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IEEE International Conference on Robotics and Biomimetics

作者： Jake Jie Hou Xinrui Wu Jiwei Shan Dun Li Hesheng Wang Department of Automation Shanghai Jiao Tong University China Shanghai Karlang Industrial Nantong Intelligent Technology Co. Ltd China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665481106

SLAM is significant to autonomous mobile robots. In order to realize drift-free state estimation and provide accurate localization results for the subsequent navigation, we develop an optimization-based framework consisting of a high-frequency Inertial-Wheel Odometry(IWO), Visual-Inertial-Wheel Odometry(VIWO) and a global fusion module. To fuse the local state with the position provided by GNSS, we perform ternary edge in factor graph optimization to estimate the transformation between the local frame and ENU coordinate. Thanks to the tightly-coupled VIWO, the localization property of the odometry presents more robustness, and improve the accuracy of global fusion. A noise repropagation strategy is introduced to ensure the consistency of the IMU and wheel odometer. We evaluate the proposed system on public datasets and test it in real-world scenario. The experimental results show that our system achieves accurate pose estimation in outdoor environments.

关键词： Location awareness Global navigation satellite system Simultaneous localization and mapping Robot kinematics Wheels Robustness Sensors

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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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IEEE International Conference on Real-time Computing and Robotics (RCAR)

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

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.

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Distributed Shadow Dynamic Consensus Optimization Framework for Power Market with Parameterization Bidding Strategy of GENCOs

Distributed Shadow Dynamic Consensus Optimization Framework ...

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IEEE PES Conference on Innovative Smart Grid Technologies - Middle East (ISGT Middle East)

作者： Liying Yu Peng Wang Zhe Chen Dewen Li Ning Li Rachid Cherkaoui Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China School of Electricity and Electronic Engineering École Polytechnique Fédérale de Lausanne Switzerland

This paper presents a distributed shadow dynamic consensus (DSDC) framework to address the generation company (GENCO) strategic bidding problem in the independent system operator (ISO) operation. First, each sub-group is defined according to the communication principle. The global resource allocation can be solved by introducing the local shadow power demand. Then, for the general market clearing optimization problem, a distributed dynamic consensus algorithm is presented to estimate the general equality constraint. The optimal locational marginal price (LMP) and power allocation in each sub-group are obtained in this step. Finally, the optimal solution is corrected when considering the transmission constraint. A theoretical analysis of the DSDC framework is provided. The algorithm and model are tested by the IEEE 30-bus test system to support the proposed DSDC framework.

关键词： Power demand Heuristic algorithms ISO Consensus algorithm Dynamic scheduling Power markets Smart grids

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Accelerated Simulation of Master Equation for Open Quantum systems

Accelerated Simulation of Master Equation for Open Quantum S...

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Chinese control Conference (CCC)

作者： Bo Yue Yanfang Wang Shibei Xue Department of Automation Shanghai Jiao Tong University Shanghai P. R. China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai P. R. China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai P. R. China

Simulation of open quantum systems is undoubtedly critical to design of quantum devices and applications of quantum technologies since dynamics of open quantum systems cannot be solved analytically in general. However, the dimension of open quantum systems grows exponentially as the number of their subsystems increases or as involving complicated environments, which reduces the computational efficiency of the simulation. In this paper, we boost numerical simulation of master equations of open quantum systems from several aspects with which the total time and the occupied memory in the simulation are significantly reduced without losing precision. Our method can be applied to the simulation of non-Markovian quantum systems described by an augmented system model as well. We conduct numerical experiments on simulation of a non-Markovian spin chain. Compared with the algorithm in a well-known toolbox, QuTiP, our method can take 26.3% time and less memory under fair conditions.

