【摘要】：當(dāng)今社會(huì)科技高速發(fā)展，人們的生活水平不斷提高，越來越多的人擁有私家車，，人們?cè)絹碓诫x不開車輛導(dǎo)航系統(tǒng)，而智能車輛導(dǎo)航系統(tǒng)中最核心的部分就是路徑選擇算法,所以本文是解決人們生活中的實(shí)際問題。 論文第一章介紹了我國目前交通道路上存在的一些問題，解決這些問題最好的辦法是交通智能化，利用車輛導(dǎo)航系統(tǒng)使車輛能夠在道路上井井有條的行駛。本章概述了智能車輛導(dǎo)航系統(tǒng)，研究了它的背景和意義，對(duì)其發(fā)展現(xiàn)狀作了簡單的介紹了，構(gòu)思了本章論文的研究內(nèi)容。第二章主要研究了智能車輛導(dǎo)航系統(tǒng)的基本框圖及其主要功能，此外，智能車輛導(dǎo)航系統(tǒng)技術(shù)用到了多個(gè)學(xué)科里面的各種技術(shù)方法，但是用到的關(guān)鍵技術(shù)有六種。第三章介紹了圖論中圖的相關(guān)知識(shí)，確定了實(shí)際的交通路網(wǎng)可以抽象為一個(gè)賦權(quán)有向圖，研究了路網(wǎng)的兩種連通性表達(dá)和三種存儲(chǔ)結(jié)構(gòu)。 第四章是本文的重點(diǎn)，本章討論了智能車輛導(dǎo)航系統(tǒng)中路徑選擇算法，研究了目前應(yīng)用最廣泛的Dijkstra算法、Floyd算法和基本蟻群算法；研究了這三種算法的主要思想，這些算法實(shí)現(xiàn)步驟和流程，針對(duì)初始的時(shí)候基本蟻群算法容易陷入局部最優(yōu)的缺陷已做了一點(diǎn)改進(jìn)，把蟻群算法數(shù)學(xué)模型中轉(zhuǎn)移概率公式做了一些簡化，并將其加入一個(gè)放大因子，通過蟻群算法實(shí)驗(yàn)仿真可知，改進(jìn)蟻群算法提高了搜索效率。 最后，以Windows7系統(tǒng)為開發(fā)平臺(tái)，以MatlabR2008a為開發(fā)工具，搭建了智能導(dǎo)航最短路徑規(guī)劃系統(tǒng)，完成了對(duì)四種算法的集成，實(shí)現(xiàn)了四種方法的路徑規(guī)劃，本文還利用實(shí)際數(shù)據(jù)完成了對(duì)系統(tǒng)的測(cè)試。
[Abstract]:Nowadays, with the rapid development of social science and technology, people's living standard is improving, more and more people own private cars, and people are more and more inseparable from vehicle navigation system, and the most important part of intelligent vehicle navigation system is path selection algorithm. So this paper is to solve the actual problems in people's lives. The first chapter introduces some problems existing in the traffic road in our country. The best way to solve these problems is to use the vehicle navigation system to make the vehicle run orderly on the road. This chapter summarizes the intelligent vehicle navigation system, studies its background and significance, makes a brief introduction to its development status, and conceive the research content of this chapter. The second chapter mainly studies the basic block diagram of intelligent vehicle navigation system and its main functions. In addition, intelligent vehicle navigation system technology uses a variety of technical methods in many disciplines, but there are six key technologies used. The third chapter introduces the related knowledge of graph in graph theory, determines that the actual traffic network can be abstracted as a weighted directed graph, and studies two kinds of connectivity representation and three storage structures of road network. The fourth chapter is the focus of this paper, this chapter discusses the intelligent vehicle navigation system path selection algorithm, the most widely used Dijkstra algorithm, Floyd algorithm and basic ant colony algorithm; The main ideas of these three algorithms are studied. The steps and flow of these algorithms are discussed. The basic ant colony algorithm is easy to fall into local optimum at the beginning. The formula of transfer probability in the mathematical model of ant colony algorithm is simplified, and an amplification factor is added to it. Through the simulation of ant colony algorithm experiment, the improved ant colony algorithm improves the search efficiency. Finally, using Windows7 system as the development platform and MatlabR2008a as the development tool, the intelligent navigation shortest path planning system is built, the integration of four algorithms is completed, and the path planning of the four methods is realized. The test of the system is completed by using the actual data.
【學(xué)位授予單位】：中原工學(xué)院
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP301.6;U495

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