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International Conference on Intelligent Transportation

作者： Wanqing Zuo Yunwen Xu Youren Chen Dewei Li School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

The blind spots of heavy vehicles during right turns arise a safety concern for traffic. Hence, the regulation mandating heavy vehicles to come to a complete stop before making right turns is crucial in ensuring intersection safety. However, the current trajectory planning methods struggle to integrate these traffic regulations, and the restrictions imposed by such traffic rules greatly impact traffic efficiency at intersections. This paper proposes a signal temporal logic (STL) based trajectory planning method for connected and autonomous vehicles (CAVs) at a signal-free intersection, where traffic rules for heavy vehicles are conveniently described by STL formulas. All CAVs at the intersection are modeled without predefined paths to enhance the flexibility of trajectories. Each STL formula is encoded in a standardized way as a conjunction of inequality and equality constraints for the optimization, and the whole trajectory planning of CAVs is achieved through solving a mixed-integer nonlinear programming (MINLP) problem. Simulation results indicate that our method improves traffic efficiency in the scenario with heavy vehicles right turns.

关键词： Connected vehicles Trajectory planning Simulation Programming Regulation Trajectory Safety Planning Logic Autonomous vehicles

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Robot-CT Scanning Oriented Synchronous Trajectory Planning of a Dual-Manipulator system 42

Robot-CT Scanning Oriented Synchronous Trajectory Planning o...

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42nd Chinese control Conference, CCC 2023

作者： Zhang, Shuaikang Yuan, Mingxing Zhang, Xuebo Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin Key Laboratory of Intelligent Robotics Tianjin300350 China Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Chongqing400065 China

ISBN: (纸本)9789887581543

At present, most CT scanning systems adopt the traditional horizontal O-frame, suspension or C-arm structures, which can not balance the flexibility and functionality. With the development of robot technology, CT scanning systems based on manipulators have gradually emerged. However, limited by the motion accuracy of the manipulator, most researchers sacrifice the flexibility or increase the mass to improve the accuracy. To overcome this limitation, a new master-slave teaching-based scanning method is proposed to improve the scanning accuracy of the dual-manipulator Robot-CT system with the auxiliary function of rigid links. First, a rigid link is employed to connect the end effectors of the two manipulators. Next, the sampled trajectory of each joint of the slave-manipulator is recorded while the master-manipulator pulls the slave-manipulator to perform a circular motion. Finally, when carrying out the scanning, the dual-manilupator system reproduces the recorded joint trajectory without the rigid element, thereby improving the accuracy of the dual-manipulator CT scanning system to achieve a high-accuracy spatiotemporal synchronization and alignment of pose. To this end, a fifth-order spline interpolation is used to interpolate the sampled trajectory of each joint of the slave-manipulator, which allows it to perfectly reproduce the joint trajectory recorded during the teaching process and ensures the smoothness of the angular velocity and acceleration curves. Simulations are investigated and the corresponding results confirm the effectiveness of the trajectory planning method of the slave-manipulator. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Manipulators

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Spatial-Temporal-Rule-based Traffic Movement Status Interpretation at Intersections *

Spatial-Temporal-Rule-based Traffic Movement Status Interpre...

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2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

作者： Zhuang, Hanyang Gao, Liyang Qian, Yeqiang Wang, Chunxiang Yang, Ming University of Michigan Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

ISBN: (纸本)9798350327182

Traffic light is the most significant component of road transportation. Traffic lights control the traffic movement at intersections by allowing the movement of some lanes and forbidding that of the others to ensure safety and efficiency. It is important for intelligent vehicles to understand the meaning of traffic lights. Conventional research works on traffic light detection and tracking consider each light independently, so the occlusion of some lights may mislead the decision of the vehicle. Since all traffic lights at an intersection work together, it is more essential to understand the traffic movement status controlled by the traffic lights rather than the perception of a single light. Therefore, this paper proposes a spatial-temporal-rule-based framework to interpret the current traffic movement status and predict the next status. The spatial information, including position and class, is obtained by traffic light detection. The temporal information is perceived by the proposed topologic-feature-based tracking method. The rule information is recognized by combination of spatial and temporal information. A dual-view camera system consisting of a telephoto camera and a wide-angle camera is adopted to ensure long perception distance and wide coverage simultaneously. The successful tracking of traffic lights from 200 m to 10 m away provides enough data to recognize the phase-change rule, which leads to correct interpretation and prediction. A customized dataset was collected, and experiments were conducted to illustrate the capability of the proposed method. © 2023 IEEE.

