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arXiv 2023年

作者： Wu, Tianyue Gao, Fei State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China

Distance measurements demonstrate distinctive scalability when used for relative state estimation in large-scale multi-robot systems. Despite the attractiveness of distance measurements, multi-robot relative state estimation based on distance measurements raises a tricky optimization problem, especially in the context of large-scale systems. Motivated by this, we aim to develop specialized computational techniques that enable robust and efficient estimation when deploying distance measurements at scale. We first reveal the commonality between the estimation problem and the one that finds realization of a sensor network, from which we draw crucial lesson to inspire the proposed methods. However, solving the latter problem in large-scale (still) requires distributed optimization schemes with scalability natures, efficient computational procedures, and fast convergence rates. Towards this goal, we propose a complementary pair of distributed computational techniques with the classical block coordinate descent (BCD) algorithm as a unified backbone. In the first method, we treat Burer-Monteiro factorization as a rank-restricted heuristic for rank-constrained semidefinite programming (SDP), where a specialized BCD-type algorithm that analytically solve each block update subproblem is employed. Although this method enables robust and (extremely) fast recovery of estimates from initial guesses, it inevitably fails as the initialization becomes disorganized. We therefore propose the second method, derived from a convex formulation named anchored edge-based semidefinite programming (ESDP), to complement it, at the expense of a certain loss of efficiency. This formulation is structurally decomposable so that BCD can be naturally employed, where each subproblem is convex and (again) solved exactly. Since in both methods BCD seeks to solve the subproblem exactly, fewer iterations, as well as the number of communication rounds, are expected. Extensive evaluation on 2D and 3D probl

关键词： Scalability

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Adaptive Model Prediction control-Based Multi-Terrain Trajectory Tracking Framework for Mobile Spherical Robots

arXiv

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arXiv 2023年

作者： Liu, Yifan Hu, Tao Guan, Xiaoqing Wang, Yixu Zhang, Bixuan Wang, You Li, Guang State Key Laboratory of Industrial Control Technology Institute of Cyber Systems and Control Zhejiang University Hangzhou310027 China

Owing to uncertainties in both kinematics and dynamics, the current trajectory tracking framework for mobile robots like spherical robots cannot function effectively on multiple terrains, especially uneven and unknown ones. Since this is a prerequisite for robots to execute tasks in the wild, we enhance our previous hierarchical trajectory tracking framework to handle this issue. First, a modified adaptive RBF neural network (RBFNN) is proposed to represent all uncertainties in kinodynamics. Then the Lyapunov function is utilized to design its adaptive law, and a variable step-size algorithm is employed in the weights update procedure to accelerate convergence and improve stability. Hence, a new adaptive model prediction control-based instruction planner (VAN-MPC) is proposed. Without modifying the bottom controllers, we finally develop the multi-terrain trajectory tracking framework by employing the new instruction planner VAN-MPC. The practical experiments demonstrate its effectiveness and robustness. Copyright © 2023, The Authors. All rights reserved.

关键词： Mobile robots

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Distilling Siamese Trackers with Attention Mask 41

Distilling Siamese Trackers with Attention Mask

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第41届中国控制会议

作者： Han Sun Yongqiang Bai Wenbo Zhang State Key Lab of Intelligent Control and Decision of Complex Systems and School of Automation Beijing Institute of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In recent years,the introduction of Siamese network has brought new vitality to the object tracking ***,high-performance Siamese trackers cannot run at a real-time speed on mobile devices due to their complex and huge *** distillation is a common and effective model compression method,but it is difficult to be applied to the challenging task like object *** find out the fundamental cause is that the imbalance between the foreground and background in the object tracking task,which aggravates the problem of insufficient feature extraction ability of small ***,we propose the attention mask distillation(AMD) to help the student tracker focus on the foreground area faster and more *** attention mask can be easily obtained from the feature maps and brings fine-granularity to the traditional binary *** experimental results on OTB100 and VOT2018 show that our method enables the student tracker perform as well as the teacher *** the same time,it's able to run on the CPU at a hyper-real-time of 66 fps and achieves nearly 9 times model compression *** low computational and storage costs make it possible to deploy high-performance trackers on resource-constrained platforms.

