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A Trajectory Generation Method Based on Control Points and Improved Whale Algorithm Optimization Algorithm for Solving Unmanned Aerial Vehicle Path Planning Problems

SSRN

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SSRN 2023年

作者： Li, Bingyu Gan, Chao Zhu, Yifeng Cao, Wei-Hua Wuhan430074 China Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

Three-dimensional trajectory planning is crucial for improving the safety and operational efficiency of unmanned aerial vehicle(UAV). However, it is hard to plan a suitable path in complex real-world environments. To overcome this challenge, this paper introduces a novel trajectory planning method based on control point modelling and an advanced whale optimisation algorithm (WOA). Firstly, a trajectory modeling method based on control point interpolation algorithm was proposed, which constrains the one-dimensional dimensions of 3D points, reducing the complexity of the traditional modelling methods and increasing the efficiency of trajectory generation. After that, an advanced adaptive multi-population cooperative whale optimisation algorithm (AMCWOA) has been proposed to enhance the problem-solving capabilities of the algorithm in complex environments. Through a population evolution stage evaluation strategy (ESE), this algorithm dynamically divides the population size, thereby balancing the problem-solving breadth and precision of the algorithm. It increases algorithm accuracy and convergence speed in solving trajectory planning problems. It is imperative to emphasis that the evaluation strategy in the evolution stage purposefully addresses the blindness of multi-population evolution algorithms when dividing populations. Finally, multiple function sets are utilized to test the solving performance of the algorithm and the simulations in multiple environments are designed to demonstrate the performance in UAV path planning. The experimental comparison results demonstrate that the proposed population evolution stage evaluation strategy (ESE) and population information sharing strategy can effectively increase the search range and convergence accuracy of the algorithm in generating high-quality paths and optimising test functions. © 2023, The Authors. All rights reserved.

关键词： Efficiency

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Conual Information Fusion for Small Object Detection 40

Conual Information Fusion for Small Object Detection

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40th Chinese Control Conference, CCC 2021

作者： Chen, Jun Chen, Xiaoqiang Luo, Linbo Wang, Ganbei China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China

ISBN: (纸本)9789881563804

In view of the difficulty and low accuracy of small object detection for remote sensing images, this paper proposes a small object detection algorithm based on conual information fusion to solve the problem of real-time detection accuracy of small object. In this paper, we use bottom-up VGG16 network to realize multi-scale feature extraction to deal with the problem of insufficient image feature extraction. To direct at the problem that the feature information of each feature layer is single, the shallow feature layer and the deep feature layer are fused through the feature fusion module, which achieves the purpose that some feature layers have more abundant fusion features in the structure level. Aiming at the problem that the detection objects in remote sensing images are mainly small and medium-sized objects, this paper proposes to use the multivariate information of four different scale feature layers for classification prediction and regression prediction, so as to reduce the complexity of network model. The experimental results show that the proposed small object detection algorithm based on the fusion of four scale deep and shallow conual information can obtain good accuracy and real-time performance on the NWPU VHR-10 dataset, improve the detection accuracy on the basis of ensuring the real-time detection, and perform well in the small object detection task of remote sensing images. © 2021 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Remote sensing

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Automated Assembly by Two-Fingered Microhand for Fabrication of Soft Magnetic Microrobots

Automated Assembly by Two-Fingered Microhand for Fabrication...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yue Zhao Xiaoming Liu Ruixi Wang Dan Liu Masaru Kojima Qiang Huang Tatsuo Arai Key Laboratory of Biomimetic Robots and Systems Ministry of Education State Key Laboratory of Intelligent Control and Decision of Complex System Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing China School of Mechatronical Engineering Beijing Institute of Technology Beijing China Department of Intelligent Manufacturing Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Materials Engineering Science Osaka University Osaka Japan Center for Neuroscience and Biomedical Engineering The University of Electro-Communications Tokyo Japan

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Micro-assembly is an emerging method to fabricate microrobots with multiple modules or particles. However, there is always a lack of a flexible and efficient method to freely create the desired magnetic soft microrobots. In this paper, an automated assembly system based on a two-fingered microhand is presented for fabricating magnetic soft microrobots. Our proposed system can automatically pick and place components to assemble microrobots with a two-fingered micromanipulator, and orient these components through an external magnetic field. The automated assembly has the advantages of high accuracy, high speed, and high success rate. It can endow magnetic microrobots with flexible material selection, arbitrary geometry design, and programable magnetization profile. We can make full use of this system to fabricate multiple magnetic soft microrobots. The experiment results demonstrate that this system can efficiently fabricate microrobots with excellent mechanical properties, which have application potential in robotics, biomedical engineering, and environmental governance.

关键词： Three-dimensional displays Shape Micromanipulators Magnetization Manuals Three-dimensional printing Soft magnetic materials

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Research on Path Planning of Mobile Robot Based on DWA-IMP-A* Algorithm

Research on Path Planning of Mobile Robot Based on DWA-IMP-A...