关键词： Cameras

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Efficient Reinforcement Learning for 3D LiDAR Navigation of Mobile Robot 41

Efficient Reinforcement Learning for 3D LiDAR Navigation of ...

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第41届中国控制会议

作者： Yu Zhai Zhe Liu Yanzi Miao Hesheng Wang China University of Mining and Technology MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University Department of Automation Key Laboratory of System Control and Information Processing of Ministry of EducationShanghai Engineering Research Center of Intelligent Control and ManagementShanghai Jiao Tong University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Developing an efficient automatic navigation system for mobile robots is challenging in the strange scenarios where robots can only observe the environment of the surrounding limited *** other distributed automatic navigation systems exist,they often require extracting semantic information to calculate navigation action,which requires extra modules to provide perceptual information and is not *** propose an end-to-end automatic navigation system based on the reinforcement learning *** particular,the raw 3 D LiDAR data is used to directly map an efficient navigation *** design a novel dense reward function to handle the reward sparsity issue and provide a graphical representation method to enable the efficient feature learning from the raw 3 D LiDAR data in our navigation *** addition,an imitation learning based policy initialization is introduced before the subsequent reinforcement learning,which increases the learning efficiency and,in the meantime,still encouraging the robot to explore all the potential states to achieve advanced performance than the imitated *** navigation model is trained in the Webots environment and the experimental results show that our model has efficient and flexible navigation performance in complex *** importantly,trained model can be easily extended to unfamiliar environments.

关键词： End-to-end Navigation Mobile robot Reinforcement Learning 3D LiDAR

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ACBL:Attentive CNN-BiLSTM Model For Trajectory Prediction

ACBL:Attentive CNN-BiLSTM Model For Trajectory Prediction

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第43届中国控制会议

作者： Gan Yang Guangwei Wang Yichen Li Wenbin Yu Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Tianjin Navigation Instruments Research Institute

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Predicting the future trajectory of a target becomes particularly challenging while dealing with occlusion conditions,where current information is unavailable,and historical data becomes the only basis for *** this paper,we propose a trajectory prediction model that utilizes scaled dot product attention mechanism,a convolution neural network,and a bidirectional long short term memory *** scaled dot product attention mechanism gauges the impact of historical sequences,while the convolution neural network extracts intricate feature information from these ***,these data is fed into the bidirectional long short term memory network to forecast the target's navigation *** results highlight that the Attentive CNN-Bi LSTM(ACBL) network which we proposed has lower root mean square error(RMSE) by 23% on average than current methods.

关键词： Occlusion conditions Scaled dot product attention mechanism Convolution neural networks Bidirectional long short term memory networks Trajectory prediction

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Digital-Twin-Based Clock Synchronization in Industrial IoT systems 13

Digital-Twin-Based Clock Synchronization in Industrial IoT S...

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13th IEEE International Conference on CYBER Technology in Automation, control, and Intelligent systems, CYBER 2023

作者： Cheng, Ting Liu, Shuai Xu, Qimin Zhu, Shanying Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China School of Control Science and Engineering Shandong University Jinan250061 China

ISBN: (纸本)9798350315196

Close and efficient cooperation between devices in the Industrial Internet of Things (IIoT) requires precise clock synchronization as a prerequisite. The uncertainty of IIoT networks and the complexity of industrial environments have led to less precise clock synchronization achieved by traditional algorithms and higher network resource consumption. This paper proposes a digital-twin-based clock synchronization scheme for IIoT systems. By building the digital model for each local clock to predict their clock skew, the information interaction required to achieve synchronization is reduced. In order to model the clock accurately, this paper proposes to use the extended state Kalman filter to handle uncertain dynamics and unknown measurement bias to improve the estimation accuracy of clock skew further. The simulation results show that the proposed algorithm can achieve high- precision clock synchronization while reducing the consumption of network resources. © 2023 IEEE.

关键词： Internet of things

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Traffic Pricing Based on Network Congestion Games with Player and Resource Failures

Traffic Pricing Based on Network Congestion Games with Playe...

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第43届中国控制会议

作者： Shuqi Liu Jinhuan Wang Lin Wang Institute of Mathematics School of ScienceHebei University of Technology Department of Automation Shanghai Jiaotong Universityand the Key Laboratory of System Control and Information ProcessingMinistry of Education

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This paper investigates the pricing problem of transportation based on a framework of network congestion game with both player and resource ***,the traffic pricing problem is modeled as a network congestion game by redefining the player's cost function in the event of player and resource ***,by using the method of semi-tensor product of matrices,the game with player and resource failures is algebraized and verified to be a weighted potential *** weighted potential function is also ***,the impact of player and resource failures on Nash equilibria(NEs) is studied,and the condition on keeping the NE of the original game unchanged is ***,the theoretical results are verified by numerical examples.

