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arXiv 2024年

作者： Yu, Ruiqi Wang, Qianshi Wang, Yizhen 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

Traversing risky terrains with sparse footholds presents significant challenges for legged robots, requiring precise foot placement in safe areas. To acquire comprehensive exteroceptive information, prior studies have employed motion capture systems or mapping techniques to generate heightmap for locomotion policy. However, these approaches require specialized pipelines and often introduce additional noise. While depth images from egocentric vision systems are cost-effective, their limited field of view and sparse information hinder the integration of terrain structure details into implicit features, which are essential for generating precise actions. In this paper, we demonstrate that end-to-end reinforcement learning relying solely on proprioception and depth images is capable of traversing risky terrains with high sparsity and randomness. Our method introduces local terrain reconstruction, leveraging the benefits of clear features and sufficient information from the heightmap, which serves as an intermediary for visual feature extraction and motion generation. This allows the policy to effectively represent and memorize critical terrain information. We deploy the proposed framework on a low-cost quadrupedal robot, achieving agile and adaptive locomotion across various challenging terrains and showcasing outstanding performance in real-world scenarios. Video at: ***/Rj9v5EZsn-M. Copyright © 2024, The Authors. All rights reserved.

关键词： Multipurpose robots

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Learning adaptive manipulation of objects with revolute joint: A case study on varied cabinet doors opening

Learning adaptive manipulation of objects with revolute join...

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第42届中国控制会议

作者： Hongxiang Yu Dashun Guo Zhongxiang Zhou Yue Wang Rong Xiong the State Key Laboratory of Industrial Control and Technology and the Institute of Cyber-Systems and Control Zhejiang University

This paper introduces a learning-based framework for robot adaptive manipulating the object with a revolute joint in unstructured environments. We concentrate our discussion on various cabinet door opening tasks. To improve the performance of Deep Reinforcement Learning in this scene, we analytically provide an efficient sampling manner utilizing the constraints of the objects. To open various kinds of doors, we add encoded environment parameters that define the various environments to the input of out policy. To transfer the policy into the real world, we train an adaptation module in simulation and fine-tune the adaptation module to cut down the impact of the policy-unaware environment parameters. We design a series of experiments to validate the efficacy of our framework. Additionally, we testify to the model's performance in the real world compared to the traditional door opening method.

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control of Antagonistic McKibben Muscles via a Bio-inspired Approach

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Journal of Bionic Engineering 2022年第6期19卷 1771-1789页

作者： Xinyi Chen Wenxin Zhu Wenyu Liang Yilin Lang Qinyuan Ren State Key Laboratory of Industrial Control Technology Zhejiang University310027HangzhouZhejiangChina Institute for Infocomm Research A^(*)STAR138632SingaporeSingapore

McKibben muscles are increasingly used in many robotic applications due to their advantages of lightweight,compliant,and skeletal muscles-like ***,there are still huge challenges in the motion control of McKibben muscles due to the system nonlinearity(e.g.,hysteresis)and model *** investigate the control issues,a soft artificial arm actuated by an antagonistic pair of McKibben muscles,mimicking the biological structure of skeleton-muscle systems,is *** by the biological motor control capability that humans can control and coordinate a group of muscles to achieve complex motions,a cerebellum-like controller based on Spiking Neural Networks(SNNs)is employed for the motion control of the developed artificial *** from the employment of the SNN-based cerebellar model,the proposed control scheme provides online adaptive learning capability,good computational efficiency,fast response,and strong ***,several simulations and experiments are conducted subject to different environmental *** simulation and experimental results verify that the proposed method can achieve good tracking performance,adaptability,and strong robustness.

