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arXiv 2024年

作者： Wang, Qianhao Wang, Zhepei Wang, Mingyang Ji, Jialin Han, Zhichao Wu, Tianyue Jin, Rui Gao, Yuman Xu, Chao Gao, Fei State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China Huzhou Institute Zhejiang University Huzhou313000 China

Convex polytopes have compact representations and exhibit convexity, which makes them suitable for abstracting obstacle-free spaces from various environments. Existing generation methods struggle with balancing high-quality output and efficiency. Moreover, another crucial requirement for convex polytopes to accurately contain certain seed point sets, such as a robot or a front-end path, is proposed in various tasks, which we refer to as manageability. In this paper, we propose Fast Iterative Regional Inflation (FIRI) to generate high-quality convex polytope while ensuring efficiency and manageability simultaneously. FIRI consists of two iteratively executed submodules: Restrictive Inflation (RsI) and Maximum Volume Inscribed Ellipsoid (MVIE) computation. By explicitly incorporating constraints that include the seed point set, RsI guarantees manageability. Meanwhile, iterative MVIE optimization ensures high-quality result through monotonic volume bound improvement. In terms of efficiency, we design methods tailored to the low-dimensional and multi-constrained nature of both modules, resulting in orders of magnitude improvement compared to generic solvers. Notably, in 2-D MVIE, we present the first linear-complexity analytical algorithm for maximum area inscribed ellipse, further enhancing the performance in 2-D cases. Extensive benchmarks conducted against state-of-the-art methods validate the superior performance of FIRI in terms of quality, manageability, and efficiency. Furthermore, various real-world applications showcase the generality and practicality of FIRI. The high-performance code of FIRI will be open-sourced. Copyright © 2024, The Authors. All rights reserved.

关键词： Efficiency

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The Journey/DAO/TAO of Embodied Intelligence: From Large Models to Foundation Intelligence and Parallel Intelligence

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1313-1316页

作者： Tianyu Shen Jinlin Sun Shihan Kong Yutong Wang Juanjuan Li Xuan Li Fei-Yue Wang College of Information Science and Technology Beijing University of Chemical TechnologyBeijing 100029China School of Electrical and Information Engineering Jiangsu UniversityZhenjiang 212013China State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Peng Cheng Laboratory Shenzhen 518000China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China IEEE

THE tremendous impact of large models represented by ChatGPT[1]-[3]makes it necessary to con-sider the practical applications of such models[4].However,for an artificial intelligence(AI)to truly evolve,it needs to possess a physical“body”to transition from the virtual world to the real world and evolve through interaction with the real *** this context,“embodied intelligence”has sparked a new wave of research and technology,leading AI beyond the digital realm into a new paradigm that can actively act and perceive in a physical environment through tangible entities such as robots and automated devices[5].

关键词： Intelligence DAO TAO

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A Deep Reinforcement Learning control Method for a Four-Link Brachiation Robot 2

A Deep Reinforcement Learning Control Method for a Four-Link...

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2nd International Conference on Machine Learning, Cloud Computing, and Intelligent Mining, MLCCIM 2023

作者： Zhang, Xuanyu Ji, Zishang Zhang, Haodong Xiong, Rong School of Mechatronical Engineering Beijing Institute of Technology Beijing China Zhejiang University State Key Laboratory of Industrial Control and Technology Hangzhou China

ISBN: (纸本)9798350328530

Brachiation is a common way for primates to move between treetops. This movement has the characteristics of adapting to complex, discontinuous environment and low energy consumption. However, traditional control methods are often difficult to complete such tasks where the support is point-contact and discrete. Reinforcement learning (RL) gives a solution to such tasks due to its strong ability to adapt to complex environments. Therefore, in this paper, we design a four-link underactuated robot model with a pair of suspended hooks and propose a deep reinforcement learning-based control method to realize its brachiation between bars. The policy for outputting control signals is trained using the proximal policy optimization (PPO) algorithm because of its strong performance and ability to handle continuous action spaces. A reward function considering energy consumption and the challenges posed by the introduction of hooks is designed to obtain an energy-optimized control policy. Through simulation, a comparison is drawn against the author's previously proposed method of generating energy-optimal offline trajectories and tracking by model predictive control (MPC), thereby substantiating the superiority of the proposed method outlined in this paper. © 2023 IEEE.

关键词： Model predictive control

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Novel Multimode Process Soft Sensing Methods Based on the Dynamic Mixture Variational Autoencoder Regression Model 11

Novel Multimode Process Soft Sensing Methods Based on the Dy...