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Chinese Control Conference (CCC)

作者： Zhi Liu Lin Zhang Shoukun Wang Tianwei Niu Yongkang Xu Jin Cao Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing China Key Laboratory of Servo Motion System Drive and Control Ministry of Industry and Information Technology School of Automation Beijing Institute of Technology Beijing China

Path planning is one of the most critical links in mobile robots. Its timeliness, security and accessibility are crucial to the development and wide application of mobile robots. However, in solving the problem of path planning, the most popular A* algorithm has some problems, such as heuristic function cannot be estimated accurately, node redundancy, path is not smooth, and obstacle avoidance cannot be achieved in real time. To solve these problems, A fusion algorithm of improved A* combined with reverse path and dynamic window method (DWA-IMP-A*) was proposed. The algorithm refines the heuristic function by incorporating the reverse path. The node optimization algorithm is used to further reduce the path length. The generated trajectories are smoothed by cubic spline interpolation. At the same time, it is integrated with the improved DWA algorithm to improve the efficiency and safety of robot path planning. The algorithm takes ROS mobile robot as the carrier and is tested under typical road conditions. Compared with A* algorithm, the planning time is reduced by 54.6% and the path length is reduced by 6.37%. Experimental results verify the effectiveness and robustness of the algorithm. The research results have certain reference significance for the path planning of various types of mobile robots and the research of driverless vehicles.

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A Generative Prediction Model Based on TimeVAE and Crossformer for Predicting Multi-Channel Data of Transformer Bushing

A Generative Prediction Model Based on TimeVAE and Crossform...

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Chinese Automation Congress (CAC)

作者： Xiangjie Huang Xun Lang Peng Li Tao Guo Li Yu Department of Electronic Engineering Information School Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China Honghe Power Supply Bureau Yunnan Power Grid Corporation Yunnan Honghe

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Transformer bushings play a crucial role in power systems. Despite their importance, there has been limited research specifically on the state prediction of transformer bushings. Moreover, currently available models only predict univariate data of bushings without considering the physical connection to other relevant data, which would give rise to a compromised prediction. To achieve a more accurate joint prediction of three critical transformer states: dielectric loss, end shield current, and equivalent capacitance, this work builds upon the state-of-the-art predictive network Crossformer. In addition to Crossformer, a data generation model named TimeVAE is introduced to enlarge the scale of the training dataset. This combination enabled us to achieve accurate multivariate predictions for transformer bushings. To evaluate the effectiveness of the proposed predictor, we conducted comparative experiments with attention-based frameworks (Autoformer, Pyraformer, Informer, Reformer, Transformer), which are considered efficient for multivariable prediction tasks. The results show that our proposed model outperforms these models in four key metrics: mean square error, mean absolute error, root mean squared error, and coefficient of determination.

关键词： Training Measurement Accuracy Dielectric losses Mean square error methods Predictive models Data collection Transformers Insulators Data models

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Study on Disturbance Propagation Characteristics of Power Grid and Evaluation of Temporal and Spatial Correlation Characteristics of Voltage Phase Trajectory 41

Study on Disturbance Propagation Characteristics of Power Gr...

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第41届中国控制会议

作者： Yibo Kong Changyou Feng Hongying Yang Liping Yan Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology Xinjiang Electric Power Research Institute of State Grid Xinjiang Electric Power Co. Ltd. National Electric Power Dispatching and Control Center of State Grid Corporation of China China Electric Power Research Institute

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the access of large-scale new energy and power electronic equipment,as well as the influence of various random uncertain factors,the analysis and control of dynamic security of large power grids become particularly difficult,so it is of great practical significance to study the disturbance propagation characteristics of complex power *** is an important parameter that affects the security of power systems,and the voltage phase trajectory contains abundant power grid ***,based on the measured data,this paper quantitatively describes the characteristics of dynamic space-time distribution of the voltage,and numerically reveals the different influences of disturbance on different nodes and the characteristics of disturbance ***,the geometric characteristics of the measured voltage phase trajectory are mined,the new trajectory distance is defined,the characteristic trajectory plane is constructed,and the spatio-temporal evolution law of voltage phase trajectory is *** the sequel,through the density-based DBSCAN clustering method,the characteristic tracks of voltage movement are clustered and analyzed,and the temporal and spatial correlation characteristics of voltage movement trends of each node are measured,and the law of disturbance propagation is ***,based on the measured data of IEEE 39-bus system,the simulation analysis is carried out,and the effectiveness of the presented algorithm is verified.

关键词： Disturbance propagation Voltage phase trajectory Temporal and spatial correlation characteristics Trajectory clustering

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Signal discrimination with wavelet algorithm and Pearson correlation-based algorithm in f-OTDR systems 2

Signal discrimination with wavelet algorithm and Pearson cor...

