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A Collision-Free Planning and control Framework for a Biomimetic Underwater Vehicle in Dynamic Environments

IEEE-ASME TRANSACTIONS ON MECHATRONICS 2023年第3期28卷 1415-1424页

作者： Lv, Jiaqi Wang, Yu Wang, Shuo Bai, Xuejian Wang, Rui Tan, Min Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techno Shanghai 200031 Peoples R China

In this article, a collision-free planning and control framework for a biomimetic underwater vehicle (BUV) in dynamic environments is presented. It consists of obstacle avoidance planning, arctangent nonsingularity terminal sliding mode (ANTSM) control, and fuzzy inference. A fuzzy artificial potential field with a velocity component is designed for obstacle avoidance planning. An ANTSM controller with an arctangent function is proposed to guarantee a shorter convergence time of system states. The stability of the system is analyzed by the Lyapunov theory. A fuzzy inference module is given to construct the nonlinear relationship between the control parameters of the flippers and force/torque. Finally, comparative simulations, robot operating system-based simulations, and underwater obstacle avoidance experiments of the BUV in a swimming pool are conducted to validate the performance of the proposed collision-free planning and control algorithm.

关键词： Arctangent nonsingularity terminal sliding mode (ANTSM) control biomimetic underwater vehicle (BUV) collision-free in dynamic environments fuzzy artificial potential field (FAPF) planning

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Short-Term Wind Power Prediction Based on KNN-BiGRU

Short-Term Wind Power Prediction Based on KNN-BiGRU

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Power Engineering (ICPE), International Conference on

作者： Xinyu Fan Zhenlong Wu Guibin Bian Yanhong Liu School of Electrical and Information Engineering Zhengzhou University Zhengzhou China State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331531140

ISBN: (纸本)9798331531157

Wind power generations have received widespread concern recently, however, due to the continuity of time series, ordinary machine learning models cannot learn the dependencies of continuous time series data well. To better predict the time series and improve the prediction effect of wind power, a shortterm wind power prediction method based on K-Nearest Neighbor(KNN) and Bidirectional Gated Recurrent Unit(BiGRU) is proposed. Firstly, KNN is used to detect outliers in the wind speed-power sequence in the field collected data, and then the wind speed value of all abnormal data is modified to the mean value of wind speed. Then the modified data is input to the bidirectional gated recurrent unit for prediction. The bidirectional gated recurrent unit can efficiently learn the context information of the data and improve the prediction effect. The experimental results show that the proposed prediction method based on KNN-BiGRU obtains a good prediction effect on a single unit. Compared with other methods, it has the best prediction effect on multi-unit where the accuracy rate is $\mathbf{8 4. 3 0 4} \%$.

关键词： Training Power engineering Accuracy Wind speed Time series analysis Nearest neighbor methods Wind power generation Predictive models Data models Optimization

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ACF-Net: Asymmetric Cascade Fusion for 3D Detection With LiDAR Point Clouds and Images

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第2期9卷 3360-3371页

作者： Tian, Yonglin Zhang, Xianjing Wang, Xiao Xu, Jintao Wang, Jiangong Ai, Rui Gu, Weihao Ding, Weiping Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Haomo Technol Co Ltd AI Ctr Beijing 100192 Peoples R China Anhui Univ Sch Artificial Intelligence Hefei 230031 Peoples R China Haomo Technol Co Ltd Beijing 100192 Peoples R China Nantong Univ Sch Informat Sci & Technol Nantong 226019 Peoples R China

The recognition and utilization of complementary information arising from modality-intrinsic properties play crucial roles in multimodal 3D detection. However, most of the current approaches for fusion-based 3D detection follow symmetrical fusion paradigms and adopt early fusion, middle fusion as well as late fusion styles, which ignore the unequal status of data with different modalities. In this paper, according to the timing of fusion, we adopt an asymmetric cascade fusion network to exploit both the structural information from point clouds and the complementary semantic information from images. A multi-stage cascade design of 3D object detection is proposed to iteratively refine predictions and several late image features (comprised of detection clues, segmentation clues, and deep features from encoders) are incorporated into different stages of the LiDAR branch to maintain the integrity of image features and enable deep multimodal interactions. Besides, to mitigate the effects of the down-sampling of voxelized features and possible mismatching of multimodal data, we propose proxy-based cross-modality sampling to utilize the high-density point clouds coordinates and develop an image degeneration process to simulate the noise in cross-modality matching for robust training. Extensive experiments are conducted on KITTI and Waymo Open Dataset, which validate the effectiveness of the proposed method.

