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FasFast Triangulation and local optimization for Scan Data of Laser Radar

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Journal of Shanghai Jiaotong university(Science) 2010年第1期15卷 1-5页

作者：石路杜正春姚振强洪迈生 School of Mechanical Engineering State Key Laboratory of Mechanical System and Vibration Shanghai Jiaotong University

In order to study the triangulation for the point cloud data collected by three-dimensional laser radar,in accordance with the line-by-line characteristics of laser radar scanning,an improved Delaunay triangulation method is proposed to mesh the point cloud data as a triangulation irregular *** on the geometric topology location information among radar point cloud data,focusing on the position relationship between adjacent scanning line of the point data,a preliminary match network is obtained according to their geometric relationship.A reasonable triangulation network for the object surface is acquired after the use of local optimization on initial mesh by Delaunay ***,a new judging rule is proposed to contrast the triangulation before and after the optimization on the *** result shows that triangulation for point cloud with full use of its own characteristics can improve the speed of the algorithm obviously,and the rule for judging the triangulation can evaluate the quality of network.

关键词： triangulation local optimization point cloud data laser radar

Objective Variation Simplex Algorithm for Continuous Piecewise Linear Programming

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Tsinghua Science and Technology 2017年第1期22卷 73-82页

作者： Yu Bai Zhiming Xu Xiangming Xi Shuning Wang Department of Automation Tsinghua National Laboratory for Information Science and Technology(TNList)Tsinghua UniversityBeijing100084China Faculty of College of Science Air Force Engineering UniversityXi'an 710051 Department of Automation Tsinghua UniversityBeijing 100084China

This paper works on a modified simplex algorithm for the local optimization of Continuous Piece Wise Linear（CPWL） programming with generalization of hinging hyperplane objective and linear constraints. CPWL programming is popular since it can be equivalently transformed into difference of convex functions programming or concave optimization. Inspired by the concavity of the concave CPWL functions, we propose an Objective Variation Simplex Algorithm（OVSA）, which is able to find a local optimum in a reasonable time. Computational results are presented for further insights into the performance of the OVSA compared with two other algorithms on random test problems.

关键词： local optimization continuous piecewise linear programming modified simplex algorithm

Longitudinal parameter identification of a small unmanned aerial vehicle based on modified particle swarm optimization

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Chinese Journal of Aeronautics 2015年第3期28卷 865-873页

作者： Jiang Tieying Li Jie Huang Kewei College of Mechanical and Electrical Engineering Beijing Institute of Technology

Abstract This paper describes a longitudinal parameter identification procedure for a small unmanned aerial vehicle （UAV） through modified particle swam optimization （PSO）. The proce- dure is demonstrated using a small UAV equipped with only an micro-electro-mechanical systems （MEMS） inertial mea,mring element and a global positioning system （GPS） receiver to provide test information. A small UAV longitudinal parameter mathematical model is derived and the modified method is proposed based on PSO with selective particle regeneration （SRPSO）. Once modified PSO is applied to the mathematical model, the simulation results show that the mathematical model is correct, and aerodynamic parameters and coefficients of the propeller can be identified accurately. Results are compared with those of PSO and SRPSO and the comparison shows that the proposed method is more robust and faster than the other methods for the longitudinal parameter identification of the small UAV. Some parameter identification results are affected slightly by noise, but the identification results are very good overall. Eventually, experimental validation is employed to test the proposed method, which demonstrates the usefulness of this method.

关键词： Aerodynamic parameters local optimization Parameter identification Particle swarm optimization(PSO) Small unmanned aerialvehicle

Sediment fingerprinting in fluvial systems:review of tracers,sediment sources and mixing models

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International Journal of Sediment Research 2013年第4期28卷 560-578页

作者： Arman HADDADCHI Darren S.RYDER Olivier EVRARD Jon OLLEY Australian Rivers Institute Griffith University School of Environmental and Rural Sciences University of New England Laboratoire des Sciences du Climat et de l'Environnement(LSCE/IPSL)

Suspended sediments in fluvial systems originate from a myriad of diffuse and point sources,with the relative contribution from each source varying over time and *** process of sediment fingerprinting focuses on developing methods that enable discrete sediment sources to be identified from a composite sample of suspended *** review identifies existing methodological steps for sediment fingerprinting including fluvial and source sampling,and critically compares biogeochcmical and physical tracers used in fingerprinting *** of applying different mixing models to the same source data are explored using data from 41 catchments across Europe,Africa,Australia,Asia,and North and South *** application of seven commonly used mixing models to two case studies from the US(North Fork Broad River watershed) and France(BIcone watershed) with local and global(genetic algorithm) optimization methods identified all outputs remained in the acceptable range of error defined by the original *** propose future sediment fingerprinting studies use models that combine the best explanatory parameters provided by the modified Collins(using correction factors) and Hughes(relying on iterations involving all data,and not only their mean values) models with optimization using genetic algorithms to best predict the relative contribution of sediment sources to fluvial systems.

