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arXiv 2022年

作者： Zhang, Zhuqing Jiao, Yanmei Huang, Shoudong Wang, Yue Xiong, Rong State Key Laboratory of Industrial Control Technology Zhejiang University Zhejiang Hangzhou China Centre for Autonomous Systems Faculty of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia

Drift-free localization is essential for autonomous vehicles. In this paper, we address the problem by proposing a filter-based framework, which integrates the visual-inertial odometry and the measurements of the features in the pre-built map. In this framework, the transformation between the odometry frame and the map frame is augmented into the state and estimated on the fly. Besides, we maintain only the keyframe poses in the map and employ Schmidt extended Kalman filter to update the state partially, so that the uncertainty of the map information can be consistently considered with low computational cost. Moreover, we theoretically demonstrate that the ever-changing linearization points of the estimated state can introduce spurious information to the augmented system and make the original four-dimensional unobservable subspace vanish, leading to inconsistent estimation in practice. To relieve this problem, we employ first-estimate Jacobian (FEJ) to maintain the correct observability properties of the augmented system. Furthermore, we introduce an observability-constrained updating method to compensate for the significant accumulated error after the long-term absence (can be 3 minutes and 1 km) of map-based measurements. Through simulations, the consistent estimation of our proposed algorithm is validated. Through real-world experiments, we demonstrate that our proposed algorithm runs successfully on four kinds of datasets with the lower computational cost (20% time-saving) and the better estimation accuracy (45% trajectory error reduction) compared with the baseline algorithm VINS-Fusion, whereas VINS-Fusion fails to give bounded localization performance on three of four datasets because of its inconsistent estimation. Copyright © 2022, The Authors. All rights reserved.

关键词： Kalman filters

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Zero-Sum Differential Game-Based Fault-Tolerant Tracking control for Nonlinear systems With Multiplicative Failures

Zero-Sum Differential Game-Based Fault-Tolerant Tracking Con...

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Chinese Automation Congress (CAC)

作者： Cong Tan Mingduo Lin Hongbing Xia Bo Zhao Yancai Xu School of Systems Science Beijing Normal University Beijing China Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

A novel fault-tolerant tracking control (FTTC) approach for affine nonlinear systems is developed from the perspective of zero-sum differential games (ZSDG) to deal with unknown multiplicative actuator failures in this paper. By regarding the control input and the multiplicative actuator failure as two players of a zero-sum game, a performance index function reflecting the trajectory tracking error, the control policy and the unknown failure is established. To handle the tracking Hamilton-Jacobi-Isaacs (HJI) equation associated with the established performance index function, a critic neural network is utilized. Then, the Nash equilibrium solution can be obtained, and the FTTC is achieved. The stability of the faulty nonlinear system is examined through Lyapunov-based approach. Finally, simulation results of a spring system is provided to ensure the utility and the realizability of the ZSDG-based FTTC approach.

关键词： Actuators Fault tolerance Trajectory tracking Simulation Fault tolerant systems Stability criteria Mathematical models

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AdvDiffuser: Generating Adversarial Safety-Critical Driving Scenarios via Guided Diffusion

AdvDiffuser: Generating Adversarial Safety-Critical Driving ...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Yuting Xie Xianda Guo Cong Wang Kunhua Liu Long Chen School of Computer Science and Engineering Sun Yat-Sen University School of Computer Science Wuhan University State Key Laboratory for Management and Control of Complex Systems at the Institute of Automation Chinese Academy of Sciences School of Mechanical and Automotive Engineering Qingdao University of Technology

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Safety-critical scenarios are infrequent in natural driving environments but hold significant importance for the training and testing of autonomous driving systems. The prevailing approach involves generating safety-critical scenarios automatically in simulation by introducing adversarial adjustments to natural environments. These adjustments are often tailored to specific tested systems, thereby disregarding their transferability across different systems. In this paper, we propose AdvDiffuser, an adversarial framework for generating safety-critical driving scenarios through guided diffusion. By incorporating a diffusion model to capture plausible collective behaviors of background vehicles and a lightweight guide model to effectively handle adversarial scenarios, AdvDiffuser facilitates transferability. Experimental results on the nuScenes dataset demonstrate that AdvDiffuser, trained on offline driving logs, can be applied to various tested systems with minimal warm-up episode data and outperform other existing methods in terms of realism, diversity, and adversarial performance.

关键词： Training Diffusion models Intelligent robots Autonomous vehicles Testing

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A radial seismic metamaterial filled with auxetic foam for filtering low-frequency Lamb waves and surface waves

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Acta Mechanica Sinica 2025年第2期42卷 1-18页

作者： Li, Pengfei Yang, Fan Hou, Xuanxuan Li, Lingbo Wu, Jiacheng Fan, Hualin School of Aerospace Engineering and Applied Mechanics Tongji University Shanghai China Shanghai Institute of Aircraft Mechanics and Control Tongji University Shanghai China State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment Dalian University of Technology Dalian China Research Center of Lightweight Structures and Intelligent Manufacturing Nanjing University of Aeronautics and Astronautics Nanjing China