关键词： Three-dimensional displays Feature extraction Point cloud compression Laser radar Object detection Timing Fuses 3D detection autonomous driving asymmetric fusion cascade fusion multimodal fusion

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AI4S Based on DeSci: Reference Model and Research Issues

AI4S Based on DeSci: Reference Model and Research Issues

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IEEE International Conference on systems, Man and Cybernetics

作者： Wenwen Ding Juanjuan Li Rui Qin Sangtian Guan Fei-Yue Wang Faculty of Innovation Engineering Macau University of Science and Technology Macau China The State Key Laboratory for Management and Control of Complex Systems The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

The rise of Artificial Intelligence for Science (AI4S) has highlighted the importance and urgency of ensuring open-ness, fairness, impartiality, diversity, and sustainability in scientific systems. Existing scientific systems, referred to as Centralized Science (CeSci), are built on centralized organizational structures and top-down institutional frameworks, which are lagging behind the development and practical requirements of AI4S. To address these limitations, AI4S needs to embrace a new scientific organizational and operational paradigm, namely Decentralized Science (DeSci). It can provide strong support to AI4S via effectively addressing issues such as information silos, biases, unfair distribution, and monopolies and promoting multidisciplinary, interdisciplinary, and trans disciplinary cooperation in science. Based on these considerations, this paper presents the framework of AI4S based on DeSci and explores its potential application scenarios and research issues. The research can provide effective guidance for the development of scientific systems.

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Attitude tracking control of spacecraft with preset-time preset-bounded convergence

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2022年第18期32卷 10162-10179页

作者： Zou, An-Min Liu, Yangyang Shantou Univ Coll Engn Dept Elect & Informat Engn Shantou 515063 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Guangdong Prov Key Lab Digital Signal & Image Pro Shantou Peoples R China Shantou Univ Key Lab Intelligent Mfg Technol Minist Educ Shantou Peoples R China

This article addresses the issue of attitude tracking control of rigid spacecraft in the existence of bounded external disturbances. By introducing a smooth time-varying scaling function, a preset-time sliding mode surface is designed. Then, a nonsingular and continuous preset-time preset-bounded attitude tracking control scheme is developed. The distinctive feature of the present control law is that the size of the convergence region and the settling time can be prescribed a priori. In addition, the proposed control scheme exhibits superiority in reducing the magnitude of the applied control torques and saving energy. At last the feasibility of the derived control protocol is illustrated by numerical simulations including a comparative example.

关键词： attitude control preset-time preset-bounded stability rigid spacecraft sliding mode control

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A Doubly Graduated Method for Inference in Markov Random Field\ast

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SIAM JOURNAL ON IMAGING SCIENCES 2021年第3期14卷 1354-1373页

作者： Yang, Xu Liu, Zhi-Yong Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

Maximum a posteriori (MAP) inference in Markov random field (MRF) lays the foundation for many computer vision tasks, which can be formulated by a binary quadratic programming (BQP) problem. Compared with the discrete methods, the continuous relaxation scheme becomes popular due to its generality and efficiency. However, existing continuous relaxation based MAP algorithms are still limited by two problems, i.e., the highly nonconvex original objective function and the gap between the original BQP problem and the relaxed continuous optimization problem. Targeting the two problems, this paper presents a doubly graduated continuous relaxation algorithm for MAP inference in MRF, which are, respectively, the Gaussian smoothing based graduated nonconvexity process and conditional gradient ascent based graduated projection. Experiments on both synthetic data and real-world images illustrate the algorithm's state-of-the-art performance in objective function optimization and typical computer vision tasks.

关键词： maximum a posteriori Markov random field Gaussian smoothing continuous relaxation

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Microfabrication of Li4SiO4 pebbles with complex pore structure via digital light processing: Accuracy control and properties optimizing

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2024年 54卷 1410-1421页

作者： Cai, Liang Tao, Sirong Shen, Zhen Wan, Li Liu, Ziao He, Xilin Zhang, Yingchun Univ Sci & Technol Beijing Sch Mat Sci & Engn 30 Xueyuan Rd Beijing 100083 Peoples R China Chinese Acad Sci Inst Automat Beijing Engn Res Ctr Intelligent Syst & Technol State Key Lab Management & Control Complex Syst 95 Zhongguancun E Rd Beijing 100190 Peoples R China Ten Dimens Guangdong Technol Co Ltd 33 Informat Rd Foshan 528225 Peoples R China

Li4SiO4 ceramic pebble is deemed as the most attractive tritium breeder for fusion reactor blanket. In this work, we fabricated a novel Li4SiO4 pebble with micrometer-scale pore structure by digital light processing to verify the feasibility of complex pore structure manufacturing for future breeders. The effects of processing parameters on printing accuracy, microstructure and mechanical properties of Li4SiO4 pebbles were investigated. For synthesizing the pure Li4SiO4 phase, the optimal stoichiometric ratio of Li2SiO3 to Li2CO3 was 1:1.09 in the premixed powder. The photosensitivity of dual-phase ceramic suspension was investigated according to an attenuation model, and the overgrowth was reduced to similar to 50 mu m by using the dimension formula. After optimizing the post-processing, the radial pore structure Li4SiO4 pebbles with excellent crush load (50.3 N, standard deviation = 4.4 N) and suitable open porosity (18.3 %) can be yielded. This work provides a fresh solution to control the pore structure of tritium breeding pebbles in an unprecedented micrometer scale.

