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A Robotic End-Effector for Screwing and Unscrewing Bolts From the Side

IEEE ROBOTICS AND AUTOMATION LETTERS 2022年第4期7卷 9793-9800页

作者： Tao, Rui Fan, Junfeng Jing, Fengshui Jun, Hou Xing, Shiyu Ma, Yunkai Tan, Min 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 letter presents a novel robotic end-effector for screwing and unscrewing bolts. In many industrial scenarios, it is required to manipulate bolts from the side using robots with end-effectors. Besides, the reaction torque during tightening needs to be balanced to the stability of the robot. To address these challenges, we design a robotic end-effector that can realize side screwing and torque counteraction. Specifically, through an open gear set and a width-adjustable screwing mouth, the end-effector can approach the bolt from the side in any direction and then screw and unscrew it. A gripper with force-magnification and self-locking drive is equipped to counteract the reaction torque during screwing and unscrewing. Considering the extensions for different application scenarios, a method to extend the end-effector by switching screwing mouths is given, and the extended end-effector is able to screw a variety of objects. After further analysis and optimization of the force and size, experiments are carried out. The results show that the end-effector can screw and unscrew the target bolt robustly and counteract the reaction torque. It is also applicable in different scenarios.

关键词： Fasteners End effectors Robots Torque Mouth Gears Grippers Screwing and unscrewing grippers and other end-effectors mechanism design assembly

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Robust Detection of Malicious URLs With Self-Paced Wide & Deep Learning

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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2022年第2期19卷 717-730页

作者： Liang, Yunji Wang, Qiushi Xiong, Kang Zheng, Xiaolong Yu, Zhiwen Zeng, Daniel Northwestern Polytech Univ Sch Comp Sci Xian 710060 Shaanxi Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100080 Peoples R China

As cybercrimes grow in scale with devastating economic costs, it is important to protect potential victims against diverse attacks. In spite of the diversity of cybercrimes, it is the uniform resource locators (URLs) that connect vulnerable users with potential attacks. Although numerous solutions (e.g., rule-based solutions and machine learning-based methods) are proposed for malicious URL detection, they cannot provide robust performance due to the diversity of cybercrimes and cannot cope with the explosive growth of malicious URLs with the evolution of obfuscation strategies. In this paper, we propose a deep learning-based system, dubbed as CyberLen, to detect malicious URLs robustly and effectively. Specifically, we use factorization machine (FM) to learn the latent interaction among lexical features. For the deep structural features, position embedding is introduced for token vectorization to reduce the ambiguity of URL tokens. Meanwhile, temporal convolution network (TCN) is utilized to learn the long-distance dependency among URL tokens. To fuse heterogeneous features, self-paced wide & deep learning strategy is proposed to train a robust model effectively. The proposed solution is evaluated on a large-scale URL dataset. Our experimental results show that position embedding is constructive to reducing the ambiguity of URL tokens, and the self-paced wide & deep learning strategy shows superior performance in terms of F1 score and convergence speed.

关键词： paced learning temporal convolutional network factorization machine wide & deep malicious URL detection

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Cooperative ETM-Based Adaptive Neural Network Tracking control for Nonlinear Pure-Feedback MASs: A Special-Shaped Laplacian Matrix Method

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systemS 2024年第3期35卷 3528-3538页

作者： Zhu, Qiangqiang Niu, Ben Wang, Ding Li, Shengtao Wang, Xiaomei Kong, Jie Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Shandong Peoples R China Shandong Univ Jinan Sch Control Sci & Engn Jinan 250061 Shandong Peoples R China Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This article solves the cooperative adaptive tracking control problem for nonlinear pure-feedback multi-agent systems (MASs). Compared with the previous achievements of adaptive control of pure-feedback MASs, the partial derivative of the nonaffine function may not exist by using decoupling technology. In the controller design framework based on the backstepping technique, the additional state variables are processed using the special properties of the radial basis function neural networks (RBF NNs). A special-shaped Laplacian matrix is proposed to unify the leader gain form in the tracking error design process (the coefficient in the second term of tracking error). Furthermore, an event trigger mechanism (ETM) is introduced to save resources. The constructed controller under the ETM can not only stabilize the system states but also make the tracking error reach a small accuracy. Finally, the simulation results demonstrated the feasibility of the proposed method.

