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Multi-Objective Loosely Synchronized Search for Multi-Objective Multi-Agent Path Finding with Asynchronous Actions

Journal of Shanghai Jiaotong university(Science) 2024年第4期29卷 667-677页

作者： DU Haikuo GUO Zhengyu ZHANG Lulu CAI Yunze Department of Automation Shanghai Jiao Tong UniversityShanghai200240China Key Laboratory of System Control and Information Processing Ministry of EducationShanghai200240China China Airborne Missile Academy LuoyangHenan471000China National Key Laboratory of Air-based Information Perception and Fusion LuoyangHenan471000China

In recent years,the path planning for multi-agent technology has gradually matured,and has made breakthrough *** main difficulties in path planning for multi-agent are large state space,long algorithm running time,multiple optimization objectives,and asynchronous action of multiple *** solve the above problems,this paper first introduces the main problem of the research:multi-objective multi-agent path finding with asynchronous action,and proposes the algorithm framework of multi-objective loose synchronous(MO-LS)*** combining A*and M*,MO-LS-A*and MO-LS-M*algorithms are respectively *** completeness and optimality of the algorithm are proved,and a series of comparative experiments are designed to analyze the factors affecting the performance of the algorithm,verifying that the proposed MO-LS-M*algorithm has certain advantages.

关键词： multi-agent path finding multi-objective path planning asynchronous action loosely synchronous search

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Path Planning Based on A-Star and Dynamic Window Approach Algorithm for Wild Environment

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Journal of Shanghai Jiaotong university(Science) 2024年第4期29卷 725-736页

作者： DONG Dejin DONG Shiyin ZHANG Lulu CAI Yunze Department of Automation Shanghai Jiao Tong UniversityShanghai200240China Key Laboratory of System Control and Information Processing Ministry of EducationShanghai200240China Shanghai Electro-Mechanical Engineering Institute Shanghai201109China

The path planning problem of complex wild environment with multiple elements still poses *** paper designs an algorithm that integrates global and local planning to apply to the wild environmental path *** modeling process of wild environment map is *** optimization strategies are designed to improve the A-Star in overcoming the problems of touching the edge of obstacles,redundant nodes and twisting paths.A new weighted cost function is designed to achieve different planning ***,the improved dynamic window approach(DWA)is designed to avoid local optimality and improve time efficiency compared to traditional *** the necessary path re-planning of wild environment,the improved A-Star is integrated with the improved DWA to solve re-planning problem of unknown and moving obstacles in wild environment with multiple *** improved fusion algorithm effectively solves problems and consumes less time,and the simulation results verify the effectiveness of improved algorithms above.

关键词： path planning path re-planning wild environment A-Star algorithm dynamic window approach

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Online Optimization in Power systems With High Penetration of Renewable Generation:Advances and Prospects

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 839-858页

作者： Zhaojian Wang Wei Wei John Zhen Fu Pang Feng Liu Bo Yang Xinping Guan Shengwei Mei Key Laboratory of System Control and Information Processing Ministry of Education of ChinaDepartment of AutomationShanghai Jiao Tong UniversityShanghai 200240China State Key Laboratory of Power System and the Department of Electrical Engineering Tsinghua UniversityBeijing 100084China Institute of High Performance Computing(IHPC) Agency for Science Technology and Research(A*STAR)Singapore 138632Singapore

Traditionally,offline optimization of power systems is acceptable due to the largely predictable loads and reliable *** increasing penetration of fluctuating renewable generation and internet-of-things devices allowing for fine-grained controllability of loads have led to the diminishing applicability of offline optimization in the power systems domain,and have redirected attention to online optimization ***,online optimization is a broad topic that can be applied in and motivated by different settings,operated on different time scales,and built on different theoretical *** paper reviews the various types of online optimization techniques used in the power systems domain and aims to make clear the distinction between the most common techniques *** particular,we introduce and compare four distinct techniques used covering the breadth of online optimization techniques used in the power systems domain,i.e.,optimization-guided dynamic control,feedback optimization for single-period problems,Lyapunov-based optimization,and online convex optimization techniques for multi-period ***,we recommend some potential future directions for online optimization in the power systems domain.