This study presents a novel radial seismic metamaterial (RSM) based on auxetic foam, which can achieve omnidirectional ultra-low-frequency suppression of Lamb waves in the range of 0.97 to 21.04 Hz and effectively shield omnidirectional surface waves within 10 Hz. Firstly, the Finite Element method is utilized to investigate the bandgap (BG) characteristics and vibration modes, which shows that the BG can be significantly widened by utilizing the local resonance of the auxetic foam. Subsequently, a three-dimensional radial periodic structure is constructed and the displacement fields of Lamb waves are analyzed in the frequency and time domains. The simulation results demonstrate that the proposed RSM can effectively attenuate Lamb waves. Parametric analysis indicates that filling auxetic foam with a high elastic modulus, low Poisson’s ratio, and low mass density can further expand the BG. For surface waves, the band structure is first analyzed using the acoustic cone method. Furthermore, a radial periodic structure consisting of the RSM cells is analyzed in the frequency domain, and a significant attenuation is observed within the BG. The attenuation capability of the designed RSM is further tested against real seismic wave signals, and a satisfactory shielding effect for low-frequency signals is achieved within 10 Hz. The designed RSM provides new insights into the development of SMs, exhibiting the superior application potential of auxetic foam for seismic wave shielding.

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Mixed Motivation Driven Social Multi-Agent Reinforcement Learning for Autonomous Driving

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IEEE/CAA Journal of Automatica Sinica 2025年第6期12卷 1272-1282页

作者： Long Chen Peng Deng Lingxi Li Xuemin Hu State Key Laboratory of Multimodal Artificial Intelligence Systems and the State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing WAYTOUS Inc. Beijing Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China School of Computer Science and Information Engineering Hubei University Wuhan China Purdue School of Engineering and Technology Indiana University-Purdue University Indianapolis Indianapolis IN USA School of Artificial Intelligence Hubei University Wuhan Key Laboratory of Intelligent Sensing System and Security (Hubei University) Ministry of Education Wuhan China

Despite great achievement has been made in autonomous driving technologies, autonomous vehicles (AVs) still exhibit limitations in intelligence and lack social coordination, which is primarily attributed to their reliance on single-agent technologies, neglecting inter-AV interactions. Current research on multi-agent autonomous driving (MAAD) predominantly focuses on either distributed individual learning or centralized cooperative learning, ignoring the mixed-motive nature of MAAD systems, where each agent is not only self-interested in reaching its own destination but also needs to coordinate with other traffic participants to enhance efficiency and safety. Inspired by the mixed motivation of human driving behavior and their learning process, we propose a novel mixed motivation driven social multi-agent reinforcement learning method for autonomous driving. In our method, a multi-agent reinforcement learning (MARL) algorithm, called Social Learning Policy Optimization (SoLPO), which takes advantage of both the individual and social learning paradigms, is proposed to empower agents to rapidly acquire self-interested policies and effectively learn socially coordinated behavior. Based on the proposed SoLPO, we further develop a mixed-motive MARL method for autonomous driving combined with a social reward integration module that can model the mixed-motive nature of MAAD systems by integrating individual and neighbor rewards into a social learning objective for improved learning speed and effectiveness. Experiments conducted on the MetaDrive simulator show that our proposed method outperforms existing state-of-the-art MARL approaches in metrics including the success rate, safety, and efficiency. More-over, the AVs trained by our method form coordinated social norms and exhibit human-like driving behavior, demonstrating a high degree of social coordination.

关键词： Measurement Reinforcement learning Safety Driver behavior Autonomous vehicles Optimization

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Smart Contract Service Optimization in Blockchain-Cloud Collaborative Computing

Smart Contract Service Optimization in Blockchain-Cloud Coll...

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International Conference on Mobile Data Management

作者： Ji Wan Kai Hu Jie Li Hao Su Qingshun Wu Mingyuan Li Libo Feng Yan Pan State Key Laboratory of Software Development Environment Beihang University Beijing China Yunnan Key Laboratory of Blockchain Application Technology Beihang Yunnan Innovation Institute Kunming China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China The First Affiliated Hospital of Zhengzhou University Zhengzhou China National Pilot School of Software Yunnan University Kunming China Institute of Software China Industrial Control Systems Cyber Emergency Response Team Beijing China

Smart contract is a trusted service provided on the blockchain, while cloud service is a traditional service mode with a large number of resources. The combination of the blockchain and cloud service is of great significance to the trusted expansion of services and the access to services inside and outside the blockchain. In this paper, we study the smart contract extension service in blockchain-cloud collaborative computing. The service module decoupling method of smart contract is proposed, and the parallel execution algorithm of smart contract service is designed, which improves the execution efficiency of smart contract service. Finally, this paper designs a secure data interaction method of smart contract and cloud service, which helps to maintain the data consistency between cloud computing and blockchain. The experimental results show that the proposed method can save at most 42.13% of the running time, and it can promote the data consistency between the cloud service and the blockchain.