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Coarray Tensor Train Decomposition for Bistatic MIMO Radar with Uniform Planar Array

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IEEE Transactions on Antennas and Propagation 2025年

作者： Xie, Qianpeng Wang, Zhanling Wen, Fangqing He, Jin Truong, Trieu-Kien National University of Defense Technology State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System Changsha410073 China Hubei University of Automotive Technology Institute of Vehicle Information Control and Network Technology Shiyan442002 China China Three Gorges University Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering Yichang443002 China Shanghai Jiao Tong University Shanghai Key Laboratory of Navigation and Location Based Services School of Electronic Information and Electrical Engineering Shanghai200240 China

Recently, tensor network decomposition has attracted increasing attention due to its high efficacy in modeling multi-dimensional correlations in high-order tensors. Considering this advantage, this study employs tensor train decomposition (TTD) to achieve joint two-dimensional direction-of-departure (2D-DOD) and direction-of-arrival (2D-DOA) estimation in bistatic multiple-input multiple-output (MIMO) radar systems with uniform planar array (UPA) configurations. First, a five-dimensional (5D) tensor is defined to incorporate the two-dimensional (2D) spatial information from transmit and receive UPAs, which is then combined with temporal information. Next, a low-rank four-dimensional (4D) tensor is generated by performing an autocorrelation operation along the temporal dimension of the 5D tensor, forming a structure that encapsulates the difference coarrays of the transmit and receive UPAs. Then, a TTD framework is designed to decompose the 4D tensor into interconnected lower-order TT-cores. Furthermore, a precise bidirectional mapping is performed between the TT-cores and the original Vandermonde factor matrices. Finally, leveraging distinct combinations of TT-cores, two innovative methods are developed to recover the Vandermonde factor matrices. The proposed methods conduct automatic pairing of 2D-DOD and obtain 2D-DOA estimates by enforcing consistent permutation orders across all factor matrices. The proposed methods are verified by simulation experiments. The results confirm that the proposed methods can surpass the existing tensor-based methods in terms of estimation accuracy. © 1963-2012 IEEE.

关键词： Matrix algebra

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Accelerated Simulation of Non-Markovian Quantum systems Using Quantum Trajectory

Accelerated Simulation of Non-Markovian Quantum Systems Usin...

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Chinese control Conference (CCC)

作者： Yanfang Wang Bo Yue Shibei Xue Department of Automation Shanghai Jiao Tong University Shanghai P. R. China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai P. R. China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai P. R. China

Quantum trajectory can be viewed as a numerical simulation method for wavefunctions which consume smaller memories compared to density matrices in master equations. In this paper, we adopt quantum trajectory to simulate the evolution of a non-Markovian quantum system modeled by an augmented system and further use parallel computing to improve efficiency. Compared to the Lindblad master equation solver in QuTiP, it can run more efficiently using less memory. In the example of a 8-spin-chain non-Markovian system, it only takes about 0.57% of the memory footprint and 4.12% of the runtime of the solver for the Lindblad master equation.

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A Robust Multi-AGV Cooperative Path Planning Method Considering Spatiotemporal Distance

A Robust Multi-AGV Cooperative Path Planning Method Consider...

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Computer Engineering and intelligent control (ICCEIC) International Conference on

作者： Maolin Chen Songhang Chen Mingchang Lian School of Advanced Manufacturing Fuzhou University Quanzhou China Fujian Institute of Research on the Structure of Matter Chinese Academy of Sciences Fuzhou China Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic System Quanzhou China Fujian College University of Chinese Academy of Sciences Fuzhou China Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou China

ISBN: (数字)9798331507992

ISBN: (纸本)9798331508005

With the widespread application of Automated Guided Vehicles (AGVs) in warehousing and logistics systems, the optimization of multi-AGV path planning has become a critical issue. Current methods primarily focus on minimizing operating time and energy consumption but often overlook spatiotemporal distances between paths, leading to frequent path conflicts and interferences in the system. This paper proposes a neural network based time window estimation model, designed to generate time windows that better align with dynamic environments. Based on this model, we introduce a collaborative multi-AGV path planning method that optimizes spatiotemporal distances between paths. Compared to the Conflict-Based Search (CBS) method, this approach significantly improves the spatiotemporal distance between AGVs during task execution, enhancing the robustness of AGV clusters while only marginally increasing time costs.

