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Modeling and analysis of an underwater biomimetic vehicle-manipulator system

Science China(Information Sciences) 2022年第3期65卷 255-257页

作者： Xuejian BAI Yu WANG Shuo WANG Rui WANG Min TAN Wei WANG School of Artificial Intelligence University of Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology School of Control Science and Engineering Shandong University

In recent years, underwater vehicle-manipulator systems(UVMSs) have captured researchers' attention. Inspired by fish, bionic mechanisms were introduced into a *** underwater biomimetic vehicle-manipulator system(UBVMS) is actuated by two symmetrically arranged biomimetic undulatory fin propulsors [1]. Based on the UBVMS, some topics have been researched, such as freefloating autonomous operation [2, 3], three-dimensional helical path following [4], and underwater video processing [5].

关键词： underwater biomimetic vehicle underwater robots underwater vehicle-manipulator system biomimetic robots hydrodynamic modeling

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MADDPG: Multi-agent Deep Deterministic Policy Gradient Algorithm for Formation Elliptical Encirclement and Collision Avoidance 5th

MADDPG: Multi-agent Deep Deterministic Policy Gradient Algor...

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5th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2021

作者： Xu, Leixin Chen, Weibin Liu, Xiang Chen, Yang-Yang School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China Jiangsu Automation Research Institute Lianyungang222061 China China Tiesiju Civil Engineering Group Co. Ltd. Hefei230023 China

ISBN: (纸本)9789811939976

This paper considers the formation encirclement problem of multi-agent systems with collision avoidance, where the enclosing orbit is an ellipse, the target and the formation are dynamic. The novel rewards, that is the elliptical encirclement reward, the formation reward, the angular velocity reward and collision avoidance reward are designed and a reinforcement learning (RL) algorithm, that is multi-agent deep deterministic policy gradient (MADDPG), is designed based on the novel setting of rewards. Simulation results are given to show the validity of the proposed method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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Investigating the dynamic memory effect of human drivers via ON-LSTM

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Science China(Information Sciences) 2020年第9期63卷 83-93页

作者： Shengzhe DAI Zhiheng LI Li LI Dongpu CAO Xingyuan DAI Yilun LIN Department of Automation Tsinghua University Shenzhen International Graduate School Tsinghua University Department of Mechanical and Mechatronics Engineering University of Waterloo State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

It is a widely accepted view that considering the memory effects of historical information(driving operations) is beneficial for vehicle trajectory prediction models to improve prediction accuracy. However,many commonly used models(e.g., long short-term memory, LSTM) can only implicitly simulate memory effects, but lack effective mechanisms to capture memory effects from sequence data and estimate their effective time range(ETR). This shortage makes it hard to dynamically configure the most suitable length of used historical information according to the current driving behavior, which harms the good understanding of vehicle motion. To address this problem, we propose a modified trajectory prediction model based on ordered neuron LSTM(ON-LSTM). We demonstrate the feasibility of ETR estimation based on ON-LSTM and propose an ETR estimation method. We estimate the ETR of driving fluctuations and lane change operations on the NGSIM I-80 dataset. The experiment results prove that the proposed method can well capture the memory effects during trajectory prediction. Moreover, the estimated ETR values are in agreement with our intuitions.

关键词： driving behavior memory effect trajectory prediction historical information ON-LSTM

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Evaluation of Landslide Susceptibility Using Machine Learning Based on Information Value Sampling Method

Evaluation of Landslide Susceptibility Using Machine Learnin...

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2023 China automation Congress, CAC 2023

作者： Liu, Lang Chen, Xin Zhao, Lingran Cao, Weihua School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Exploration Institute of Geological Engineering of Xiangyang Xiangyang441000 China

ISBN: (纸本)9798350303759

In landslide susceptibility prediction using machine learning (ML) models, the selection of appropriate negative samples significantly impacts the accuracy of the predictions. Many existing studies tend to randomly choose negative samples from the entire research area or specific attribute areas such as low slopes, leading to inaccuracies and biases that decrease the reliability of susceptibility mapping. To address this issue, this paper proposes an ML model based on the coupled information value (IV) method for susceptibility modeling. In this approach, the attribute values of environmental factors are transformed into information values, representing their contribution to landslides, using the information quantity method. Very low and low prone areas are then identified based on these information values, and random points are selected from these areas as negative samples for training and verifying subsequent machine learning models, which include the Random Forest (RF) model and Support Vector Machine (SVM) *** study focuses on the Three Gorges reservoir area in the Zigui-Badong area as an example. The comparative analysis of models trained using traditional sampling methods reveals that the RF model and SVM model, which were sampled using the information value method, achieve accuracies of 95.33% and 91.13%, respectively. In contrast, models trained using traditional sampling methods achieve accuracies of 90.43% and 88.69%, respectively. © 2023 IEEE.

关键词： Landslides

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Adaptive Fusional Localization for Robot Fish Based on Dynamic-weight Fuzzy Inference

Adaptive Fusional Localization for Robot Fish Based on Dynam...