关键词： optimization Lyapunov optimization online convex optimization online optimization optimization-guided control

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Infrared Dim Small Target Detection Algorithm Based on Adaptive Attention Transformer Network

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Journal of Shanghai Jiaotong University (Science) 2025年 1-13页

作者： Ding, Leqi Xia, Yilin Wang, Biyun Cai, Yunze Department of Automation Shanghai Jiao Tong UniversityKey Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China National Key Laboratory of Air-based Information Perception and Fusion Henan Luoyang471009 China Shanghai Institute of Aerospace Control Technology Infrared Detection Technology R & D Center of China Aerospace Science and Technology Corporation Shanghai201109 China

This paper presents a novel algorithm for detecting infrared dim small targets in complex backgrounds by integrating both global and local feature information. The proposed method employs an adaptive attention transformer network which addresses challenges associated with computational complexity and information redundancy in traditional self-attention mechanisms. A global context aggregation module is designed to address the loss of small target features in high-level feature maps. This module compensates for spatial distribution information during the multi-scale feature fusion process, ensuring the preservation of target features. Comparison experiments were conducted on two datasets, NUAA-SIRST and IRSTD-1k. The proposed algorithm achieved 0.784 for mIoU and 0.878 for F-measure on NUAA-SIRST, 0.632 for mIoU and 0.807 for F-measure on IRST-1k, all of which are the highest values. The proposed algorithm shows a significant improvement over other existing detection algorithms, demonstrating its capability to accurately extract both local and global features of targets with high recall rates and low false alarm rates across various scenarios. The ablation study results highlight the significant improvements contributed by the key modules to the proposed algorithm. (Figure presented.) © Shanghai Jiao Tong University 2025.

关键词： A adaptive attention global context aggregation module infrared dim small target detection TP391.4 Transformer network

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A Timeslot Clustering-Based Hybrid Traffic Scheduling in Time-Sensitive Networking 22

A Timeslot Clustering-Based Hybrid Traffic Scheduling in Tim...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Yang, Zhihao Wang, Shouliang Xu, Qimin Li, Xin Chen, Cailian Department of Automation Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China

ISBN: (纸本)9798331527471

The development of Industrial Internet of Things necessitates deterministic transmission of hybrid traffic with varying real-time requirements. Time-Sensitive Networking offers schemes combining scheduling mechanisms like the Time-Aware Shaper (TAS) and Cyclic Queuing and Forwarding (CQF). Current methods utilize the combination of TAS&CQF to schedule Time-Triggered (TT) and Audio-Video Bridging (AVB) traffic under corresponding mechanisms, respectively. However, the prior TT traffic orchestration restricts the scheduling solution space of AVB traffic, decreasing the effectiveness of combination and finally resulting in low schedulability. In this paper, we focus on enhancing the schedulability of TT&AVB scheduling by integrating TAS and Cyclic Specified Queuing and Forwarding (CSQF), which brings larger scheduling solution space to AVB traffic. The integration of TAS&CSQF is proposed by analyzing the impact of TAS on CSQF to expand the allocatable resource for AVB traffic. Then TAS&CSQF scheduling model is formulated to ensure deterministic requirements. Within this model, Timeslot Clustering Optimization (TCO) is proposed to optimize the scheduling solution space of AVB traffic and slice timeslot by refining the scheduling of TAS. Based on TCO, Flow Congestion Metric (FCM) is designed to improve schedulability by the congestion level of each link. An FCM-based algorithm is further presented to generate TT&AVB solutions incrementally. Simulation results demonstrate that our method reduces scheduling time cost by over 168 times and improves schedulability by 32% for 3000 flows compared to existing methods. © 2024 IEEE.

关键词： Traffic congestion

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OIDM: An Observability-based Intelligent Distributed Edge Sensing Method for Industrial Cyber-Physical systems 63

OIDM: An Observability-based Intelligent Distributed Edge Se...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Wang, Shigeng Jin, Tiankai Ma, Yehan Chen, Cailian Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Automation Shanghai200240 China

ISBN: (纸本)9798350316339

Industrial cyber-physical systems (ICPS) integrate physical processes with computational and communication technologies in industrial settings. With the support of edge computing technology, it is feasible to schedule large-scale sensors for efficient distributed sensing. In the sensing process, observability is the key to obtaining complete system states, and stochastic scheduling is more suitable considering uncertain factors in wireless communication. However, existing works have limited research on observability in stochastic scheduling. Targeting this issue, we propose an observability-based intelligent distributed edge sensing method (OIDM). Deep reinforcement learning (DRL) methods are adopted to optimize sensing accuracy and power efficiency. Based on the system's ability to achieve observability, we establish a bridge between observability and the number of successful sensor transmissions. Novel linear approximations of observability criteria are provided, and probabilistic bounds on observability are derived. Furthermore, these bounds guide the design of action space to achieve a probabilistic observability guarantee in stochastic scheduling. Finally, our proposed method is applied to the estimation of slab temperature in industrial hot rolling process, and simulation results validate its effectiveness. © 2024 IEEE.

关键词： Stochastic systems

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3D Machine Vision Enabled Shrinkage Pin Detection of Aviation Connectors in the Aircraft EWIS 43

3D Machine Vision Enabled Shrinkage Pin Detection of Aviatio...

