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25th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2024

作者： Rahvar, Faraz Yilmaz, Abdurrahman Department of Mechatronics Engineering Istanbul Technical University Istanbul Turkey Department of Control and Automation Engineering Istanbul Technical University Istanbul Turkey

ISBN: (纸本)9783031720611

Quadruped robots are indispensable for specialized tasks, particularly in disaster scenarios like earthquakes, where their mobility surpasses that of fixed robots. However, altering their dimensions significantly impacts their mechanical requirements and control systems. This study investigates and compares the principal mechanical parameters affecting the control systems of quadruped robots during scaling, aiming to understand how scalability influences their design across different sizes. Using a standard quasi-direct driven commercial quadruped robot1(Unitree A 1) model featuring dual coaxial motor design and serial link architecture actuators, simulations are conducted at four different sizes, each expanding the robot size by 30%. These simulations analyze static and dynamic forces as the robot walks on a flat surface at a constant speed. To fortify our findings, simulations employ three trajectory scenarios, in each scenario, four robots are scaled in length, increasing by a coefficient of 0.3 from robot 1 to robot 4. The first and second scenarios feature different trajectory lengths, while the third scenario increases the trajectory height while maintaining the length of the second scenario. Specifically, we examine the required torque, energy consumption of the robot motors, reaction forces between the robot feet and terrain, and the mechanical cost of transport. These outcomes encompass critical components of the robot from a mechanical analysis perspective, including joints, providing valuable references for various actuator architecture designs. Our findings reveal a logical pattern in the increase of torques, power consumptions, and reaction forces as the robot scales. These insights lay the groundwork for developing a scalable control architecture for legged robots. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Multipurpose robots

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Time-varying formation-containment control for heterogeneous multi-agent systems with communication and output delays via observer-based feedback protocol

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IEEE Transactions on automation Science and engineering 2025年 22卷 15435-15448页

作者： Yang, Yi He, Yong China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China

In this article, the time-varying formation-containment control problem of heterogeneous multi-agent systems (MASs) with an observer-based state feedback protocol is studied, where both communication delays and output delays are taken into consideration. By introducing an augmented Lyapunov-Krasovskii functionals (LKFs) candidate and employing less conservative methods to estimate its derivative, an improved quasi-formation-containment criterion is derived. On this basis, some observers and controllers are designed to allow for a larger maximum upper bound (MUB) of time delays on the premise of guaranteeing the fulfillment of the time-varying formation of leaders and the bounded convergence of followers. The estimation of the MUB determines the allowable delay range for system stability under the designed observers and controllers. Moreover, a distributed algorithm is developed to calculate the MUB of time delays, the observer gains, and the controller gains. Finally, two simulation experiments are implemented to demonstrate the obtained results, which verify the superiority and practicability of the criterion and algorithm. © 2004-2012 IEEE.

关键词： State feedback

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A Lyapunov-Based Framework for Trajectory Planning of Wheeled Vehicle Using Imitation Learning

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IEEE Transactions on automation Science and engineering 2025年 22卷 12118-12133页

作者： Lai, Jialun Wu, Zongze Ren, Zhigang Chen, Ci Tan, Qi Xie, Shengli Guangdong University of Technology School of Automation Guangdong Guangzhou510006 China Shenzhen University College of Mechatronics and Control Engineering Guangdong Shenzhen518052 China Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China

Trajectory planning with a learning-based approach has emerged as a crucial element in autonomous unmanned systems and has attracted substantial interest from both academia and industry. However, unresolved issues persist concerning data efficiency, safety, convergence, and generalization within the control pipeline. To address this gap, this work presents a trajectory planning method that combines the differential flatness of wheeled vehicle with global convergence property. Our proposed framework transforms the trajectory planning problem, integrating kinematic constraints into a motion planning paradigm. This transformation significantly reduces the state space associated with trajectory planning. Initially, Gaussian mixture regression (GMR) is employed to learn the nonlinear mapping from flat input, leveraging a limited number of demonstrations solved by the optimal control method. Subsequently, we design an asymmetric quadratic Lyapunov function that incorporates both random barrier information and the potential convergence property of the demonstration trajectories. Based on the optimized parameterized Lyapunov function incorporating the convergence and safety criteria, the analytical supplementary control is subsequently obtained by solving a quadratic programming problem to compensate for the prediction errors of GMR, which make the framework complete. Both numerical and real-world experiments are performed to validate the effectiveness of our framework. © 2004-2012 IEEE.