关键词： Object Tracking Siamese Network Knowledge Distillation Attention Mask

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A Novel Evaluation Strategy to Artificial Neural Network Model Based on Bionics

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Journal of Bionic Engineering 2022年第1期19卷 224-239页

作者： Sen Tian Jin Zhang Xuanyu Shu Lingyu Chen Xin Niu You Wang School of Mathematics and Statistics Hunan Normal UniversityChangsha410081China College of Information Science and Engineering Hunan Normal UniversityChangsha410081China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China Key Laboratory of Industrial Control Technology Zhejiang UniversityHangzhou310058China Science and Technology on Parallel and Distributed Laboratory College of ComputerNational University of Defense TechnologyChangsha410199China Key Laboratory of Industrial Control Technology Institute of Cyber Systems and ControlZhejiang UniversityHangzhou310027China

With the continuous deepening of Artificial Neural Network(ANN)research,ANN model structure and function are improving towards diversification and ***,the model is more evaluated from the pros and cons of the problem-solving results and the lack of evaluation from the biomimetic aspect of imitating neural networks is not inclusive ***,a new ANN models evaluation strategy is proposed from the perspective of bionics in response to this problem in the ***,four classical neural network models are illustrated:Back Propagation(BP)network,Deep Belief Network(DBN),LeNet5 network,and olfactory bionic model(KIII model),and the neuron transmission mode and equation,network structure,and weight updating principle of the models are analyzed *** analysis results show that the KIII model comes closer to the actual biological nervous system compared with other models,and the LeNet5 network simulates the nervous system in ***,evaluation indexes of ANN are constructed from the perspective of bionics in this paper:small-world,synchronous,and chaotic ***,the network model is quantitatively analyzed by evaluation indexes from the perspective of *** experimental results show that the DBN network,LeNet5 network,and BP network have synchronous *** the DBN network and LeNet5 network have certain chaotic characteristics,but there is still a certain distance between the three classical neural networks and actual biological neural *** KIII model has certain small-world characteristics in structure,and its network also exhibits synchronization characteristics and chaotic *** with the DBN network,LeNet5 network,and the BP network,the KIII model is closer to the real biological neural network.

关键词： Artificial neural network(ANN) Back Propagation(BP)network Deep Belief Network(DBN) LeNet5 network Olfactory bionic model(KIII model) Small world Chaos Synchronous

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A Conflict-Reducing Path Planning Algorithm in Automated Warehouses 18

A Conflict-Reducing Path Planning Algorithm in Automated War...

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18th IEEE International Conference on Networking, Sensing and control, ICNSC 2021

作者： Feng, Ligen Chen, Xinyu Zhang, Tingqi Wu, Weimin Zhejiang University State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Hangzhou China

ISBN: (纸本)9781665440486

With the development of modern logistics industry, automated warehouses are becoming more and more widely used. An automated guided vehicle (AGV) is often used to carry goods in warehouse. Under specific limitations of the road layout, AGVs are prone to conflict when moving. According to the layout characteristics of automated warehouses, this paper proposes a conflict-reducing path planning algorithm, which is an improvement of A-star algorithm. The conflict cost and turn cost are introduced to the algorithm to reduce the conflict probability and the number of turns of paths. Through simulation comparison, it is verified that the algorithm can reduce the probability of conflicts among AGVs and improve the overall efficiency of multi-AGV systems. © 2021 IEEE.

关键词： Motion planning

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Reinforcement Learning for Adaptive Planner Parameter Tuning: A Perspective on Hierarchical Architecture

arXiv

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arXiv 2025年

作者： Lu, Wangtao Wei, Yufei Xu, Jiadong Jia, Wenhao Li, Liang Xiong, Rong Wang, Yue State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China College of Information and Engineering Zhejiang University of Technology Hangzhou310023 China

Automatic parameter tuning methods for planning algorithms, which integrate pipeline approaches with learning-based techniques, are regarded as promising due to their stability and capability to handle highly constrained environments. While existing parameter tuning methods have demonstrated considerable success, further performance improvements require a more structured approach. In this paper, we propose a hierarchical architecture for reinforcement learning-based parameter tuning. The architecture introduces a hierarchical structure with low-frequency parameter tuning, mid-frequency planning, and high-frequency control, enabling concurrent enhancement of both upper-layer parameter tuning and lower-layer control through iterative training. Experimental evaluations in both simulated and real-world environments show that our method surpasses existing parameter tuning approaches. Furthermore, our approach achieves first place in the Benchmark for Autonomous Robot Navigation (BARN) Challenge. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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A Real Time Vehicle Scheduling Strategy in Container Terminals 18

A Real Time Vehicle Scheduling Strategy in Container Termina...

