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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第2期8卷 1813-1825页

作者： Yuwen, Xuan Zhang, Hui Yan, Fengjun Chen, Long Beihang Univ Sch Transportat Sci & Engn Beijing 102206 Peoples R China Mc Master Univ Dept Mech Engn Hamilton ON L8S4L8 Canada Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In this work, we aim to improve the positioning accuracy of the visual simultaneous localization and mapping (VSLAM) through actively controlling the gaze of the positioning camera mounted on an autonomous guided vehicle (AGV). A panoramic cost map (PCM)-based gaze control method (PGC) is proposed for the active VSLAM. Different from traditional method, a panoramic camera is added beside the positioning camera to aid the gaze control of the positioning camera. The panoramic camera is used to perceive the environment and evaluate the potential performance of each available orientation of the positioning camera. The evaluation of all the available orientations will make up a panoramic cost map. The cost map is then used to help the gaze control method to select an optimal target gaze for the positioning camera. In the calculation of the panoramic cost map, the effective factors of the VSLAM, such as feature points and moving objects, are taken into consideration. In the gaze control method, we also take into consideration of errors of the system, the time delay of the proposed method, and the velocity of the AGV. The test results in different scenes with different VSLAM algorithms show that the proposed method can improve the positioning accuracy of all the tested VSLAM algorithms compared to fixed camera gaze.

关键词： Cameras Costs Visualization Simultaneous localization and mapping Task analysis Phase change materials Intelligent vehicles Visual SLAM active panoramic gaze control

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RI-LIO: Reflectivity Image Assisted Tightly-Coupled LiDAR-Inertial Odometry

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IEEE ROBOTICS AND AUTOMATION LETTERS 2023年第3期8卷 1802-1809页

作者： Zhang, Yanfeng Tian, Yunong Wang, Wanguo Yang, Guodong Li, Zhishuo Jing, Fengshui Tan, Min Chinese Acad Sci Engn Lab Intelligent Ind Vis Beijing Peoples R China Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China State Grid Intelligence Technol Co Ltd Jinan 250101 Peoples R China

In this letter, we propose RI-LIO, a new reflectivity image assisted tightly-coupled LiDAR-inertial odometry (LIO) framework that introduces additional reflectivity texture information to efficiently reduce the drift of geometric-only methods. To achieve this, we construct an iterated extended Kalman filter framework by blending the point-to-plane geometric measurement and the reflectivity image measurement. Specifically, the geometric measurement is defined as the distance from the raw point of a new scan to its nearest neighbor plane in the global incremental kd-tree map. The searched nearest neighbor point is used to render a sparse reflectivity image after LiDAR motion distortion information is given by its corresponding raw point. Then, the reflectivity measurement is built to align the sparse reflectivity image with the dense reflectivity image of the current scan by minimizing the photometric errors directly. In addition, based on the mechanism of high-resolution LiDAR, a corrected spherical projection model is proposed to project spatial points into the image frame. Finally, extensive experiments are conducted in structured, unstructured and challenging open field scenarios. The results demonstrate that the proposed method outperforms existing geometric-only methods in terms of robustness and accuracy, especially in the rotation direction.

关键词： Laser radar Feature extraction Distortion Simultaneous localization and mapping Pose estimation Head Vertical cavity surface emitting lasers Localization mapping projection model reflectivity image SLAM

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Secure Containment control for Multi-UAV systems by Fixed-Time Convergent Reinforcement Learning

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IEEE TRANSACTIONS ON CYBERNETICS 2025年第4期55卷 1981-1994页

作者： Yang, Feisheng Gong, Zhenyu Wei, Qinglai Lei, Yifei Northwestern Polytech Univ Sch Automat Xian 710129 Shaanxi Peoples R China Northwestern Polytech Univ Chongqing Sci & Technol Innovat Ctr Chongqing 401135 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

