發(fā)布時間：2018-03-08 09:28

  本文選題：人工魚群算法　切入點：混合算法　出處：《安徽工程大學》2017年碩士論文　論文類型：學位論文

【摘要】：在移動機器人導航技術(shù)的研究中,路徑規(guī)劃作為一個必不可少的組成部分,具有十分重要研究意義。移動機器人的路徑規(guī)劃是從起始位置開始,按照預先設(shè)置的條件,移動到目標位置且避開所有障礙物的最短路徑。群智能優(yōu)化算法是模擬生物的各種行為,把行為轉(zhuǎn)換成對數(shù)學問題的求解與優(yōu)化,將自然界中的個體轉(zhuǎn)化成優(yōu)化空間中的點進行求解,把種群對環(huán)境的適應力轉(zhuǎn)變成求解數(shù)學模型中的目標函數(shù)。本文針對群智能算法中的人工魚群算法進行改進,得到混合魚群算法并將其應用于移動機器人的路徑規(guī)劃。主要研究內(nèi)容有如下三個方面:首先,針對移動機器人的研究背景和研究意義進行了詳細的闡述;分析了國內(nèi)外移動機器人路徑規(guī)劃方法的研究現(xiàn)狀和發(fā)展趨勢;對移動機器人環(huán)境建模方法進行了分析比較,基于柵格法理論,建立了移動機器人路徑規(guī)劃的環(huán)境模型。其次,將粒子群算法的線性遞減慣性權(quán)重策略引入到人工魚群算法,提出一種新的粒子群與人工魚群的混合算法,該算法提高了人工魚的搜索效率和最優(yōu)解的精確度,通過經(jīng)典的旅行商問題測試了算法的性能;并將該混合算法應用于移動機器人的路徑規(guī)劃,通過數(shù)值仿真說明了本文提出算法的優(yōu)越性和有效性。最后,在傳統(tǒng)人工魚群算法中引入多策略混合機制,利用加權(quán)平均距離策略,擴大了人工魚的視野范圍;采用對數(shù)函數(shù)作為步長的移動因子,克服了傳統(tǒng)固定步長的缺陷;進一步利用高斯變異策略擴大了種群的多樣性;同時給出了基于多策略混合人工魚群算法的移動機器人路徑規(guī)劃步驟,實驗仿真結(jié)果表明了該方法迭代速度更快、尋優(yōu)效果更好。
[Abstract]:In the research of mobile robot navigation technology, path planning, as an essential part of the research, is of great significance. The path planning of mobile robot starts from the starting position, according to the pre-set conditions. Moving to the target position and avoiding the shortest path of all obstacles, the swarm intelligence optimization algorithm simulates various behaviors of biology, and converts behavior into solving and optimizing mathematical problems. The individuals in nature are transformed into the points in the optimization space and the adaptability of the population to the environment is transformed into the objective function in solving the mathematical model. This paper improves the artificial fish swarm algorithm in the swarm intelligence algorithm. The hybrid fish swarm algorithm is obtained and applied to the path planning of mobile robot. The main research contents are as follows: firstly, the research background and significance of mobile robot are described in detail; This paper analyzes the research status and development trend of mobile robot path planning methods at home and abroad, analyzes and compares the modeling methods of mobile robot environment, establishes the environment model of mobile robot path planning based on grid method theory. The linear decreasing inertia weight strategy of particle swarm optimization algorithm is introduced into artificial fish swarm algorithm, and a new hybrid algorithm of particle swarm and artificial fish swarm is proposed. The algorithm improves the search efficiency and the accuracy of optimal solution of artificial fish. The performance of the algorithm is tested by classical traveling salesman problem, and the hybrid algorithm is applied to path planning of mobile robot. Numerical simulation shows the superiority and effectiveness of the proposed algorithm. In the traditional artificial fish swarm algorithm, the multi-strategy hybrid mechanism is introduced, the weighted average distance strategy is used to enlarge the field of vision of the artificial fish, and the logarithmic function is used as the moving factor of the step size, which overcomes the defect of the traditional fixed step size. Furthermore, the diversity of population is expanded by using Gao Si mutation strategy, and the path planning steps of mobile robot based on multi-strategy hybrid artificial fish swarm algorithm are given. The simulation results show that the iterative speed of this method is faster. The optimization effect is better.
【學位授予單位】：安徽工程大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP18;TP242

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