关键词： McKibben muscle Cerebellum-like controller Spiking Neural Network Soft artificial arm Adaptive control

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Review of data security within energy blockchain:A comprehensive analysis of storage,management,and utilization

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High-Confidence Computing 2024年第3期4卷 82-102页

作者： Yunhua He Zhihao Zhou Yan Pan Fahui Chong Bin Wu Ke Xiao Hong Li School of Information Science and Technology North China University of TechnologyBeijing 100144China China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China Key Laboratory in Blockchain and Data Security by Ministry of Industry and Information Technology Beijing 100040China Institute of Information Engineering Chinese Academy of Sciences Beijing 100195China

Energy systems are currently undergoing a transformation towards new paradigms characterized by decarbonization,decentralization,democratization,and *** this evolving context,energy blockchain,aiming to enhance efficiency,transparency,and security,emerges as an integrated technological solution designed to address the diverse challenges in this *** security is essential for the reliable and efficient functioning of energy *** pressing need to address challenges related to secure data storage,effective data management,and efficient data utilization is increasingly *** paper offers a comprehensive survey of academic discourse on energy blockchain data security over the past five years,adopting an all-encompassing perspective that spans data storage,management,and *** work systematically evaluates and contrasts the strengths and weaknesses of various research ***,this paper proposes an integrated hierarchical on-chain and off-chain security energy blockchain architecture,specifically designed to meet the complex security requirements of multi-blockchain business ***,this paper identifies key directions for future research,particularly in advancing the integration of storage,management,and utilization of energy blockchain data security.

关键词： Energy blockchain Data security Storage Management Utilization

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Spatio-temporal decision-making and trajectory planning framework with flexible constraints in closed-loop dynamic traffic

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IET Intelligent Transport systems 2023年第4期17卷 704-715页

作者： Zhang, Ting Fu, Mengyin Liu, Tong Song, Wenjie State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China Nanjing University of Science and Technology Nanjing China

Driving in interactive dynamic traffic is a huge challenge for autonomous vehicles, especially for motion planning. The autonomous vehicle not only needs to predict the future states of the social vehicles to avoid a collision but also realizes the comfort and continuous plan. To deal with the problem, a spatio-temporal decision-making and motion planning framework with flexible constraints based on previous work is proposed. Improvements can be highlighted in three aspects. First, a neural network for trajectory prediction is trained and it is integrated into the framework, which can learn the social vehicles' reactions in the negotiation better and give more accurate predictions. Second, instead of setting the fixed local targets, the flexible-constraint mechanism is introduced to increase the success rate of continuous planning, specifically, the local planning target is determined conditioned on the situation with an elastic factor. Third, two popular simulators with randomly disposed traffic participants are exploited, where vehicles are mutually influenced, more similar to real-world traffic. The closed-loop tests with zero crashes and the comparative experiments with traditional models demonstrate the superior performance and practical significance of the proposed framework. © 2022 The Authors. IET Intelligent Transport systems published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and technology.

关键词： Autonomous vehicles

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Multi-objective optimization for 10-kW rated power dynamic wireless charging systems of electric vehicles

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Science China(Information Sciences) 2022年第10期65卷 178-195页

作者： Ze ZHOU Zhitao LIU Hongye SU Liyan ZHANG State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University School of Automation Wuhan University of Technology

In this paper, we propose a method to optimize the output power, efficiency, and cost of the dynamic wireless charging(DWC) system of electric vehicles by using the transmitting coil spacing as the decision variable. In a set of transmitting equipment, we adopt a structure with two transmitting networks in parallel and derive loss models. The expressions of the output power and efficiency of the DWC system are obtained by data fitting. In addition, combined with the proposed cost function, we construct a multiobjective optimization problem on output power, efficiency, and cost. Due to the complexity of the objective function, it is difficult to solve the problem by the analytic method, and thus we propose a constrained adaptive particle swarm optimization(CAPSO) algorithm with high accuracy to solve the problem. Finally,the simulation results verify the feasibility of the proposed model. The advantages of the proposed CAPSO algorithm and the optimization results under different weight combinations are presented.