关键词： Energy consumption Costs Remotely guided vehicles Neural networks Warehousing Estimation Collaboration Path planning Spatiotemporal phenomena Vehicle dynamics

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High-Precision Admittance control of Mechanical Arm Based on Force Sensor Compensation

High-Precision Admittance Control of Mechanical Arm Based on...

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第34届中国控制与决策会议

作者： Yaohui Wu Jiangbo Zhao Junzheng Wang Xin Zhang Key Laboratory of Complex System Intelligent Control and Decision School of AutomationBeijing Institute of Technology Key Laboratory of Servo Motion System Drive and Control Ministry of Industry and Information TechnologySchool of AutomationBeijing Institute of Technology Technical center Xinxing Cathay International Group Co.Ltd.

Admittance control is one of the important methods of compliance *** is performed by inputting force signals and outputting position *** control can achieve high accuracy,but the force signals have large errors,which is an important factor affecting its *** the force sensor collects force data,there are non-negligible initial deviations due to gravity,installation,and other factors,resulting in poor admittance control *** order to solve this problem,this paper takes the six-degree-of-freedom manipulator admittance control as the research *** to the mapping relationship between the end attitude and the sensor errors,we propose a method of obtaining the initial offset value of the sensor through data *** method traverses the initial values of the sensor in each working posture when the sensor has no external force input,and then performs secondary interpolation to obtain the offset values of the sensor in any posture,corrects the output values of the force/torque sensor,and then improve the accuracy of admittance *** experimental results show that this method can quickly and effectively improve the calculation accuracy of the sensor's initial deviation values and the admittance control accuracy.

关键词： Admittance control Force/torque sensor Quadratic interpolation Precision optimization

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Adaptive Estimation for Environmental Monitoring Using an Autonomous Underwater Vehicle

Adaptive Estimation for Environmental Monitoring Using an Au...

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IEEE Conference on Decision and control

作者： Ziwen Yang Joana Fonseca Shanying Zhu Cailian Chen Xinping Guan Karl H. Johansson Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Digital Futures

This paper considers the problem of monitoring and adaptively estimating an environmental field, such as temperature or salinity, using an autonomous underwater vehicle (AUV). The AUV moves in the field and persistently measures environmental scalars and its position in its local coordinate frame. The environmental scalars are approximately linearly distributed over the region of interest, and an adaptive estimator is designed to estimate the gradient. By orthogonal decomposition of the velocity of the AUV, a linear time-varying system is equivalently constructed, and the sufficient conditions on the motion of the AUV are established, under which the global exponential stability of the estimation error system is rigorously proved. Furthermore, an estimate of the exponential convergence rate is given, and a reference trajectory that maximizes the estimate of the convergence rate is obtained for the AUV to track. Numerical examples verify the stability and efficiency of the system.

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A Model-free False Data Injection Attack Strategy in Networked control systems

A Model-free False Data Injection Attack Strategy in Network...

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IEEE Conference on Decision and control

作者： Xiaoyu Luo Chongrong Fang Chengcheng Zhao Jianping He The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China The State Key Laboratory of Industrial Control Technology and Institute of Cyberspace Research Zhejiang University China

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

The data-driven attack strategies recently have received much attention when the full knowledge of the system model is unknown or difficult to be obtained for the adversary. Note that despite the critical parameters of the system model being unavailable for the adversary, the existing data-driven attack methods still depend on the linearity of the unknown system model. In this paper, we design a completely model-free attack strategy where the adversary with limited capability aims to compromise state variables such that the output value follows the expected trajectory. Specifically, we first construct a zeroth-order feedback optimization framework and uninterruptedly use probing signals for real-time measurements. Then, we iteratively update the attack signals along the composite direction of the gradient estimates of the objective function evaluations and the projected gradients. These objective function evaluations can be obtained only by real-time measurements. Furthermore, we characterize the optimality of these solutions via the optimality gap, which is affected by the dimensions of the attack signal, the iterations of solutions, and the convergence rate of the system. Extensive simulations are conducted to show the effectiveness of the proposed attack strategy.

关键词： Networked control systems Computational modeling Current measurement Linear programming Real-time systems Data models Time measurement

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