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43rd Chinese control Conference, CCC 2024

作者： Wang, Yao Ren, Cheng Xu, Jiaxin Chen, Cailian Guan, Xinping Shanghai Jiao Tong University And Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Automation Shanghai200240 China

ISBN: (纸本)9789887581581

Aviation connectors are responsible for information and energy transmission in the aircraft electrical wiring interconnection system (EWIS). Even a tiny shrinkage of any connector pin may lead to a critical impact on the reliability and stability of the EWIS. However, the current detection method of connector pin shrinkage mainly relies on manual visual inspection, which is prone to missed and false detection. Additionally, traditional 2D machine vision methods face challenges in dealing with fine pins and deep calibers of connectors. To address this issue, this paper designs a defect detection device and proposes a 3D-enabled shrinkage pin detection (3D-SPD) algorithm. For the device, a 4-degree-of-freedom (4-DOF) support mechanism is designed to collect the connector point cloud based on the imaging principle of a 3D structured light camera. Then, a preprocessed method is presented to extract defective regions from the raw point cloud. Finally, 3D-SPD algorithm can automatically select the reference plane and detect the pin position, enabling online detection of connector shrinkage pins. Experiment results show that 3D-SPD algorithm is effective in detecting shrinkage pin defects on various types of aviation connectors, with an accuracy rate of 98.62% and a shrinkage pin detection rate of 100%. © 2024 Technical Committee on control Theory, Chinese Association of automation.

关键词： Machine vision

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An Improved Method for Approximating the Infinite-Horizon Value Function of the Discrete-Time Switched LQR Problem

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Journal of systems Science & Complexity 2024年第1期37卷 22-39页

作者： HOU Tan LI Yuanlong LIN Zongli Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China Charles L.Brown Department of Electrical and Computer Engineering University of VirginiaCharlottesvilleVA 22904-4743USA

This paper considers the problem of approximating the infinite-horizon value function of the discrete-time switched LQR *** particular,the authors propose a new value iteration method to generate a sequence of monotonically decreasing functions that converges exponentially to the value *** method facilitates us to use coarse approximations resulting from faster but less accurate algorithms for further value iteration,and thus,the proposed approach is capable of achieving a better approximation for a given computation time compared with the existing *** numerical examples are presented in this paper to illustrate the effectiveness of the proposed method.

关键词： Optimal control switched linear systems switched LQR problem.

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Multi-camera Registration with Small FOV Based on Pose Graph Optimization 1

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13th International Conference on Green Intelligent Transportation systems and Safety, GITSS 2022

作者： Chen, Long He, Xing He, Yuesheng Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China

ISBN: (数字)9789819730520

ISBN: (纸本)9789819730513

Multiple roadside cameras sense and localize autonomous vehicles for Automated Valet Parking. The accurate multi-camera registration into a shared coordinate system is of great importance. Owing to the sparse and distributed placement of multiple cameras, there is a very small field of view (FOV) between the cameras. The multi-camera registration is complex and dependent on human expertise. Therefore, a multi-camera registration method with small FOV based on pose graph optimization is proposed in this paper to automatically generate the extrinsic. The moving checkerboard in the public FOV is utilized as a reference for calibration. The optimization method constructs a pose graph to store camera and checkerboard pose and solves the pose graph by calculating checkerboard reprojection errors. To increase the overall flatness of the ground, ground plane assumptions can be utilized to further optimize the parameters. The experimental results show that the proposed method has greater calibration accuracy on the constructed multiple RGB-D camera system, as well as being simple and efficient in the real calibration procedure. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Automated valet parking Automatic calibration Multi-camera registration Pose graph optimization Small FOV

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A joint sparse Bayesian learning for wideband DOA estimation

A joint sparse Bayesian learning for wideband DOA estimation

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2024 Conference on Spectral Technology and Applications, CSTA 2024

作者： Zhao, Yonghong Liu, Jisong Wang, Junlong Dong, Shuxin Shannxi Key Laboratory of Complex System Control and Intelligent Information Processing Department of Automation and Information Engineering Xi'an University of Technology Shaanxi Xi'an710048 China

ISBN: (纸本)9781510683082

Wideband direction of arrival (DOA) estimation using sensor array is a noteworthy problem frequently occurring in many applications involving radar, sonar, and communication. We present a wideband DOA method based on a sparse Bayesian learning of multiple sub-bands jointly with the redundant dictionary matrix at the reference frequency. Explicitly enforcing the focusing operation and the singular value decomposition (SVD) to sensor measurements is motivated by a desire to decrease the computational complexity and storage capacity. A joint sparse Bayesian learning framework is proposed to deal with multiple sub-bands under one segment to obtain the DOA information. Moreover, the proposed method can be directly extended to multiple segments. Comparing to other wideband DOA estimation methods, the proposed method has the advantages of increased resolution, higher accuracy, and correlation of the sources, as well as not requiring the initial angles for the focusing matrix. Simulations are provided to show the effectiveness and improved performance of the proposed method. © 2024 SPIE.

关键词： Direction of arrival

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