关键词： Additive manufacturing Digital light processing Tritium breeder material Ceramic pebble Lithium orthosilicate

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Missile guidance with assisted deep reinforcement learning for head-on interception of maneuvering target

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complex & INTELLIGENT systems 2022年第2期8卷 1205-1216页

作者： Li, Weifan Zhu, Yuanheng Zhao, Dongbin Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst 95 Zhongguancun East Rd Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 101408 Peoples R China

In missile guidance, pursuit performance is seriously degraded due to the uncertainty and randomness in target maneuverability, detection delay, and environmental noise. In many methods, accurately estimating the acceleration of the target or the time-to-go is needed to intercept the maneuvering target, which is hard in an environment with uncertainty. In this paper, we propose an assisted deep reinforcement learning (ARL) algorithm to optimize the neural network-based missile guidance controller for head-on interception. Based on the relative velocity, distance, and angle, ARL can control the missile to intercept the maneuvering target and achieve large terminal intercept angle. To reduce the influence of environmental uncertainty, ARL predicts the target's acceleration as an auxiliary supervised task. The supervised learning task improves the ability of the agent to extract information from observations. To exploit the agent's good trajectories, ARL presents the Gaussian self-imitation learning to make the mean of action distribution approach the agent's good actions. Compared with vanilla self-imitation learning, Gaussian self-imitation learning improves the exploration in continuous control. Simulation results validate that ARL outperforms traditional methods and proximal policy optimization algorithm with higher hit rate and larger terminal intercept angle in the simulation environment with noise, delay, and maneuverable target.

关键词： Reinforcement learning Missile guidance Auxiliary learning Self-imitation learning

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NA-CPG: A robust and stable rhythm generator for robot motion control

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Biomimetic Intelligence & Robotics 2022年第4期2卷 30-38页

作者： Ru Tong Changlin Qiu Zhengxing Wu Jian Wang Min Tan Junzhi Yu State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Department of Advanced Manufacturing and Robotics College of EngineeringPeking UniversityBeijing 100871China

Central pattern generators(CPGs)have been widely applied in robot motion control for the spontaneous output of coherent periodic ***,the underlying CPG network exhibits good convergence performance only within a certain range of parameter spaces,and the coupling of oscillators affects the network output accuracy in complex topological ***,CPGs may diverge when parameters change drastically,and the divergence is irreversible,which is catastrophic for the control of robot ***,normalized asymmetric CPGs(NA-CPGs)that normalize the amplitude parameters of Hopf-based CPGs and add a constraint function and a frequency regulation mechanism are ***-CPGs can realize parameter decoupling,precise amplitude output,and stable and rapid convergence,as well as asymmetric output ***,it can effectively cope with large parameter changes to avoid network oscillations and *** optimize the parameters of the NA-CPG model,a reinforcement-learning-based online optimization method is further ***,a biomimetic robotic fish is illustrated to realize the whole optimization *** demonstrated that the designed NA-CPGs exhibit stable,secure,and accurate network outputs,and the proposed optimization method effectively improves the swimming speed and reduces the lateral swing of the multijoint robotic fish by 6.7%and 41.7%,*** proposed approach provides a significant improvement in CPG research and can be widely employed in the field of robot motion control.

关键词： Normalized asymmetric CPGs Rhythm generator Motion control Robotic fish

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Completed Interaction Networks for Pedestrian Trajectory Prediction

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IEEE Transactions on Multimedia 2025年

作者： Zhang, Zhong Zhou, Jianglin Liu, Shuang Xiao, Baihua Tianjin Normal University Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission Tianjin300387 China Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Institute of Automation Beijing100190 China

The social and environmental interactions, as well as the pedestrian goal are crucial for pedestrian trajectory prediction. This is because they could learn both complex interactions in the scenes and the intentions of the pedestrians. However, most existing methods either learn the one-moment social interactions, or supervise the pedestrian trajectories using long-term goal, resulting in suboptimal prediction performances. In this paper, we propose a novel network named Completed Interaction Network (CINet) to simultaneously consider the social interactions in all moments, the environmental interactions and the short-term goal of pedestrians in a unified framework for pedestrian trajectory prediction. Specifically, we propose the Spatio-Temporal Transformer Layer (STTL) to fully mine the spatio-temporal information among historical trajectories of all pedestrians in order to obtain the social interactions in all moments. Additionally, we present the Gradual Goal Module (GGM) to capture the environmental interactions under the supervision of the short-term goal, which is beneficial to understanding the intentions of the pedestrian. Afterwards, we employ the cross-attention to effectively integrate the all-moment social and environmental interactions. The experimental results on three standard pedestrian datasets, i.e., ETH/UCY, SDD and inD demonstrate that our method achieves a new state-of-the-art performance. Furthermore, the visualization results indicate that our method could predict trajectories more reasonably in complex scenarios such as sharp turns, infeasible areas and so on. © 1999-2012 IEEE.

关键词： Prediction models

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