This article concerns the secure containment control problem for multiple autonomous aerial vehicles. The cyber attacker can manipulate control commands, resulting in containment failure in the position loop. Within a zero-sum graphical game framework, secure containment controllers and malicious attackers are regarded as game players, and the attack-defense process is recast as a min-max optimization problem. Acquiring optimal distributed secure control policies requires solving the game-related Hamilton-Jacobi-Isaacs (HJI) equations. Based on the critic-only neural network (NN) structure, the reinforcement learning (RL) method is employed in solving coupled HJI equations. The fixed-time convergence technique is introduced to improve the convergence rate of RL, and the experience replay mechanism is utilized to relax the persistence of excitation condition. The associated NN convergence and closed-loop stability are analyzed. In the attitude loop, the optimal feedback control law is obtained by solving Hamilton-Jacobi-Bellman equations using the fixed-time convergent RL method. The simulation example and the quadrotor experiment are given to show the effectiveness of the proposed scheme.

关键词： Autonomous aerial vehicles Convergence Games Game theory Process control Heuristic algorithms Vectors Convex hulls Automation Multi-agent systems Cooperation fixed-time reinforcement learning (RL) secure control autonomous aerial vehicle zero-sum game

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Learning latent causal factors from the intricate sensor feedback of contact-rich robotic assembly tasks

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ROBOTICS AND AUTONOMOUS systems 2025年 183卷

作者： Chen, Yurou Yu, Jiyang Lin, Zhenyang Shen, Liancheng Liu, Zhiyong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Shanghai Peoples R China China Acad Space Technol Beijing Inst Spacecraft Syst Engn Beijing Peoples R China

Achieving precise robotic assembly is paramount for safety and performance in industrial settings. Conventional assembly methods require tedious manual adjustment of many parameters, and it is challenging to meet the assembly requirements with tight clearance. Robot learning has been at the forefront of research, showing potential for automation and intelligence in robotic operations. However, intricate information such as force feedback is indispensable for high-precision robot assembly tasks, and current learning methods grapple with processing this complex observation data due to discontinuous force changes during operation and the inherent noise from the force sensor. To enhance learning efficiency amidst challenging observations, this paper proposes introducing the concept of latent causal factors that drive sensor observations and hold paramount significance in assembly tasks. This paper analyses the impact of discontinuous and noisy observations and offers two ways to infer causal factors. Based on the implied latent factors, we propose a method that learns high- precision assembly policies from interacting with the environment. The algorithm's performance is evaluated in simulated and real-world nut insertion environments, demonstrating significant improvements over the previous methods. This research also underscores the promise of causal inference in addressing industrial challenges.

关键词： Causal inference Reinforcement learning Robotic manipulation Nut insertion

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An Improved Four-Pin Gripper for Robust 2.5-D Form-Closure Grasp

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2023年第3期28卷 1500-1511页

作者： Li, Rui Cao, Yanjun Bing, Zhenshan Qiao, Hong Chongqing Univ Sch Automat Chongqing 400040 Peoples R China Zhejiang Univ Huzhou Inst Huzhou 313000 Peoples R China Tech Univ Munich Informat D-80333 Munich Germany Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

The four-pin gripper is an ideal end-effector to achieve form-closure grasps, which is especially useful in scenarios where a stable grasp is necessary. In this article, we propose an improved parallel four-pin gripper design. Compared with the previous designs, the new design achieves more planar configurations while introducing only one more actuator, thus enlarging the possibility of the gripper to stably grasp. Based on the improved design, we extend the environment constraint-based form-closure grasping point search algorithm to find the optimal parameters for the improved four-pin gripper. Furthermore, we apply an inverse kinematics-based planning method to drive the gripper from any initial state to a form-closure state. The simulation and experiment demonstrate that, compared with the previous designs, the improved design can achieve stable grasping for a wider range of planar objects.