关键词： Laplace equations Nonlinear systems Multi-agent systems Switches Simulation Learning systems Information science Decoupling technology neural network (NN) pure-feedback multi-agent systems (MASs) relative threshold event trigger mechanism (ETM) special-shaped Laplacian matrix

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Pythagorean-Hodograph curves-based trajectory planning for pick-and-place operation of Delta robot with prescribed pick and place heights

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ROBOTICA 2023年第6期41卷 1651-1672页

作者： Su, Tingting Liang, Xu Zeng, Xiang Liu, Shengda Beijing Univ Technol Beijing Inst Artificial Inteligence Beijing Peoples R China North China Univ Technol Dept Mech & Elect Engn Beijing Peoples R China Chinese Acad Sci CInst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China

In this paper, a Pythagorean-Hodograph (PH) curve-based pick-and-place operation trajectory planning method for Delta parallel robots is proposed, which realizes the flexible control of pick-and-place operations to meet the requirements of various practical scenarios. First, according to the geometric relationship of pick-and-place operation path, different pick-and-place operations are classified. Then trajectory planning is carried out for different situations, respectively, and in each case, the different polynomial motion laws adopted by the linear motion segment and the curved motion segment are solved. Trajectory optimization is performed with the motion period as optimization objective. The proposed method is easier to implement, and at the same time satisfies the safety, optimization, mobility, and stability of the robot;that is, the proposed method realizes obstacle avoidance, optimal time, flexible control of the robot trajectory, and stable motion. Simulations and experiments verify the effectiveness of the method proposed in this paper. The proposed method can not only realize the fast, accurate, and safe operation in intelligent manufacturing fields such as rapid classification, palletizing, grasping, warehousing, etc., but its research route can also provide a reference for trajectory planning of intelligent vehicles in logistics system.

关键词： trajectory planning trajectory generation Delta parallel robot pick-and-place operation Pythagorean-Hodograph curves

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Category-Level 6D Object Pose Estimation With Structure Encoder and Reasoning Attention

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IEEE TRANSACTIONS ON CIRCUITS AND systemS FOR VIDEO TECHNOLOGY 2022年第10期32卷 6728-6740页

作者： Liu, Jierui Cao, Zhiqiang Tang, Yingbo Liu, Xilong Tan, Min Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

Category-level 6D object pose estimation has gained popularity and it is still challenging due to the diversity of different instances within the same category. In this paper, a novel category-level 6D object pose estimation framework with structure encoder and reasoning attention is proposed. A structure autoencoder is introduced to mine the shared structure features in the color images within the same category, via a distinct learning strategy that recovers the image of another instance but with the most similar pose to the input. On this basis, a reasoning attention decoder and full connected layers are stacked to form a rotation prediction network, where the structure features and 3D shape features are integrated and projected to a semantic space. The semantic space includes observed patterns and learnable patterns, which are better learned by adding a shortcut connection branch parallel to reasoning attention decoder with gradient decouple. Further reasoning based on these patterns endows the decoder with powerful feature representation. Without 3D object models, the proposed method models the attributes of category implicitly in the semantic space and better performance of 6D object pose estimation is guaranteed by reasoning on this space. The effectiveness of the proposed method is verified by the results on public datasets and actual experiments.