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11th IEEE Data Driven control and Learning systems Conference, DDCLS 2022

作者： Cui, Linlin Yao, Le Ge, Zhiqiang Song, Zhihuan Institute of Industrial Process Control College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

Modern industrial processes with increasing complexity not only contain nonlinear and multi-mode characteristics, but also are commonly the dynamic processes, which brought challenging problems to soft sensor modeling. In order to solve these problems, a dynamic mixture variational autoencoder regression (DMVAER) model is proposed for the nonlinear multi-mode modeling, which is suitable for industrial process quality prediction with multiple complex process characteristics. Furthermore, in order to deal with the problem of semi-supervised data with a large number of unlabeled samples, a semi-supervised dynamic mixture variational autoencoder regression (ssDMVAER) model is proposed, and the corresponding semi-supervised data sequence division method is adopted to make full use of the information in both labeled data and unlabeled data. Finally, in order to verify the feasibility and effectiveness of the proposed methods, the two models are applied to an actual industrial process of methanation furnace. The results show that the proposed methods have superior soft sensing performance than existing methods. © 2022 IEEE.

关键词： Mixtures

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ColAG: A Collaborative Air-Ground Framework for Perception-Limited UGVs’ Navigation

ColAG: A Collaborative Air-Ground Framework for Perception-L...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Zhehan Li Rui Mao Nanhe Chen Chao Xu Fei Gao Yanjun Cao State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Huzhou Institute of Zhejiang University Huzhou China Dalian University of Technology China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Perception is necessary for autonomous navigation in an unknown area crowded with obstacles. It’s challenging for a robot to navigate safely without any sensors that can sense the environment, resulting in a blind robot, and becomes more difficult when comes to a group of robots. However, it could be costly to equip all robots with expensive perception or SLAM systems. In this paper, we propose a novel system named ColAG, to solve the problem of autonomous navigation for a group of blind UGVs by introducing cooperation with one UAV, which is the only robot that has full perception capabilities in the group. The UAV uses SLAM for its odometry and mapping while sharing this information with UGVs via limited relative pose estimation. The UGVs plan their trajectories in the received map and predict possible failures caused by the uncertainty of its wheel odometry and unknown risky areas. The UAV dynamically schedules waypoints to prevent UGVs from collisions, formulated as a Vehicle Routing Problem with Time Windows to optimize the UAV’s trajectories and minimize time when UGVs have to wait to guarantee safety. We validate our system through extensive simulation with up to 7 UGVs and real-world experiments with 3 UGVs.

关键词： Uncertainty Simultaneous localization and mapping Navigation Pose estimation Autonomous aerial vehicles Trajectory Safety

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Point Cloud Classification Using Content-Based Transformer via Clustering in Feature Space

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 231-239页

作者： Yahui Liu Bin Tian Yisheng Lv Lingxi Li Fei-Yue Wang the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190 IEEE the Transportation and Autonomous Systems Institute(TASI)and the Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University Indianapolis(IUPUI)Indianapolis 46202 USA

Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention,but ignore their content and fail to establish relationships between distant but relevant points. To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short. It exploits the locality of points in the feature space(content-based), which clusters the sampled points with similar features into the same class and computes the self-attention within each class, thus enabling an effective trade-off between capturing long-range dependencies and computational complexity. We further introduce an inception feature aggregator for point cloud classification, which uses parallel structures to aggregate high-frequency and low-frequency information in each branch separately. Extensive experiments show that our PointConT model achieves a remarkable performance on point cloud shape classification. Especially, our method exhibits 90.3% Top-1 accuracy on the hardest setting of ScanObjectN N. Source code of this paper is available at https://***/yahuiliu99/PointC onT.

关键词： Content-based Transformer deep learning feature aggregator local attention point cloud classification

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Design and Experimental Validation of a Worm-Like Tensegrity Robot for In-Pipe Locomotion

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Journal of Bionic Engineering 2023年第2期20卷 515-529页

作者： Xiaolin Dai Yixiang Liu Wei Wang Rui Song Yibin Li Jie Zhao School of Control Science and Engineering Shandong UniversityJinan250061China Engineering Research Center of Intelligent Unmanned System of Ministry of Education Jinan250061China Shandong Research Institute of Industrial Technology Jinan250061China State Key Laboratory of Robotics and System Harbin Institute of TechnologyHarbin150080China