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2020 2nd International Conference on Electronics and Communication, Network and Computer Technology, ECNCT 2020

作者： Mo, Dai Li Yu, Zhi Hua Cai, Yun Fei Liu, Rui School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences Wuhan430074 China

This paper presents a novel method of distinguishing signal, the way expected to reduce the nuisance alarm rate since the high nuisance alarm rate will restrict the capability of phase-sensitive optical time-domain reflection technology. The proposed method includes two parts: wavelet positioning mutation to obtain the perturbation area and Pearson correlation algorithm to directly convert the intensity of the perturbation into a useful amplitude. This technique avoids the use of irrelevant data in these differential signals and provides a simple and feasible new approach for distinguishing signal and optimizing the positioning speed of f-OTDR systems. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Time domain analysis

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Disturbance Rejection in Lower-Limb Rehabilitation Robot with Parameter Uncertainties

Disturbance Rejection in Lower-Limb Rehabilitation Robot wit...

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intelligent systems, International Workshop on

作者： He Wang Jinhua She Feng Wang Juan Zhao Seiichi Kawata School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

ISBN: (数字)9798350353303

ISBN: (纸本)9798350353310

This paper presents a method integrated equivalent-input-disturbance (EID) and repetitive control (RC) to mitigate the effects of non-linearity and uncertainty in a pedaling rehabilitation robot. The repetitive controller is designed to ensure accurate steady-state tracking of periodic signal. Meanwhile, the EID estimator, combined with a state observer, is responsible for estimating and compensating for the impacts of non-linearity, parameter uncertainties, and disturbances caused by patients with lower-limb impairments. We design a Lyapunov stability function to assess the stability of system. The parameters of the control gain are obtained by linear matrix inequality (LMI). The results confirm the effectiveness of the proposed method.

关键词： Uncertain systems Accuracy Uncertainty Simulation Observers Assistive robots Stability analysis Steady-state Robots Lyapunov methods

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Improved BP Neural Network Utilizing Bayesian optimization Algorithm for Iron-making Process Control

Improved BP Neural Network Utilizing Bayesian optimization A...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Shaoran Wang Jinhua She Seiichi Kawata Feng Wang Juan Zhao Qifu Chen Zhenhong Liao School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

The manufacturing sector covers a wide range of areas, focusing mainly on major equipment manufacturing and process industries, including new energy and new materials manufacturing. The ultimate goal is to realize intelligent manufacturing. Iron and steel metallurgy is an important pillar of the national economy, especially in the field of process industry. In the ironmaking process, a large number of process parameters are systematically collected, forming high-dimensional time series big data. These parameters contain hundreds of influencing factors. key production indicators such as yield, energy consumption and iron quality are closely related to a controlled intermediate variable in the smelting process, the furnace temperature. Silicon content [Si] and the mass percentage of Si in the molten iron is a good example. In order to predict and control the blast furnace ironmaking process more effectively, this paper introduces a BP neural network prediction model, and optimizes and compares the neural network model with genetic algorithm and Bayesian optimization technique.

关键词： Heuristic algorithms Neural networks Process control Predictive models Prediction algorithms Silicon Iron

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Mixed Motivation Driven Social Multi-Agent Reinforcement Learning for Autonomous Driving

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IEEE/CAA Journal of Automatica Sinica 2025年第6期12卷 1272-1282页

作者： Long Chen Peng Deng Lingxi Li Xuemin Hu State Key Laboratory of Multimodal Artificial Intelligence Systems and the State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing WAYTOUS Inc. Beijing Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China School of Computer Science and Information Engineering Hubei University Wuhan China Purdue School of Engineering and Technology Indiana University-Purdue University Indianapolis Indianapolis IN USA School of Artificial Intelligence Hubei University Wuhan Key Laboratory of Intelligent Sensing System and Security (Hubei University) Ministry of Education Wuhan China

Despite great achievement has been made in autonomous driving technologies, autonomous vehicles (AVs) still exhibit limitations in intelligence and lack social coordination, which is primarily attributed to their reliance on single-agent technologies, neglecting inter-AV interactions. Current research on multi-agent autonomous driving (MAAD) predominantly focuses on either distributed individual learning or centralized cooperative learning, ignoring the mixed-motive nature of MAAD systems, where each agent is not only self-interested in reaching its own destination but also needs to coordinate with other traffic participants to enhance efficiency and safety. Inspired by the mixed motivation of human driving behavior and their learning process, we propose a novel mixed motivation driven social multi-agent reinforcement learning method for autonomous driving. In our method, a multi-agent reinforcement learning (MARL) algorithm, called Social Learning Policy Optimization (SoLPO), which takes advantage of both the individual and social learning paradigms, is proposed to empower agents to rapidly acquire self-interested policies and effectively learn socially coordinated behavior. Based on the proposed SoLPO, we further develop a mixed-motive MARL method for autonomous driving combined with a social reward integration module that can model the mixed-motive nature of MAAD systems by integrating individual and neighbor rewards into a social learning objective for improved learning speed and effectiveness. Experiments conducted on the MetaDrive simulator show that our proposed method outperforms existing state-of-the-art MARL approaches in metrics including the success rate, safety, and efficiency. More-over, the AVs trained by our method form coordinated social norms and exhibit human-like driving behavior, demonstrating a high degree of social coordination.

关键词： Measurement Reinforcement learning Safety Driver behavior Autonomous vehicles Optimization

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