关键词： Shape Three-dimensional displays Cognition Pose estimation Feature extraction Decoding Solid modeling Category-level 6D object pose estimation structure encoder reasoning attention

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A Spatial-Temporal Approach for Multi-Airport Traffic Flow Prediction Through Causality Graphs

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systemS 2024年第1期25卷 532-544页

作者： Du, Wenbo Chen, Shenwen Li, Zhishuai Cao, Xianbin Lv, Yisheng Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Beihang Univ Key Lab Adv Technol Near Space Informat Syst Beijing 100191 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Shandong Jiaotong Univ Shandong Key Lab Smart Transportat Preparat Jinan 250353 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

Accurate airport traffic flow estimation is crucial for the secure and orderly operation of the aviation system. Recent advances in machine learning have achieved promising prediction results in the single-airport scenario. However, these works overlook the variational spatial interactions hidden among airports and show limited performances on the traffic flow prediction task for the aviation system which is composed of several airports. In this paper, we consider the multi-airport scenario and propose a novel spatio-temporal hybrid deep learning model to efficiently capture spatial correlations as well as temporal dependencies in a parallelized way. Specifically, we introduce the causal inference among airports to model their interactions and thus construct adaptive causality graphs in a data-driven manner to address the heterogeneity of airports. Furthermore, given that multi-source features are not applicable for all airports, a feature mask module is designated to adaptively select the features in spatial information mining. Extensive experiments are conducted on the real data of top-30 busiest airports in China. The results show that our spatio-temporal deep learning approach is superior to state-of-the-art methodologies and the improvement ratio is up to 4.7% against benchmarks. Ablation studies emphasize the power of the proposed adaptive causality graph and the feature mask module. All of these prove the effectiveness of the proposed methodology.

关键词： Airport traffic flow predictive models deep learning causality graph spatiotemporal analysis

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Parallel Tourism system With Foundation Models and Decentralized Autonomous Organizations

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INTERNATIONAL JOURNAL OF TOURISM RESEARCH 2025年第2期27卷

作者： Huang, Jun Tian, Yonglin Zhang, Tengchao Ni, Qinghua Lin, Fei Lee, Timothy J. Wu, Naiqi Wang, Fei-Yue Macau Univ Sci & Technol Fac Innovat Engn Dept Engn Sci Macau Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing Peoples R China Macau Univ Sci & Technol Fac Hospitality & Tourism Management Macau Peoples R China

Tourism is a critical driver of economic growth and cultural exchange. However, traditional tourism models often struggle to address challenges such as insufficient understanding of diverse travel preferences, limited capacity for personalized services, and a lack of intelligent, convenient service solutions. In this paper, we propose a virtual-real interactive Parallel Tourism system (PTS) and a Smart Tourism and Lifestyle Service Large Model (STLS-LM) to handle these challenges. By integrating Decentralized Autonomous Organizations (DAOs) and Foundation Models (FMs) supported by Retrieval-Augmented Generation, PTS elevates tourism services by incorporating agentic intelligence and autonomous intelligence, advancing beyond traditional AI-powered applications. The proposed framework enables a deep understanding of task requirements, precise multi-source information sensing, personalized functionality customization, and timely public feedback adoption, forming a comprehensive intelligent travel service system that caters to the evolving needs of modern tourism.

关键词： agentic intelligence autonomous intelligence creative tourism decentralized autonomous organizations large language models parallel intelligence retrieval-augmented generation

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Toward a Novel Robotic Manta With Unique Pectoral Fins

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IEEE TRANSACTIONS ON systemS MAN CYBERNETICS-systemS 2022年第3期52卷 1663-1673页

作者： Meng, Yan Wu, Zhengxing Dong, Huijie Wang, Jian Yu, Junzhi 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 Peking Univ Coll Engn BIC ESATDept Adv Mfg & Robot State Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China

This article proposes the mechanical design and dynamic model of an innovative manta-inspired robot system for both efficient fast swimming and high spatial maneuverability. Inspired by some biological studies, a pair of unique pectoral fins with six separate degrees of freedoms (DOFs) are developed. The novel design is characterized by an improved crank-rocker mechanism and a distinctive horizontal DOF. The former not only endows the robot with high swimming speed, but also guarantees efficient flapping patterns which are close to manta rays. The latter is employed to coordinate with the flapping movement, allowing remarkable pitch adjustment. Further, the basic motion strategy is presented by detailed analyses to the pectoral fins. Besides, based on the Morrison equation and infinitesimal method, a complete dynamic model for robotic manta with flexible pectoral fins is established, whose parameters are determined through experimental data. Moreover, the linear swimming and pitching experiments are conducted, demonstrating the prominent movement performance of the presented design and the effectiveness of the dynamic model. The obtained results shed light on updated design and control of next-generation agile underwater vehicles and robots capable of multimodal motions in dynamic and complex aquatic environments.