关键词： watershed fluvial sampling source tracing modeling local optimization genetic algorithm

Dynamic collision avoidance for cooperative fixed-wing UAV swarm based on normalized artificial potential field optimization

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Journal of Central South University 2021年第10期28卷 3159-3172页

作者： LIU Wei-heng ZHENG Xin DENG Zhi-hong State Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China China Aerospace Science and Industrial Corporation Beijing 100048China

Cooperative path planning is an important area in fixed-wing UAV ***,avoiding multiple timevarying obstacles and avoiding local optimum are two challenges for existing approaches in a dynamic ***,a normalized artificial potential field optimization is proposed by reconstructing a novel function with anisotropy in each dimension,which can make the flight speed of a fixed UAV swarm independent of the repulsive/attractive gain coefficient and avoid trapping into local optimization and local ***,taking into account minimum velocity and turning angular velocity of fixed-wing UAV swarm,a strategy of decomposing target vector to avoid moving obstacles and pop-up threats is ***,several simulations are carried out to illustrate superiority and effectiveness.

关键词： fixed-wing UAV swarm cooperative path planning normalized artificial potential field dynamic obstacle avoidance local optimization

Two-Phase Genetic Algorithm Applied in the optimization of Multi-Modal Function

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Wuhan University Journal of Natural Sciences 2003年第S1期8卷 259-264页

作者： Huang Yu-zhen, Kang Li-shan,Zhou Ai-minState Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, Hubei,China Wuhan University State Key Laboratory of Software Engineering Wuhan Hubei China (GRID:grid.49470.3e) (ISNI:***)

This paper presents a two-phase genetic algorithm (TPGA) based on the multi- parent genetic algorithm (MPGA). Through analysis we find MPGA will lead the population' s evol vement to diversity or convergence according to the population size and the crossover size, so we make it run in different forms during the global and local optimization phases and then forms TPGA. The experiment results show that TPGA is very efficient for the optimization of low-dimension multi-modal functions, usually we can obtain all the global optimal solutions.

关键词： optimization of multi-modal function genetic algorithm global optimization local optimization

An Improved optimization Algorithm Applied in Image Processing

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An Improved Optimization Algorithm Applied in Image Processi...

Mobile Robot Vision Odometer Based on Point-Line Features and Graph optimization

第32届中国控制与决策会议

作者： Ping Zhang John Douglas Thomson Yining Zhang Physics Department Anshan Normal University Graduate School of Business and Law RMIT University

Aim to improve the default of Harmony Search（HS） algorithm which is easily trapping into local optima while doing global *** improved optimization algorithm presented in this *** historical iteration information to set up an instructive dataset,then the global search mechanism is combined with a local search *** that,an integer planning study based on distance strategy and the fuzzy constraint processing in spatial division of solution *** proposed model combines with FCM applied in image *** experimental results show that the proposed method can get more accuracy and less CPU operation time versus FCM and s-FCM.

关键词： Harmony search algorithm Image Processing FCM local optimization

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Mobile Robot Vision Odometer Based on Point-Line Features an...

Contract and Advance Cancellation of MRI Time Slots for Stroke Patients

第30届中国控制与决策会议

作者： Zhe Jia Jianwei Leng Tianjin University of Technology School of Electrical and Electronic Engineering

In order to solve the problem of real-time and accuracy of autonomous positioning of mobile robot, a monocular visual distance calculation algorithm based on local and global optimization of point-line feature fusion is proposed. First of all, feature points and Canny edge features are extracted from the input image, and the point features are detected by ORB features with better robustness and real-time performance. After that, we use the similarity function of the feature block to find the optimal matching position and solve the resulting interior point for the incremental estimation of the robot pose. Finally, based on the idea of graph optimization in SLAM, this paper designs a pose optimization method based on nonlinear optimization, which can achieve the optimization of local pose and the global pose optimization to reduce the accumulation of position and attitude errors and trajectory drift. The experimental results show that the algorithm meets the real-time requirements, and can significantly reduce the pose error, with higher positioning accuracy.

关键词： mobile robot feature matching pose estimation local optimization graph optimization

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Contract and Advance Cancellation of MRI Time Slots for Stro...