Traditional rigid-body in-pipe robots usually have complex and heavy structures with limited flexibility and *** soft in-pipe robots have great improvements in flexibility,they still have manufacturing difficulties due to their reliance on high-performance soft *** structure is a kind of self-stressed spatial structure consisting discrete rigid struts connected by a continuous net of tensional flexible strings,which combines the advantages of both rigid structures and soft *** applying tensegrity structures into robotics,this paper proposes a novel worm-like tensegrity robot for moving inside ***,a robot module capable of body deformation is designed based on the concept of tensegrity and its deformation performance is ***,the optimal parameters of the module are obtained based on the tensegrity *** deformation ability of the tensegrity module is tested ***,the worm-like tensegrity robot that can crawl inside pipes is developed by connecting three modules in *** performance and load capacity are tested on the prototype of the worm-like tensegrity robot by experiments of moving in horizontal pipe,vertical pipe,and elbow *** results demonstrate the effectiveness of the proposed design and suggest that the robot has high compliance,mobility,and adaptability although with simple structure and low cost.

关键词： Bio-inspired robot Soft robot application Tensegrity structure Pipe crawling

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Comprehensive Gear Shift Schedule Design for 2-AMT Electric Vehicles Based on Sparrow Search Algorithm 42

Comprehensive Gear Shift Schedule Design for 2-AMT Electric ...

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42nd Chinese control Conference, CCC 2023

作者： Wang, Tianyi Ma, Liling Wang, Junzheng School of Automation State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9789887581543

With the aim of 2-AMT electric vehicles, a comprehensive shift schedule that considers both power and economy is proposed. First, the objective function of the comprehensive shift schedule is constructed, which is the weighted sum of vehicle acceleration time and vehicle power consumption per unit distance. Second, Sparrow Search Algorithm is used to solve the objective function and obtain the comprehensive shift schedule. Finally, AVL Cruise simulation software is used to simulate vehicle dynamics and economy under an NEDC. The results show that the comprehensive shift schedule is similar to the optimal power shift schedule in terms of dynamic performance, and the economy is optimized by 3%. The feasibility of the comprehensive shift schedule is verified. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Electric vehicles

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The efficiency and toxicity of dodecafluoro-2-methylpentan-3-one in suppressing lithium-ion battery fire

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Journal of Energy Chemistry 2022年第2期31卷 532-540页

作者： Yujun Liu Kai Yang Mingjie Zhang Shi Li Fei Gao Qiangling Duan Jinhua Sun Qingsong Wang State Key Laboratory of Fire Science University of Science and Technology of ChinaHefei 230026AnhuiChina State Key Laboratory of Operation and Control of Renewable Energy&Storage Systems China Electric Power Research InstituteBeijing 100192China

Currently,the effective and clean suppression of lithium-ion battery(LIB)fires remains a *** present work investigates the use of various inhibitor doses(Xin)of dodecafluoro-2-methylpentan-3-one(C_(6) F_(12)O)in 300 Ah LIBs,and systematically examines the thermal and toxic hazards of the extinguished batteries via real scale combustion and gas *** inhibitor is shown to be completely *** inhibition mechanism involves a combination of chemical inhibition and physical *** the chemical inhibition effect tends to saturate with increasing Xin,the physical cooling remains effective at higher inhibitor ***,extinguishing the battery fire with a high Xin of C_(6)F_(12)O is found to incur serious toxicity *** results are expected to provide a guideline for the design of inhibitor doses for the suppression of LIB fires.

关键词： Lithium ion battery safety Thermal runaway Fire suppression C_(6)F_(12)O

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A Novel Sensor Scheduling Algorithm Based on Deep Reinforcement Learning for Bearing-Only Target Tracking in UWSNs

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 1077-1079页

作者： Linyao Zheng Meiqin Liu Senlin Zhang Jian Lan Institute of Artificial Intelligence and Robotics Xi’an Jiaotong UniversityXi’an 710049 State Key Laboratory of Industrial Control Technology Zhejiang UniversityHangzhou 310027China College of Electrical Engineering Zhejiang UniversityHangzhou 310027China

Dear Editor,This letter is concerned with the energy-aware multiple sensor coscheduling for bearing-only target tracking in the underwater wireless sensor networks(UWSNs).Considering the traditional methods facing with the problems of strong environment dependence and lack flexibility,a novel sensor scheduling algorithm based on the deep reinforcement learning is ***,the sensors’co-scheduling strategy in UWSNs is formulated as Markov decision process(MDP).

关键词： Deep networks underwater

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