关键词： Robots Robot kinematics Sports Vehicle dynamics Mathematical model Aerodynamics Prototypes Dynamic model pectoral fins robotic manta spatial maneuverability underwater robotics

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Parallel Learning Based Foundation Model for Networked Traffic Signal control 26

Parallel Learning Based Foundation Model for Networked Traff...

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IEEE 26th International Conference on Intelligent Transportation systems (ITSC)

作者： Zhao, Chen Dai, Xingyuan Chen, Yuanyuan Lin, Yilun Lv, Yisheng Wang, Fei-Yue Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Shanghai AI Lab Shanghai 200232 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Macao Inst Syst Engn Macau 999078 Peoples R China

ISBN: (纸本)9798350399462

Networked Traffic Signal control (NTSC) is a fundamental component of Intelligent Transportation systems (ITS) and the broader vision of smart city development. While a plethora of intelligent strategies have been developed, the Sim2Real challenge often impedes their full realization. In response, this paper introduces the Parallel Learning-based Adaptive Network for Traffic Signal control (PLANT) as a foundation model for NTSC. We employ the Wasserstein GAN with Gradient Penalty (WGAN-GP) to generate a wide range of artificial scenarios for robust PLANT training. Further, the Transformer-based Cooperation Mechanism (TCM) is integrated as the primary learner within PLANT, facilitating effective capture of traffic dynamics and knowledge accumulation. This knowledge is readily transferable to real-world applications through meticulous fine-tuning, equipping PLANT to adapt and evolve in alignment with shifting transportation paradigms. Our empirical study on the Hangzhou road network demonstrates PLANT's superiority over both traditional and emerging DRL-based approaches, emphasizing its viability as a potential foundation model for NTSC.

关键词： Intelligent systems

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Gliding Motion Optimization for a Biomimetic Gliding Robotic Fish

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2022年第3期27卷 1629-1639页

作者： Dong, Huijie Wu, Zhengxing Meng, Yan Tan, Min Yu, Junzhi Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Peking Univ Coll Engn State Key Lab Turbulence & Complex Syst BIC ESATDept Adv Mfg & Robot Beijing 100871 Peoples R China

In this article, we present a gliding efficiency optimization strategy based on deep reinforcement learning for a gliding robotic fish. For the gliding motion in shallow waters, the nonsteady motion strongly impacts the gliding range and also reduces efficiency. This article presents a concept of transient gliding motion and illustrates its importance for the gliding robotic fish. For better gliding performance of active fins, several pectoral fins with different sizes are designed and their hydrodynamics and optimizing capabilities are analyzed by computational fluid dynamics simulation. Then, a double deep Q network-based optimization strategy is proposed to improve gliding efficiency by active pectoral fins, in which an adversarial model and a two-stage reward function are presented for the adequate calculation of gliding range. Simulations are conducted to validate the convergence and effectiveness of the proposed strategy. The aquatic experiments are carried out to further verify the proposed strategy. The results reveal that the optimization strategy can save about 4.88% of energy and 19.45% of travel time. This article provides clues to the design of active control surfaces and improvement of gliding efficiency for underwater vehicles. Remarkably, the proposed strategy can significantly improve the duration and endow the robot with the potential to perform complex tasks.

关键词： Robots Optimization Buoyancy Transient analysis Robot kinematics Hydrodynamics Energy consumption Biomimetic robot deep reinforcement learning (DRL) gliding efficiency gliding robotic fish underwater robotics

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