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IEEE International Conference on Robotics and Automation (ICRA)

作者： Lun Quan Zhiwei Zhang Xingguang Zhong Chao Xu Fei Gao State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Huzhou Institute Zhejiang University Huzhou China National Engineering Research Center for Industrial Automation (Ningbo Institute) Ningbo China

The quadrotor is popularly used in challenging environments due to its superior agility and flexibility. In these scenarios, trajectory planning plays a vital role in generating safe motions to avoid obstacles while ensuring flight smoothness. Although many works on quadrotor planning have been proposed, a research gap exists in incorporating self-adaptation into a planning framework to enable a drone to automatically fly slower in denser environments and increase its speed in a safer area. In this paper, we propose an environmental adaptive planner to adjust the flight aggressiveness effectively based on the obstacle distribution and quadrotor state. Firstly, we design an environmental adaptive safety aware method to assign the priority of the surrounding obstacles according to the environmental risk level and instantaneous motion tendency. Then, we apply it into a multi-layered model predictive contouring control (Multi-MPCC) framework to generate adaptive, safe, and dynamical feasible local trajectories. Extensive simulations and real-world experiments verify the efficiency and robustness of our planning framework. Benchmark comparison also shows superior performances of our method with another advanced environmental adaptive planning algorithm. Moreover, we release our planning framework as open-source ros-packages¹ .

关键词： Adaptation models Trajectory planning Heuristic algorithms Predictive models Prediction algorithms Robustness Planning

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Dynamic Multi-Robot Path Planning in Narrow-Lane Environments with One-Way Constraint

Dynamic Multi-Robot Path Planning in Narrow-Lane Environment...

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International Conference on Intelligent Autonomous systems (ICoIAS)

作者： Zhenyu Song Ronghao Zheng Senlin Zhang Meiqin Liu College of Electrical Engineering Zhejiang University Hangzhou China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xian China

With the rapid development of the logistics industry, the path planning problem for automated guided vehicles (AGVs) in warehousing systems has become a prominent research topic. Traditional multi-robot path planning algorithms often perform poorly in narrow-lane environments found in warehouses. This paper proposes an improved dynamic multi-robot path planning algorithm specifically tailored for narrow-lane environments. By constraining the traversal directions within narrow lanes and dynamically determining the optimal directions, this algorithm achieves efficient multi-robot path planning and enhances the quality of robot paths. The effectiveness of the algorithm is theoretically proven in this paper. Extensive simulation data demonstrates that our algorithm efficiently generates high-quality paths in high-density narrow-lane warehouse environments.

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Few-shot Underwater Acoustic Target Recognition Based on Siamese Network

Few-shot Underwater Acoustic Target Recognition Based on Sia...

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Chinese control Conference (CCC)

作者： Haizhou Yang Meiqin Liu Senlin Zhang Ronghao Zheng Shanling Dong State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China College of Electrical Engineering Zhejiang University Hangzhou China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China

Underwater acoustic target recognition is a widely investigated issue in the field of underwater acoustics. Many good results have been reported for underwater acoustic target recognition. However, in practical applications, the strong demand for labeled data for underwater acoustic target recognition is a big obstacle. In order to solve this problem, researchers have explored few-shot learning and unsupervised methods in various papers. A Siamese network is proposed which is composed of one-dimensional convolution and Long Short-Term Memory (LSTM) neural networks, called 1DCLSN. A structure for 1DCLSN is designed which combines contrastive information with label information and obtains satisfactory recognition results. In addition, the contrastive loss function with a different clustering term is modified to improve the performance. With only few labeled training samples, the performance of the proposed approach is better than those of other deep learning methods. The experiment shows the great potential of our method.

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Research Progress of Parallel control and Management

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IEEE/CAA Journal of Automatica Sinica 2020年第2期7卷 355-367页

作者： Gang Xiong Xisong Dong Hao Lu Dayong Shen State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Computer Science and Technology Beijing Institute of TechnologyBeijing 100081China College of Systems Engineering National University of Defense TechnologyChangsha 410073China

Based on ACP(artificial systems,computational experiments,and parallel execution)methodology,parallel control and management has become a popularly systematic and complete solution for the control and management of complex *** paper focuses on summarizing comprehensive review of the research literature of parallel control and management achieved in the recent years including the theoretical framework,core technologies,and the application *** future research,application directions,and suggestions are also discussed.

关键词： ACP methodology artificial systems computational experiments parallel control parallel management parallel systems

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Tensorial Hankel Reconstruction for Coherent DOA Estimation with Sensor Failure

Tensorial Hankel Reconstruction for Coherent DOA Estimation ...

