligns with the robot's line of sight, exteroceptive perception enhances locomotion, enabling extreme climbing and leaping. When vision is obstructed or the direction of movement lies outside the robot's field of view, the robot relies on proprioception for tasks like crawling and climbing stairs. We integrate all these skills into a single neural network by introducing a pseudo-siamese network structure combining supervised and contrastive learning which helps the robot infer its surroundings beyond its field of view. Experiments in both simulations and real-world scenarios demonstrate the robustness of our method, broadening the operational environments for robotics with egocentric vision. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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A Time-delay Approach for Stabilization of Linear systems with Unknown control Direction

A Time-delay Approach for Stabilization of Linear Systems wi...

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IEEE Conference on Decision and control

作者： Gaofeng Pan Yang Zhu Zhengguang Wu State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou 310027 China and Ningbo Innovation Center Zhejiang University Ningbo China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We propose a novel stability analysis for linear systems with unknown control directions, focusing on stabilization through extremum seeking to minimize a Lyapunov function. Our approach draws inspiration from Lie bracket systems, employing a transformation using time-delay analysis to convert the closed-loop system into a nominal time-delay system. Leveraging techniques from Input-to-state Stability (ISS), we provide a proof of semiglobal practical asymptotic stability. Compared to the preceding analysis based on Lie bracket systems, the use of time-delay methods transforms intricate mathematical discussions and proofs into a structured computational framework, facilitating a more intuitive understanding. Also, we propose a method for selecting controller parameters and can provide a more comprehensive explanation for the current high-gain parameter choices. Finally, we present a new control parameter to reduce the requirements of the frequency of the sinusoidal signals.

关键词： Linear systems Asymptotic stability Focusing Transforms Stability analysis Robustness Closed loop systems Frequency control Lyapunov methods

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Toward Understanding key Estimation in Learning Robust Humanoid Locomotion

arXiv

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arXiv 2024年

作者： Wang, Zhicheng Wei, Wandi Yu, Ruiqi Wu, Jun Zhu, Qiuguo Institute of Cyber-Systems and Control Zhejiang University 310027 China State Key Laboratory of Industrial Control Technology 310027 China

Accurate state estimation plays a critical role in ensuring the robust control of humanoid robots, particularly in the context of learning-based control policies for legged robots. However, there is a notable gap in analytical research concerning estimations. Therefore, we endeavor to further understand how various types of estimations influence the decision-making processes of policies. In this paper, we provide quantitative insight into the effectiveness of learned state estimations, employing saliency analysis to identify key estimation variables and optimize their combination for humanoid locomotion tasks. Evaluations assessing tracking precision and robustness are conducted on comparative groups of policies with varying estimation combinations in both simulated and real-world environments. Results validated that the proposed policy is capable of crossing the sim-to-real gap and demonstrating superior performance relative to alternative policy configurations. © 2024, CC BY.

关键词： Anthropomorphic robots

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Doped-nitrogen enhanced the performance of Nb_(2)CT_(x)on the electrocatalytic synthesis of H_(2)O_(2)

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Nano Research 2023年第5期16卷 6120-6127页

作者： Shijie Zhang Jingnan Zheng Junming Lin Yuanan Li Zhikang Bao Xiaoge Peng Wenkai Ji Lei Ding Zaixiang Xu Guoliang Wang Xing Zhong Jianguo Wang Institute of Industrial Catalysis State Key Laboratory Breeding Base of Green-Chemical Synthesis TechnologyCollege of Chemical EngineeringZhejiang University of TechnologyHangzhou 310014China Zhejiang Center for Disease Control and Prevention Hangzhou 310051China

Electrochemical oxygen reduction reaction(ORR)for preparing hydrogen peroxide(H_(2)O_(2))is a promising way to replace the anthraquinone *** key to H_(2)O_(2)production is the development of catalysts to regulate the oxygen reduction reaction ***,nitrogen-doped Nb_(2)CT_(x)was prepared by NH3 annealing *** with precursor Nb_(2)AlC(67.01%)and pure Nb_(2)CT_(x)(75.70%),nitrogen-doped Nb_(2)CT_(x)exhibited excellent performance for 2e−ORR with>90%H_(2)O_(2)selectivity(at 0.4 V *** hydrogen electrode(RHE)).Faradic efficiency of nitrogen-doped Nb_(2)CT_(x)reached 80.75%,whereas those for Nb2AlC and Nb_(2)CT_(x)were 60.35%and 39.27%,respectively.A desirable catalytic stability for 50 h was *** functional theory(DFT)calculations indicated excellent activity of the nitrogen-doped Nb_(2)CT_(x)was attributed to the introduction of *** excellent activity was conducive to the adsorption of oxygen and promoted the formation of the OOH *** work can serve as an important reference for regulating the electronic structure of MXene to expand the application area in the electrochemical field.

关键词： Nb_(2)CT_(x) nitrogen-doped electrocatalysis oxygen reduction reaction hydrogen peroxide

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PIE: Parkour with Implicit-Explicit Learning Framework for Legged Robots

arXiv

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arXiv 2024年

作者： Luo, Shixin Li, Songbo Yu, Ruiqi Wang, Zhicheng Wu, Jun Zhu, Qiuguo Institute of Cyber-Systems and Control Zhejiang University 310027 China State Key Laboratory of Industrial Control Technology 310027 China

Parkour presents a highly challenging task for legged robots, requiring them to traverse various terrains with agile and smooth locomotion. This necessitates comprehensive understanding of both the robot’s own state and the surrounding terrain, despite the inherent unreliability of robot perception and actuation. Current state-of-the-art methods either rely on complex pre-trained high-level terrain reconstruction modules or limit the maximum potential of robot parkour to avoid failure due to inaccurate perception. In this paper, we propose a one-stage end-to-end learning-based parkour framework: Parkour with Implicit-Explicit learning framework for legged robots (PIE) that leverages dual-level implicit-explicit estimation. With this mechanism, even a low-cost quadruped robot equipped with an unreliable egocentric depth camera can achieve exceptional performance on challenging parkour terrains using a relatively simple training process and reward function. While the training process is conducted entirely in simulation, our real-world validation demonstrates successful zero-shot deployment of our framework, showcasing superior parkour performance on harsh terrains. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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