关键词： dynamic wireless charging electric vehicles multi-objective optimization constrained adaptive particle swarm optimization

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Safe Corridor Constrained Trajectory Planning for Formation Flight of Fixed-Wing Unmanned Aerial Vehicles 1

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Ji, Jialin Peng, Yiyang Zhao, Changchun Xu, Guangtong Xu, Chao Gao, Fei State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China Huzhou Institute Zhejiang University Huzhou313000 China Shanghai Aerospace Control Technology Institute Shanghai201109 China

ISBN: (数字)9789819733408

ISBN: (纸本)9789819733392

Autonomous formation flight of fixed-wing unmanned aerial vehicles (UAVs) in confined environments still remains an open problem. To tackle this challenge, this paper proposes a safe corridor constrained trajectory planning method for formation flight. A novel safe corridor construct method is designed to generate collision-free space in narrow passage. Then, the formation keeping constraint is described by using Laplacian matrix, and the similarity deviation between current formation and predefined formation is used as the cost function in the optimization problem. The formation flight trajectory planning problem is formulated via MINCO, a powerful spatial-temporal joint trajectory representation method, to efficiently generate dynamically feasible trajectory for fixed-wing UAVs. Numerical simulations on formation flight scenarios are implemented, and the results validate the effectiveness and efficiency of the safe corridor constrained trajectory planning method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Antennas

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Adaptive control of Nonlinear systems with Sinusoidal Uncertainties via Internal Model Principle

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IFAC-PapersOnLine 2023年第1期56卷 270-275页

作者： Qi Han Lei Wang Lorenzo Marconi Zhitao Liu Hongye Su State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University China CASY - DEI University of Bologna Italy

In this paper, an adaptive control scheme is established for nonlinear systems with sinusoidal uncertainties from the perspective of internal model principle. We propose to embed the controller with a dynamic compensator which plays the role of an internal model for reproducing the unknown sinusoidal parameter. For linearly parameterized systems, under some full-information stabilizability assumptions, internal-model-based controllers are developed, rendering the closed-loop system trajectories to be bounded and the plant states to be asymptotically convergent to zero. Further, we extend the proposed internal-model-based control scheme to nonlinearly parameterized systems with a (strictly) monotonic assumption. Two simulation examples are given to verify the effectiveness of the proposed schemes.

关键词： Adaptive control internal model sinusoidal parameter

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Yolo-S: Texture-Less Object Pose Estimation with Shape Prior 13

Yolo-S: Texture-Less Object Pose Estimation with Shape Prior

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13th IEEE Data Driven control and Learning systems Conference, DDCLS 2024

作者： Wang, Danzhu Liu, Shan Polytechnic Institute Zhejiang University Hangzhou310015 China College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China

ISBN: (纸本)9798350361674

This paper aims to address the challenge of pose estimation for texture-less objects under conditions of stacking, occlusion, and complex backgrounds. The proposed method, called Yolo-S, consists of two parts: a pre-estimation network based on Yolov8 backbone and a key point post-refinement network based on Graph Neural Network. The first part uses off-the-shelf detectors result as inputs (e.g. MaskRCNN) and outputs the pre-estimate keypoints at the pixel coordinates. The second part encodes the roughly estimated keypoints and the priori knowledge of the object's CAD model, uses a graph neural network to intergrate feature and predict the final keypoints. PNP method is used to obtain the object's pose. The main advantage of Yolo-S is that is has a prior knowledge embedded GNN model for estimating the pose of texture-less objects, which greatly improves prediction quality and accuracy and achieves zero-shot sim-to-real model transfer. Experimental results on the LINEMOD dataset demonstrate the effectiveness of the proposed method and its significant competitiveness with other state-of-the-art methods. © 2024 IEEE.

关键词： Graph neural networks

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Social Radars: Finding Targets in cyberspace for cybersecurity

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 279-282页

作者： Lili Fan Changxian Zeng Yutong Wang Jiaqi Ma Fei-Yue Wang School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China School of Science Dalian Minzu UniversityDalian 116600China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Department of Civil and Environmental Engineering University of CaliforniaLos AngelesCA 90024 USA IEEE 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

INSPIRED by the insight from American political scientist Lasswell, who summarized the environmental role in societal surveillance [1], Schramm coined the term “social radar” [2] as it resembles the activities of radar in collecting and processing information, playing a crucial role in helping humans perceive changes in the internal and external environment and promptly adjusting adaptive behaviors.

关键词： prompt summarized insight

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