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2021 China automation Congress, CAC 2021

作者： Fu, Yuzhuo Liao, Xiaocun Lu, Ben Zou, Qianqian Zhang, Zhuoliang Ou, Yarning Zhou, Chao State Key Laboratory of Management and Control for Complex Systems Institute of Automation CAS Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781665426473

Accurate localization of robots in a specific environment often requires the cooperation of multiple sensors, and how to establish a more general data fusion model is always a difficult problem. For the localization of robot fish in an indoor pool environment, this paper proposes an adaptive fusional algorithm based on fuzzy inference of dynamic weights. This paper firstly constructs a confidence probability table of sensors' signals based on the calibration data sets of BLE and UWB nodes at different distances, as the basis for updating the weights of the BLE or UWB nodes;secondly, the data obtained by each node in a single sampling period in the robot fish movement is vectorized to form a judgment matrix, and then the estimated distance is obtained by fuzzy inference together with the sensor weight;finally, the coordinates are calculated by the four-point positioning method. In this paper, more than 40 sets of experiments have been carried out with a simplified carangidae-like robot fish. The results show that the average positioning error is about 0.189m, which is 88.3% and 31.8% lower than that of using BLE only and using UWB only. In this paper, the fusional positioning method based on statistics combines the advantages of different sensors to reduce the data scale and achieve data denoising while fusing sensor data, which provides a reference for indoor positioning of robot fish and multi-sensor data fusion. © 2021 IEEE

关键词： Fuzzy inference

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A New Constrained Cost Value Iteration for Optimal control of Discrete-Time Nonlinear systems

A New Constrained Cost Value Iteration for Optimal Control o...

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2021 China automation Congress, CAC 2021

作者： Li, Tao Wei, Qinglai The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665426473

In this paper, a new constrained cost value iteration (CCVI) adaptive dynamic programming (ADP) algorithm is developed to solve optimal control problems with constrained cost function. The CCVI algorithm is initialized by a feasible control law, and the cost function of this initial feasible control constitutes the constraint condition in the policy update step. The convergence and optimality of the CCVI are analyzed. Furthermore, the feasibility of any iterative control law and the on-line evolving control law are proven. It is emphasized that the feasibility of any iterative control law and the on-line evolving control law guarantee that the CCVI algorithm can be used in situations where the cost function constraint is extremely important. Finally, a simulation example is given to verify the effect of the developed algorithm. © 2021 IEEE

关键词： control theory

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Image captioning on fine art paintings via virtual paintings 1

Image captioning on fine art paintings via virtual paintings

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Lu, Yue Guo, Chao Dai, Xingyuan Wang, Fei-Yue Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665433372

Machine learning in fine art paintings is attracting increasing attention recently. Image captioning of paintings is of great importance for painting analysis, but it is rarely studied. The paintings have abstract expressions and lack annotated datasets, leading to the data-hungry problem in painting captioning. Thus, painting captioning has more significant challenges than photographic image captioning. This paper makes a novel attempt at generating content descriptions of paintings. We generate virtual paintings using the style transfer technique to deal with the data-hungry problem, then train the painting captioning model via a two-step manner. We evaluate our method on an annotated small-scale painting captioning dataset and demonstrate our improvements. © 2021 IEEE.

关键词： Painting

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Reinforcement Learning Methods in Robotic Fish: Survey

Reinforcement Learning Methods in Robotic Fish: Survey

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第43届中国控制会议

作者： Penghang Shuai Haipeng Li Yongkang Luo Liangwei Deng The Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Reinforcement learning control has gained increasing attention in the field of biomimetic robotic fish, due to its advantage of universal applicability without prior knowledge of dynamic modeling. In this paper, we review the research work on the application of reinforcement learning in the field of robotic fish. We present and discuss the general model of applying reinforcement learning approaches to robotic fish control, such as the design of environment, state, reward and the selection of reinforcement learning algorithm. Furthermore, we propose to divide the typical tasks of reinforcement learning applied in robotic fish into single control and swarm control, and review the recent advancements separately.

关键词： Reinforcement Learning control Robotic Fish Single control Swarm control

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Design of An Intelligent control System for Underground Directional Drilling Trajectory in Coal Mines 41

Design of An Intelligent Control System for Underground Dire...

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第41届中国控制会议

作者： Wangnian Li Min Wu Ke Yao Quanxin Li Chengda Lu Sheng Du School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Xi'an Research Institute Co.Ltd. China Coal Technology and Engineering Group

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Directional drilling trajectory control technology is one of the key technologies to realize automation and intelligence of underground tunnel drilling in coal *** construction of directional drilling in underground coal mines mainly relies on manual observation of measurement-while-drilling parameters to control the drilling trajectory,which is highly dependent on manual *** paper designs an intelligent control system of underground directional drilling trajectory for coal *** the idea of hierarchical design and modular design,this system collects and displays geometric parameters,geological parameters,and engineering parameters of the directional drilling ***,an intelligent decision-making platform for directional drilling trajectory prediction and control is developed,laying a foundation for the research of directional drilling trajectory prediction and intelligent control for coal mines.

关键词： Trajectory prediction trajectory control geometric parameters geological parameters engineering parameters

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Preassigned-Time Synchronization of Memristive Chaotic Circuit via Intermittent control

Preassigned-Time Synchronization of Memristive Chaotic Circu...

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2023 International Conference on Neuromorphic Computing, ICNC 2023

作者： Wang, Qiming Wang, Leimin Wu, Genping China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Wuhan Second Ship Design and Research Institute Wuhan430064 China

ISBN: (纸本)9798350316889

In this paper, the preassigned-time synchronization (PTS) problem for a fifth-order memristive chaotic circuit (MCC) is investigated by designing a time-dependent intermittent controller. First, the dynamic characteristics of the MCC, especially the existence and occurrence of chaos, are investigated by simulation experiments. Besides, the stability of the equilibrium points is discussed by using Routh-Hurwitz criterion. Then, a time-dependent intermittent controller is designed and the PTS of MCC is realized via the presented controller. Finally, the effectiveness of theoretical results is verified by means of numerical simulations. © 2023 IEEE.

关键词： controllers

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