关键词： Four-pin gripper form closure stable grasping

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Space Squeeze Reasoning and Low-Rank Bilinear Feature Fusion for Surgical Image Segmentation

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2022年第7期26卷 3209-3217页

作者： Ni, Zhen-Liang Bian, Gui-Bin Li, Zhen Zhou, Xiao-Hu Li, Rui-Qi Hou, Zeng-Guang Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci CAS Ctr Excellence Brain Sci & Technol Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol CAS MUST Joint Lab Intelligence Sci & Technol Inst Syst Engn Taipa 999078 Macao Peoples R China

Surgical image segmentation is critical for surgical robot control and computer-assisted surgery. In the surgical scene, the local features of objects are highly similar, and the illumination interference is strong, which makes surgical image segmentation challenging. To address the above issues, a bilinear squeeze reasoning network is proposed for surgical image segmentation. In it, the space squeeze reasoning module is proposed, which adopts height pooling and width pooling to squeeze global contexts in the vertical and horizontal directions, respectively. The similarity between each horizontal position and each vertical position is calculated to encode long-range semantic dependencies and establish the affinity matrix. The feature maps are also squeezed from both the vertical and horizontal directions to model channel relations. Guided by channel relations, the affinity matrix is expanded to the same size as the input features. It captures long-range semantic dependencies from different directions, helping address the local similarity issue. Besides, a low-rank bilinear fusion module is proposed to enhance the model's ability to recognize similar features. This module is based on the low-rank bilinear model to capture the inter-layer feature relations. It integrates the location details from low-level features and semantic information from high-level features. Various semantics can be represented more accurately, which effectively improves feature representation. The proposed network achieves state-of-the-art performance on cataract image segmentation dataset CataSeg and robotic image segmentation dataset EndoVis 2018.

关键词： Image segmentation Semantics Instruments Surgery Task analysis Cognition Biological tissues Bilinear feature fusion space squeeze reasoning surgical image segmentation

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Vision-Based Road Segmentation With Active Gaze control for Autonomous Vehicles

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2025年

作者： Xuan, Yuwen Chen, Jicheng Liu, Bin Wan, Xin Wen, Kai Chen, Long Zhang, Hui China Univ Petr Coll Mech & Transportat Engn Beijing 102249 Peoples R China Beihang Univ Sch Transportat Sci & Engn Beijing 102206 Peoples R China Beijing Inst Petrochem Technol Sch Informat Engn Beijing 102617 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In this article, we aim to enhance the accuracy and usefulness of vision-based road segmentation for autonomous vehicles across various scenarios by actively controlling the gaze of the camera. The camera is mounted on a pan-tilt with two degrees of freedom as azimuth and elevation. The control of the azimuth is a follow-up PID control method mimicking the preview mechanism of human drivers. It is mainly used to improve the usefulness of the road segmentation in turning conditions. The control of the elevation is mainly used to improve the accuracy of the road segmentation in extreme external conditions, at the same time, taking into consideration of the usefulness. The elevation control method is inspired by the saccade-fixation mechanism of human eyes that scans the surrounding environment in the saccade mode and focuses on the object of interesting in the fixation mode. Similarly, the active camera will capture images of the current scene at different candidate elevations and select the optimal elevation. Experimental results demonstrate that the proposed active gaze control method effectively improves the performance of various road segmentation techniques under challenging conditions such as toward-light scenarios, U-turns, and low-speed situations.

关键词： Active vision bird's-eye-view (BEV) features preview point road segmentation

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Can language models be used for real-world urban-delivery route optimization?