关键词： Motion planning

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Targets capture by distributed active swarms via bio-inspired reinforcement learning

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Science China(Physics,Mechanics & Astronomy) 2025年第1期68卷 206-217页

作者： Kun Xu Yue Li Jun Sun Shuyuan Du Xinpeng Di Yuguang Yang Bo Li Institute of Biomechanics and Medical Engineering Applied Mechanics LaboratoryDepartment of Engineering MechanicsTsinghua UniversityBeijing 100084China Institute of Nuclear and New Energy Technology Tsinghua UniversityBeijing 100084China Shanghai Aerospace Control Technology Institute Shanghai 201109China Shanghai Key Laboratory of Aerospace Intelligent Control Technology Shanghai 201109China College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China

Natural swarms arranged from cells to herds are usually decentralized but display intriguing collective intelligence in coordinating individuals across large scales to efficiently achieve their common *** from nature may provide new strategies for controlling collective dynamics of synthetic swarms to accomplish specific ***,we present a bioinspired computational framework that steers distributed active swarms to collectively capture and merge targets via reinforcement *** exploit collective milling structures of natural herds to cage the targets,and adopt a switching control policy inspired by sperms’chiral dynamics to optimize the trajectories of individuals,through which the active swarms can selforganize to enclose single or multiple distant targets in a dynamical,adaptive and scalable *** exists a critical swarm size,beyond which the excessive competition between agents would generate large mechanical forces,leading to capture instability but enabling the transition from short-distance to long-distance merging capture of multiple *** work provides physical insights into distributed active swarms and could offer a multilevel,decentralized strategy toward controlling swarm robotics in wide applications such as bio-medical devices,machine immunity,and target clearance.

关键词： swarm intelligence robotics multi-tasks artificial intelligence bionics

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Fault Diagnosis for Railway Point Machines Using VMD Multi-Scale Permutation Entropy and ReliefF Based on Vibration Signals

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Chinese Journal of Electronics 2025年第1期34卷 204-211页

作者： Yongkui Sun Yuan Cao Peng Li Shuai Su National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University School of Automation and Intelligence Beijing Jiaotong University Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University

The railway point machine plays an important part in railway systems. It is closely related to the safe operation of trains. Considering the advantages of vibration signals on anti-interference, this paper develops a novel vibration signal-based diagnosis approach for railway point machines. First, variational mode decomposition(VMD)is adopted for data preprocessing, which is verified more effective than empirical mode decomposition. Next, multiscale permutation entropy is extracted to characterize the fault features from multiple scales. Then ReliefF is utilized for feature selection, which can greatly decrease the feature dimension and improve the diagnosis accuracy. By experiment comparisons, the proposed approach performs best on diagnosis for railway point machines. The diagnosis accuracies on reverse-normal and normal-reverse processes are respectively 100% and 98.29%.

关键词： Vibrations Fault diagnosis Accuracy Data preprocessing Switches Feature extraction Rail transportation Entropy Maintenance Monitoring

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Geometric attitude tracking control of quadrotor UAVs with adaptive extended state observers

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ISA Transactions 2025年 160卷 289-297页

作者： Wang, Liping Chen, Jiapeng Huang, Yuyuan Zhu, Yongbin Pei, Hailong Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong School of Automation Science and Engineering South China University of Technology Guangzhou510640 China

This paper studies a geometric attitude tracking control problem of quadrotor unmanned aerial vehicles (UAVs) with adaptive extended state observers (AESOs). Through coordinate transformation, the error dynamic of AESO can be transformed into a canonical form which is easier to analyze by linear time-varying theory. In the presence of unknown disturbances and system uncertainties, the estimated error and stability of AESO can be analyzed. Simultaneously, a geometric tracking controller is developed on SO(3) with AESOs which guarantees that the error exponentially converges to a bounded set related to the estimated error. Some examples and a flight test are given to verify the availability of the control scheme. © 2025

关键词： Unmanned aerial vehicles (UAV)

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Multimodal industrial gearbox fault diagnosis based on vibration signals and infrared thermal imaging 24

Multimodal industrial gearbox fault diagnosis based on vibra...

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2nd International Conference on Frontiers of Intelligent Manufacturing and automation, CFIMA 2024

作者： Cao, Xunqi Li, Yan Yu, Kun Zhang, Yongchao School of Information and Control Engineering China University of Mining and Technology Xuzhou China School of Mechanical Engineering and Automation Northeastern University Shenyang China

ISBN: (纸本)9798400710681

Today, relying solely on individual vibration signals as features, intelligent fault diagnosis of gearboxes can no longer satisfy the demands of complex systems. Based on multimodal information fusion methods, it has become the main direction for gearbox fault diagnosis. Thermal images are not only easy to capture during the production process, but also can intuitively reflect the thermal change characteristics generated by various gearbox components during mechanical operation. In order to achieve multi-source information fusion and improve the accuracy of gearbox fault detection, this paper aims to propose a multimodal gearbox fault diagnosis method based on vibration signals and thermal imaging. This method combines two data sources, vibration signals and thermal images, and utilizes the powerful feature extraction ability of convolutional neural networks (CNN) to comprehensively explore the deep features of gearbox fault information, improving the accuracy and reliability of fault diagnosis. Through experimental verification, the method proposed in this paper demonstrates excellent performance in fault diagnosis of helical gearboxes, with higher diagnostic accuracy compared to single vibration signal input and single thermal imaging input methods. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Knowledge-based Retrieval Methods for Enhancing Aerospace Model Software Documentation 24

Knowledge-based Retrieval Methods for Enhancing Aerospace Mo...