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International Conference on Communication technology (ICCT)

作者： Fei Cheng Hang Zheng Zhoubin Teng Zhiguo Shi Chengwei Zhou State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China Department of Information and Communications Engineering Aalto University Espoo Finland Zhejiang Emergency Management Digital and Technology Center Hangzhou China Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province Hangzhou China

The research on coherent direction of arrival (DOA) estimation is of great significance for high-precision DOA estimation in complex electromagnetic environments. However, existing coherent DOA estimation methods do not consider the issue of sensor failure that is prone to occur under harsh environments. In this paper, we propose a tensorial Hankel reconstruction method for coherent DOA estimation with sensor failure. Specifically, by analyzing the tensorial Hankel property of complete incoherent covariance tensor, we design the tensorial Hankel mapping rule to reconstruct a Hankel tensor from a slice of incomplete coherent covariance tensor. Meanwhile, the fiber-missing elements caused by sensor failure are dispersed to higher dimensions in the reconstruction process, ensuring the effectiveness of conventional low-rank tensor completion algorithm. Then, the completed Hankel tensor is proven to admit the canonical polyadic model, which can be decomposed to obtain DOA estimations. Simulation results confirm the advantage of the proposed tensorial Hankel reconstruction method.

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Two-Timescale Nonlinear Model Predictive control for Flexible Operation of Coal-Fired Power Plant with Post Combustion Co 2 Capture System

SSRN

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SSRN 2024年

作者： Yang, Chao Guo, Wenxuan Shao, Yuhao Lin, Yangshu Pu, Xuesen Wang, Yifan Zheng, Chenghang Ding, Yi Fan, Haidong Zhu, Yucai Gao, Xiang State Key Lab of Clean Energy Utilization State Environmental Protection Center for Coal-Fired Air Pollution Control Zhejiang University Hangzhou310027 China State Key Lab of Clean Energy Utilization Institute of Carbon Neutrality State Environmental Protection Engineering Center for Coal-Fired Air Pollution Control Zhejiang University Hangzhou310027 China Dongfang Electric Autocontrol Engineering Co. LTD. Deyang618000 China College of Electrical Engineering Zhejiang University Hangzhou310027 China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China

Post-combustion carbon capture (PCC) with chemical absorption exhibits significant interactions with coal-fired power plants (CFPP). The mismatch in the timescales of dynamic responses between PCC and CFPP, along with their nonlinear characteristics, presents additional challenges for the design of controllers. To address these obstacles, this work presents a two-timescale nonlinear model predictive control (T-NMPC) strategy to align the operational flexibility of the CFPP-PCC coupled system. A slow timescale nonlinear model predictive controller (S-NMPC) manages the extensive set of variables within the integrated CFPP-PCC system, focusing on long-term stability. In contrast, a fast timescale nonlinear model predictive controller (F-NMPC) dynamically adjusts the CFPP power and steam pressure to meet real-time operational demands. The interaction between these two controllers, both using extraction steam as a controlled variable, increases the flexibility of the CFPP by exploiting the energy buffers in the PCC. Comprehensive simulations of the CFPP-PCC system substantiate the enhancements in automatic generation control (AGC) performance, affirming the strategy's effectiveness. © 2024, The Authors. All rights reserved.

关键词： Coal fired power plant

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Differential Steering control for 6 × 6 Wheel-drive Mobile Robot 26

Differential Steering Control for 6 × 6 Wheel-drive Mobile ...

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26th International Conference on Automation and Computing, ICAC 2021

作者： Zhao, Hongqiang Luo, Chao Xu, Yongkang Li, Jiehao Central South University State Key Laboratory of High Performance Complex Manufacturing School of Mechanical and Electrical Engineering ChangSha China Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing China

ISBN: (纸本)9781860435577

The 6 × 6 Wheel-drive mobile robot is widely used in the field of special operations, and there is the phenomenon of slipping in the process of steering. To study the steering accuracy and stability of mobile robot, the dynamic model of 6 × 6 Wheel-drive Mobile Robot is established, and the sliding mode control strategy of differential steering based on mobile robot is proposed. Firstly, taking the torque of six wheels and the operator's direction control as input, the three-dimensional attitude and the relative position of the steering center of the mobile robot are obtained by using the position and attitude sensor, to determine the reference steering angle of the mobile robot. Secondly, considering the relationship between the position error and the velocity error, a recursive sliding surface is designed, and a neural network is introduced to approach the uncertain part of the mobile robot model. Then, a nonlinear gain function is applied to construct a dynamic surface control law. Finally, through the Trucksim/Simulink co-simulation model, the nonlinear gain recursive sliding mode dynamic surface adaptive control method for mobile robot trajectory tracking can improve the tracking speed and control precision, and the control force and torque are smooth and reasonable. The results show that the method is effective in improving the steering accuracy and stability of mobile robot. © 2021 Chinese Automation and Computing Society in the UK-CACSUK.