关键词： Semi-tensor product network congestion game player and resource failures

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Joint Optimization of Power Allocation and Beamforming for UAV-RIS Communication system

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IEEE Wireless Communications Letters 2025年第6期14卷 1733-1737页

作者： Guo, Xinying Liu, Longfei Zhang, Jiankang Chen, Sheng Ministry of Education Key Laboratory of Grain Information Processing and Control Henan University of Technology China Henan University of Technology College of Information Science and Engineering Zhengzhou450001 China Bournemouth University Department of Computing and Informatics BH12 5BB United Kingdom University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

This paper considers the communication system assisted by a fixed-trajectory unmanned aerial vehicle (UAV), which is equipped with a reconfigurable intelligent surface (RIS). For this UAV-RIS assisted system, we jointly optimize the power allocation and active beamforming at the base station (BS) and the passive beamforming at the RIS, by a novel phase block coordinate descent algorithm framework aimed at maximizing the system sum-rate. Specifically, the joint optimization problem is decomposed into two phases, and we propose two optimization algorithms: one for BS power allocation using fractional programming (FP) and the other for jointly optimizing active and passive beamforming using FP-manifold, which alternately optimize two phases. Simulation results not only highlight the rapid convergence and evident superiority of our proposed framework but also reveal that the optimal UAV-RIS placement is related to the flight height. © 2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Velocity-Varying Target Tracking of Mobile Sensor Network Based on Flocking control

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Journal of Shanghai Jiaotong university(Science) 2021年第4期26卷 446-453页

作者： ZHANG Lalu Xiangxiang Dong YAO Lixiu CAI Yunze Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong UniversityShanghai200240China Department of Automation Shanghai Jiao Tong UniversityShanghai200240China Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai Jiao Tong UniversityShanghai200240China

Existing coupled distributed estimation and motion control strategies of mobile sensor networks present limitations in velocity-varying target tracking. Therefore, a velocity-varying target tracking algorithm based on flocking control is proposed herein. The Kalman-consensus filter is utilized to estimate the position, velocity and acceleration of a target. The flocking control algorithm with a velocity-varying virtual leader enables the position of the center of the mobile sensor network to converge to that of the target. By applying an effective cascading Lyapunov method, stability analysis is performed. Simulation results are provided to validate the feasibility of the proposed algorithm.

关键词： mobile sensor network velocity-varying target tracking flocking control Kalman-consensus filtering

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Transfer learning with a spatiotemporal graph convolution network for city flowprediction

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Frontiers of information Technology & Electronic Engineering 2025年第1期26卷 79-92页

作者： Binkun LIU Yu KANG Yang CAO Yunbo ZHAO Zhenyi XU Department of Automation University of Science and Technology of ChinaHefei 230026China Key Laboratory of System Control and Information Processing Ministry of EducationShanghai 200240China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230088China Institute of Advanced Technology University of Science and Technology of ChinaHefei 230088China

Recently,deep learning based city flow prediction has been extensively used in the establishment of *** methods are data-hungry,making them unscalable to areas lacking *** transfer learningcan use data-rich source domains to assist target domain cities in city flow prediction,the performance of existingmethods cannot meet the needs of actual use,because the long-distance road network connectivity is *** this problem,we propose a transfer learning method based on spatiotemporal graph convolution,in which weconstruct a co-occurrence space between the source and target domains,and then align the mapping of the sourceand target domains’data in this space,to achieve the transfer learning of the source city flow prediction modelon the target ***,a dynamic spatiotemporal graph convolution module along with a temporalencoder is devised to simultaneously capture the concurrent spatiotemporal features,which implies the inherentrelationship among the road network structures,human travel habits,and city bike ***,these concurrentfeatures are leveraged as cross-city invariant representations and nonlinearly spanned to a co-occurrence *** domain features are thereby aligned with the source domain features in the co-occurrence space by using aMahalanobis distance loss,to achieve cross-city bike flow *** proposed method is evaluated on the publicbike flow datasets in Chicago,New York,and Washington in 2015,and significantly outperforms state-of-the-arttechniques.

关键词： Transfer learning City flow prediction Spatiotemporal graph convolution

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