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3rd International Conference on Artificial Intelligence and Intelligent Information Processing, AIIIP 2024

作者： Duan, Jinjian Wei, Deming Wang, Meiqing Chen, Gaohui Zhang, Yang Gao, Yanhua School of Mechanical Engineering and Automation Beihang University Beijing China Beijing Institute of Control and Electronic Technology Beijing China

ISBN: (纸本)9798400707308

Addressing the critical challenges of poor structure, weak relevance, and limited reusability in aerospace model software development documents, we propose a knowledge-based retrieval method. This method constructs a knowledge base by analyzing key document features, including storage methods, file formats, content structure, inter-document associations, and naming conventions. The system implements structured document management and associates documents based on software configuration information. By partitioning the retrieval domain, constructing semantic vectors, and applying multi-dimensional weight configurations, the method enables efficient retrieval in domains characterized by dense information and frequent term repetition. An aerospace model software document retrieval system has been developed and preliminarily implemented in an aerospace enterprise, demonstrating its effectiveness in improving retrieval efficiency and enhancing the reuse of development knowledge. © 2024 Copyright held by the owner/author(s).

关键词： Content based retrieval

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A dynamic point cloud fast compression framework based on eliminating spatial and temporal redundancy

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Multimedia Tools and Applications 2025年 1-19页

作者： Su, Kainan Wang, Zunran Yang, Chenguang The Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou510641 China Huawei Technologies Co Ltd Shenzhen518028 China

Dynamic point clouds can be compressed by eliminating spatial and temporal redundancy, but few research studies have considered both simultaneously. Existing research can only distinguish the specific foreground and background of the scene, achieving the former with a limited compression ratio, while the latter relies on 3D motion estimation with high computing complexity. Therefore, it is difficult to meet the real-time requirement of building remote human-robot interaction system. Aiming at the shortcomings of the above methods, a dynamic point cloud fast compression framework based on eliminating spatial and temporal redundancy is proposed. On the one hand, for eliminating the spatial redundancy, the information of the recognized objects in point cloud data will be replaced with their categories and poses. On the other hand, we eliminate the temporal redundancy by using the motion vector of the point cloud macroblock. Specifically, in order to reduce cost time of the 3D motion estimation, we firstly introduce the 2D image ORB feature to calculate the motion vector from current frame to key frame, and then look for the similar macroblock in key frame. Thus, the information of the macroblocks similar to the key frame in the current frame is replaced by the index of the corresponding macroblocks of the key frame and the motion vector. Experimental results show that the proposed method has higher computational efficiency and compression quality than the existing compression algorithm. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantic Segmentation

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Fully Distributed Model-Free Adaptive Sliding Mode control for MASs With Hybrid-Attacked Topology

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IEEE Transactions on automation Science and engineering 2025年 22卷 12336-12346页

作者： Duan, Shitao Chen, Guangdeng Zhou, Qi Li, Hongyi Huang, Tingwen Guangdong University of Technology School of Automation Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative Control Guangzhou510006 China Southwest University College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

A fully distributed model-free adaptive sliding mode control (MFASMC) strategy is proposed in this paper for unknown nonlinear multi-agent systems (MASs), in which topology networks are exposed to hybrid attacks consisting of denial-of-service (DoS) and false data injection attacks. Hybrid attacks in topology networks can result in neighbor information dropouts and inaccuracies among agents. First, the MASs with unknown dynamics are translated to equivalent linear data equations by the dynamic linearization technique. Second, the impact of neighbor information dropouts caused by DoS attacks is mitigated by a designed attack compensation mechanism, in which the compensation error is guaranteed to be bounded in the sense of mathematical expectation. Then, a fully distributed MFASMC algorithm, which does not depend on knowledge of the Laplacian matrix, is designed to improve the robustness of MASs with topology networks exposed to hybrid attacks, thus indirectly mitigating the impact of neighbor information inaccuracies. Finally, the consensus error is rigorously proved to be bounded in the sense of mathematical expectation, and the validity of the proposed strategy is confirmed by simulations. © 2004-2012 IEEE.

关键词： Network topology

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