关键词： Mobile robots

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Towards Robust Bayesian Estimation for Linear Dynamical systems

Towards Robust Bayesian Estimation for Linear Dynamical Syst...

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Fully Actuated System Theory and Applications (CFASTA), Conference on

作者： Xinpeng Liu Xianqiang Yang School of Artificial Intelligence Dalian University of Technology Dalian P. R. China The Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian P. R. China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin P. R. China

This paper presents a robust Bayesian approach for identification of linear dynamical systems (LDS) with non-Gaussian observation noises. The Bayesian treatment of the identification problem is formulated by introducing suitable likelihood functions and prior information. The posterior distributions over unknown parameters and hidden states are jointly estimated under variational Bayesian (VB) framework, where the augmentation technique is adopted to make inference of the LDS with random parameters, and the parameter uncertainties can be also quantified by the variance statistics. Numerical studies are performed to confirm the effectiveness of the proposed method.

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DriveRP: RAG and Prompt Engineering Embodied Parallel Driving in cyber-Physical-Social Spaces

DriveRP: RAG and Prompt Engineering Embodied Parallel Drivin...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Jun Huang Hao Ma Tengchao Zhang Fei Lin Siji Ma Xiao Wang Fei-Yue Wang Faculty of Innovation Engineering Macau University of Science and Technology Macau China School of Artificial Intelligence Anhui University Hefei China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

In recent years, numerous technological advancements in Artificial Generative Intelligences (AGIs) have demonstrated significant potential to transform the intelligence acquisition mechanisms in connected autonomous vehicles (CAVs). Integrating technologies like ChatGPT into CAVs can enhance human-machine interactions. However, the emergence of such new traffic entities may introduce unforeseen hallucinations and complex risks that surpass our current understanding. To address these challenges, Retrieval-Augmented Generation (RAG) and prompt engineering technologies are being explored to enhance the reliability and safety of autonomous driving systems. RAG retrieves relevant contextual information, such as driving experiences and real-time road network status, from external databases to ensure that foundation models have access to accurate and timely data for informed decision-making. Prompt engineering optimizes the performance of large language models in autonomous driving systems by designing and refining prompts that guide the models’ responses, thereby improving their relevance and accuracy in various driving scenarios. Together, these technologies enhance the robustness and trustworthiness of autonomous driving systems. This paper proposes DriveRP, a framework that integrates RAG and prompt engineering within the Descriptive-Predictive-Prescriptive Intelligence framework of Parallel Driving theory. DriveRP aims to enhance the safety and interpretability of autonomous vehicle trajectory planning, decision-making, and motion control, ultimately achieving the "6S" goals. Grounded in Digital Twins and Metaverse-embodied parallel driving theory, DriveRP provides the infrastructure and foundational intelligence for parallel driving with Multi-modal Large Lange Models(MLLMs). Additionally, the paper discusses future trends and potential research directions, focusing on the "6S" goals of parallel driving: Smart, Safe, Secure, Sensitive, Sustainable, and Serviceable.

关键词： Accuracy Trajectory planning Decision making Transportation Transforms Safety Digital twins Prompt engineering Motion control Autonomous vehicles

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Building Intelligence Identification System via Large Language Model Watermarking: A Survey and Beyond

arXiv

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arXiv 2024年

作者： Wang, Xuhong Jiang, Haoyu Yu, Yi Yu, Jingru Lin, Yilun Yi, Ping Wang, Yingchun Yu, Qiao Li, Li Wang, Fei-Yue Shanghai Artificial Intelligence Laboratory Shanghai200433 China School of Cyber Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Automation BNRist Tsinghua University Beijing100084 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

Large Language Models (LLMs) are increasingly integrated into diverse industries, posing substantial security risks due to unauthorized replication and misuse. To mitigate these concerns, robust identification mechanisms are widely acknowledged as an effective strategy. Identification systems for LLMs now rely heavily on watermarking technology to manage and protect intellectual property and ensure data security. However, previous studies have primarily concentrated on the basic principles of algorithms and lacked a comprehensive analysis of watermarking theory and practice from the perspective of intelligent identification. To bridge this gap, firstly, we explore how a robust identity recognition system can be effectively implemented and managed within LLMs by various participants using watermarking technology. Secondly, we propose a mathematical framework based on mutual information theory, which systematizes the identification process to achieve more precise and customized watermarking. Additionally, we present a comprehensive evaluation of performance metrics for LLM watermarking, reflecting participant preferences and advancing discussions on its identification applications. Lastly, we outline the existing challenges in current watermarking technologies and theoretical frameworks, and provide directional guidance to address these challenges. Our systematic classification and detailed exposition aim to enhance the comparison and evaluation of various methods, fostering further research and development toward a transparent, secure, and equitable LLM ecosystem. Copyright © 2024, The Authors. All rights reserved.

关键词： Natural language processing systems

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