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18th IEEE International Conference on Networking, Sensing and control, ICNSC 2021

作者： Rong, Zhenrui Wu, Weimin Wang, Xingkai Jin, Xiaojie Institute of Cyber-Systems and Control Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou China

ISBN: (纸本)9781665440486

With the rapid development of global container business, there are higher requirements for the operation efficiency of container terminals. The vehicle dispatching system is an important link in the operation of container terminal. The yard environment of container terminal is very complex, and the traditional global optimization model based on time estimation is no longer suitable to deal with the vehicle dispatching problem in the scene of dock. This paper proposes a real-time vehicle scheduling strategy for container terminals. Firstly, a buffer vehicle pool is designed to balance the efficiency of each quay crane (QC). Secondly, the wider neighborhood simulated annealing algorithm (WNSA) is presented to find the optimal matching result of each round of calculation. The experimental results show that the proposed strategy in this paper is able to improve the operation efficiency of the QCs and reduce the vehicle cost in the container terminal. © 2021 IEEE.

关键词： Vehicles

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Improved Hybrid Gaussian Process Regression Model for state-of-Health Estimation of Li-ion Batteries

Improved Hybrid Gaussian Process Regression Model for State-...

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Chinese Automation Congress (CAC)

作者： Tao An Zhitao Liu Chizhou Yan Ke Xu Hongye Su State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Wanxiang A123 Systems Corp. Hangzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Accurate state-of-health (SOH) estimation con-tributes to the healthy and stable operation of the Li-ion batteries. This paper proposes an improved hybrid Gaussian process regression (GPR) method incorporating physical information for the high-accuracy estimation of battery SOH. The estimation process considers the effects of temperature, discharge rate, and depth of discharge (DOD) on the battery health, and the relevant features of the electrode degradation process are extracted for battery SOH estimation. The kernel function of GPR comprehensively considers the mechanical characteristics of battery degradation and can effectively track the battery degra-dation process. The validation experiments were conducted on the A123 LFP/Graphite battery dataset. Sufficient comparative experiments have validated the accuracy and robustness of the proposed method.

关键词： Lithium-ion batteries Electrodes Degradation Accuracy Estimation Gaussian processes Feature extraction Robustness Batteries Kernel

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A new flexible multi-AGVs scheduling model with pre-waiting time in an intelligent manufacturing system 18

A new flexible multi-AGVs scheduling model with pre-waiting ...

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18th IEEE International Conference on Networking, Sensing and control, ICNSC 2021

作者： Niu, Haoyi Xing, Zichao Wang, Xingkai Wu, Weimin Zhang, Tao Zhejiang University State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Hangzhou China

ISBN: (纸本)9781665440486

This paper studies a flexible flow-shop scheduling problem with limited resources (FFSP-LR) which is based on an actual air-conditioning production line in an intelligent manufacturing system. It extends the classical flexible flow-shop scheduling problem by considering practical constraints of the number of the AGVs (automated guided vehicles) on the transportation resources. The problem aims to determine the optimal scheduling results that minimizes the idle waiting time and pick-up waiting time of the workstations. To solve this problem, a pre-waiting scheduling model (PWSM) is proposed by taking into account the advance waiting time of AGVs. The experimental results show that the proposed PWSM is able to reduce idle waiting time and pick-up waiting time up to 425.01 and 414.25 comparing to traditional FCFS (First Come First Serve) strategy respectively. Correspondingly, these two types of time could be saved up to 87.01% and 98.57%. Finally, the overall efficiency of the intelligent manufacturing system is improved since the PWSM is able to effectively reduce the idle waiting time and pick-up waiting time of the workstations under the condition of limited transportation resources. © 2021 IEEE.

关键词： Air conditioning

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A Dynamic Joint Real-time Scheduling Strategy of Truck and Yard Crane in Container Terminals 18

A Dynamic Joint Real-time Scheduling Strategy of Truck and Y...

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18th IEEE International Conference on Networking, Sensing and control, ICNSC 2021

作者： Jin, Xiaojie Wang, Xingkai Wu, Weimin Rong, Zhenrui Zhang, Quanling Zhejiang University State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Hangzhou China

ISBN: (纸本)9781665440486

With the continuous growth of port container throughput, it is difficult to meet the port production demand by simply configuring automation equipments to improve the operation efficiency of machinery. The container truck scheduling and yard crane scheduling are the two important scheduling links in container terminals. At the same time, there is a tight coupling between them. Thus, the joint scheduling of container trucks and yard cranes is necessary to improve the operation efficiency of container terminals. In this paper, the joint real-time scheduling of trucks and yard cranes is mathematically modeled, and is solved by the elite genetic algorithm. Finally, a variety of scheduling strategies are compared to verify the effectiveness of the proposed strategy. © 2021 IEEE.

关键词： Trucks

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