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The Innovation 2023年第6期4卷 92-100页

作者： Yang Liu Fanyou Wu Zhiyuan Liu Kai Wang Feiyue Wang Xiaobo Qu State Key Laboratory of Itelligent Green Vehicle and Mobility Tsinghua UniversityBeiing 100084China Jiangsu Key Laboratory of Urban ITS Jjiangsu Province Collaborative Innovation Center of Modem Urban Traffic TechnologiesSchool of TransportationSoutheast UniversityNanjing 211189China School of Vehicle and Mobilit Tsinghua UniversityBejjing 100084China Institute of Automation State Key Laboratory for Management and Control of Complex SystemsChinese Academy of SciencesBeiing 100190China

Language models have contributed to breakthroughs in interdisciplinary research,such as protein design and molecular dynamics *** this study,we reveal that beyond language,representations of other entities,such as human behaviors,that are mappable to learmable se quences can be learned by language *** compelling example is the realworld delivery route optimization *** here propose a novel approach based on the language model to optimize delivery routes on the basis of drivers'historical *** a broad range of optimizationbased approaches have been designed to optimize delivery routes,they do not capture the implicit knowledge of complex delivery operating *** model we propose integrates this knowledge in the route optimization process by learning from driving behaviors in experienced drivers.A real-world delivery route that preserves drivers'implicit behavioral patterns is first analogized to a sentence in natural *** unsupervised learning,we then learn the vector representations of words and infer the drivers'delivery chains on the basis of the tailored chain-reaction-based *** also provide insights into the fusion of language models and operations research *** our approach,language models are applied to learn drivers'delivery behaviors and infer new deliveries at the delivery zone level,while the classic traveling salesman problem(TSP)model is embedded into the hybrid framework for intra-zone *** experiments performed on real-world data from Amazon's delivery service demonstrate that the proposed approach outperforms pure optimization,supporting the effectiveness,efficiency,and extensibility of our *** a versatile approach,the proposed framework can easily be extended to various disciplines in which the data follow cert ain grammar *** anticipate that our work will serve as a stepping stone toward the understanding and application of language models in

关键词： driving breakthrough versatile

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Exploring the Interactive Dynamic Influences Between Chinese and US’s Future Markets 10th

Exploring the Interactive Dynamic Influences Between Chinese...

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10th China Conference on Command and control, C2 2022

作者： Huang, Haitao Zheng, Xiaolong Zeng, Daniel Dajun The State Key Laboratory of Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9789811960512

In this paper, we focus on investigating the interactive dynamic influences between Chinese and US’s future markets. Based on Chinese and US’s futures daily closing prices, we construct Planar Maximally Filtered Graph of futures’ influences in different periods of time by calculating the Effective Transfer Entropy between different futures and find that influence strength between futures in US’s and Chinese future markets is time-varying. We further confirm that agriculture futures both in Chinese and US’s markets are relatively independent from other futures. And metal futures in LME and COMEX exert influences on Chinese metal, fuel and industrial product futures continuously. In contrast, fuel futures in NYMEX only affect Chinese fuel futures during economic shocks merely. These findings can give us some new insights into the direction, strength and underlying dynamics of influences between Chinese and US’s future market and further guide people make better choices. © 2022, Chinese Institute of Command and control.

关键词： Entropy

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Gradient Projection Differential Neural Solution for Quadratic Optimization with Quadratic Constraints: An ACP Perspective

Gradient Projection Differential Neural Solution for Quadrat...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Liufu, Ying Liu, Mei Jin, Long Wang, Feiyue School of Information Science and Engineering Lanzhou University Lanzhou China College of Computer Qinghai Normal University Xining China Institute of Automation The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350337020

In recent years, quadratic optimizations have become increasingly popular in engineering. However, conventional methods that investigate this problem from the perspective of a canonical form with linear constraints are not effective in dealing with the significant challenges posed by quadratic constraints in practice. This paper proposes a solution framework for the quadratic optimization with quadratic constraints (QOQC) based on innovative artificial societies, computational experiments, and parallel execution (ACP) framework. Then, a gradient projection differential neural solution (GPDNS) is proposed to address this. To illustrate the effectiveness of the GPDNS model in solving the QOQC system, numerical simulations are provided. Overall, this paper presents the potential of innovative approaches like the ACP framework to enhance our capabilities in addressing challenging optimization systems. © 2023 IEEE.

关键词： ACP constraints differential neural solution numerical simulation Quadratic optimization with quadratic

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