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Towards Efficient Trajectory Generation for Ground Robots beyond 2D Environment

arXiv

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arXiv 2023年

作者： Wang, Jingping Xu, Long Fu, Haoran Meng, Zehui Xu, Chao Cao, Yanjun Lyu, Ximin Gao, Fei State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China Huzhou Institute Zhejiang University Huzhou313000 China School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen518107 China Applicaion Innovation Laboratory Huawei Technologies Co. Ltd China

With the development of robotics, ground robots are no longer limited to planar motion. Passive height variation due to complex terrain and active height control provided by special structures on robots require a more general navigation planning framework beyond 2D. Existing methods rarely considers both simultaneously, limiting the capabilities and applications of ground robots. In this paper, we proposed an optimization-based planning framework for ground robots considering both active and passive height changes on the z-axis. The proposed planner first constructs a penalty field for chassis motion constraints defined in R3 such that the optimal solution space of the trajectory is continuous, resulting in a high-quality smooth chassis trajectory. Also, by constructing custom constraints in the z-axis direction, it is possible to plan trajectories for different types of ground robots which have z-axis degree of freedom. We performed simulations and real-world experiments to verify the efficiency and trajectory quality of our algorithm. Copyright © 2023, The Authors. All rights reserved.

关键词： Trajectories

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Moving Target Shooting control Policy Based on Deep Reinforcement Learning

Moving Target Shooting Control Policy Based on Deep Reinforc...

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2021 International Conference on Information, cybernetics, and Computational Social systems, ICCSS 2021

作者： Li, Boyu Jin, Tao Zhu, Yuanheng Li, Haoran Wu, Yingnian Zhao, Dongbin Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China Beijing Information Science and Technology University School of Automation Beijing100192 China

ISBN: (纸本)9781665402453

Robots are playing a more and more important role in people's production and life, recently. However, robot control in dynamic environment is still a difficulty. With the great breakthrough of deep reinforcement learning in the field of video games, this method is also extended to the field of robots. Due to the gap between the simulation environment and the real environment, the deep reinforcement learning algorithm trained in the simulation environment is difficult to be applied to the real environment. Aiming at the gimbal control with two degrees of freedom (DOF), a pipline combining system identification and deep reinforcement learning is proposed. On the one hand, the shooting accuracy of the gimbal to moving objects is improved through deep reinforcement learning algorithm. On the other hand, the gap between simulation and reality is reduced through system identification. The method is verified in the RoboMaster University AI Challenge (RMUA) shooting system. The results show that the shooting accuracy is better than the classical control method. © 2021 IEEE.

关键词： Deep learning

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Origami-Inspired Metamaterial with Cpmpression-Twist Coupling Effect For Low-Frequency Vibration Isolation

SSRN

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SSRN 2023年

作者： Liu, Xiangbing Zhang, Kai Shi, Haizhou Hong, Fang Liu, Hong Deng, Zichen School of Mechanics Civil Engineering and Architecture Northwestern Polytechnical University Xi’an710072 China MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical University Xi’an710072 China State Key Laboratory of Structural Analysis for Industrial Equipment Dalian University of Technology Dalian116024 China

Through unique folding methods and material selection, origami structures enable optimum structural design within a restricted space and provide strong force support. In aerospace engineering, origami-inspired structures are used in designing satellites, probes and other space devices for lightweight, compact and adjustable design requirements. A compression-twist coupled metamaterial inspired by Kresling origami structure consisting of beams and plates is presented in this study, and the vibration response of the structure is investigated. The bandgaps of the structure along longitudinal and transverse directions are studied separately due to the application of the polarization factor. The vibrational modes of the structure are further investigated to elucidate the mechanism of bandgap generation. Parametric studies make the optimal design of bandgap in graded structures possible. Finally, experiments are carried out to verify the correctness of the structural dynamic response. This research provides a new perspective on the design of origami-inspired metamaterial structures in terms of dynamics control and bandgap tuning. © 2023, The Authors. All rights reserved.

关键词： Energy gap

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Differentially Private Gradient-Tracking-Based Distributed Stochastic Optimization over Directed Graphs

arXiv

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arXiv 2025年

作者： Chen, Jialong Wang, Jimin Zhang, Ji-Feng Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education Beijing100083 China School of Automation and Electrical Engineering Zhongyuan University of Technology Zheng Zhou450007 China

This paper proposes a new differentially private gradient-tracking-based distributed stochastic optimization algorithm over directed graphs. Specifically, privacy noises are added to each agent’s state and tracking variable to prevent information leakage, and then perturbed states and tracking variables are transmitted to neighbors. We design two novel schemes of the iteration step-sizes and the sampling number for the algorithm. By using the sampling parameter-controlled subsampling method, both schemes enhance the differential privacy level, and achieve the finite cumulative privacy budget even over infinite iterations. The convergence rate of the algorithm is shown for both nonconvex with the Polyak-Lojasiewicz condition and strongly convex objectives: Scheme (S1) achieves the polynomial convergence rate, and Scheme (S2) achieves the exponential convergence rate. The trade-off between the privacy and the convergence rate is presented. The algorithm’s effectiveness and superior performance over the existing works are demonstrated through numerical examples of distributed training on benchmark datasets"MNIST" and"CIFAR-10". Copyright © 2025, The Authors. All rights reserved.

关键词： Differential privacy

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