遗传算法在机器人路径规划中的应用

遗传算法在机器人路径规划中的应用摘要移动机器人路径规划作为自主式移动机器人技术的一个重要组成部分，是研究移动机器人技术较为活跃的课题之一，吸引了国内外大批的研究学者。

随着各种新方法和新技术的不断出现，对路径规划的研究有了更广阔的天地。

我国在智能移动机器人研究方面虽然已经取得了一定的成果，如地面自主导航车、水下自主机器人和飞行机器人等。

但由于起步较晚，在研究和应用方面都落后于一些西方国家，而且还没有达到完全实用。

因此，进行这项研究，具有一定的理论和工程应用意义。

首先从移动机器人的历史和现状出发，对比了国内外的不同发展状况，对移动机器人领域的研究方向进行了综述。

着重介绍了移动机器人路径规划中常用的方法，对栅格法、遗传算法等进行了逐一的分析阐述。

应用于机器人路径规划的有很多传统的优化方法，本文主要介绍的最基本的一种算法-遗传算法在机器人路径规划中的应用。

遗传算法（简称GA）是一种借鉴生物界自然选择和自然遗传机制的随机化的搜索算法，它将“适者生存”这一基本的达尔文进化理论引入串结构，并且在串之间进行有组织但又随机的信息交换，伴随着算法的进行，优良的品质被逐渐保留并加以组合，从而不断产生出更佳的个体，也就是不断地接近于最优解。

本文采取了栅格法对机器人工作空间进行划分，用序号标识栅格，并以此序号作为机器人路径规划参数编码。

同时引入间断无障碍路径概念以简化初始种群产生，而且采用了遗传算法操作对初始路径进行寻优，这里遗传算法操作主要指的是选择操作、交叉操作、变异操作；寻优主要是选取适当的个体评价函数及适应函数对路径进行寻优。

最后采用MATLAB对机器人路径进行仿真，静态显示进化过程中生成的路径并显示机器人在障碍物存在情况下避障的运动过程。

对不同参数设置下的路径进行比较，不同种群大小的适应度值进行统计分析，并将不同环境下的最佳路径与最差路径作比较。

传统优化方法在机器人路径规划这类复杂非线性优化问题中缺乏足够的鲁棒性。

遗传算法是国际上80年代中期以来获得广泛应用的一种新型参数优化方法，它基于自然选择原理和群体进化机制，有许多区别于传统优化方法的特点，对机器人路径寻优效果更明显。

关键词遗传算法，机器人，路径规划，优化AbstractTo be a important component of the independent -like migration robot technology, the motion robot way plan is one of more active topics of motion robot technology and has attracted large quantities of the domestic and foreign research scholar. With new method and new technology's unceasing appearance, there is a broader world to research the way plan. Although our country had already made certain progress in intelligent migration robot research aspect, such as ground autonomous navigation vehicle, submarine independent robot and flight robot and so on .But it starts late, it falls behind some Western country in the application aspect and has not achieved completely usability. Therefore, it has certain theory and project application significance to conduct this research. Firstly，from the history and the present situation of moved robot ，comparing the domestic and foreign different development condition , it starts the summary. from the research direction of migration robot. It introduces commonly used method in motion robot way plan emphatically, and has carried on the analysis elaboration one by one to the grid law, the genetic algorithm and so onApplied to the plans in the robot way has many traditional optimized methods, this article mainly introduce the most basic one algorithm - genetic algorithm in the application of the robot way plan .The genetic algorithm (GA) is one kind which profits from the biosphere natural selection and the nature heredity mechanism randomisation searching algorithm, it introduces his basic Darwin Evolution theory of the survival of the fittest” t o string structure, and carries on organized but the stochastic exchange of information between the strings. Following algorithm advance, the fine quality is retained gradually and combined, thus produces a better individual unceasingly and also closes to the optimal solution unceasingly .The article adopts the method of grid work to divide robot space , with the serial numbers to identify grid, and as the parameters of code of robot path planning .And this paper introduces barrier-free path concept to simplify the initial population and using the genetic algorithm operation for the initial route optimization of genetic . The operations mainly refer to selection operation , crossover operation and variation operation. This paper mainly adopts individual evaluation function and The fitness function for optimal selection . Finally using MATLAB to simulate robot path. Traditional optimization methods which is used in this kind of complex nonlinear optimization problems lack of robustness. it displays the path of evolution statically and displays the avoidance movement of the robot in the environment of obstacles .Finally, different optimized paths under different parameters are compared; fitness values of various population sizes are statistically analyzed and best optimized paths and worst optimized paths in different environments are compared as well.. Genetic algorithm is a new parametersoptimization method which has won extensive application in the whole world since the middle of 1980s.It is based on natural selection principle and group evolution mechanism. There are many characteristics which are different from traditional optimization methods and has better effect in robot path optimization.KEY WORDSGenetic Algorithms, robot, path planning, optimize目录摘要 (1)英文摘要 (2)第一章绪论 (6)1.1课题的研究背景 (6)1.2国内外路径规划发展状况 (8)1.3路径规划解决的基本问题 (10)1.4论文主要研究内容（原理）概述 (11)1.4.1遗传算法的基本运算过程如下 (14)1.4.2路径规划的实现 (15)1.4.3 机器人路径最优化的实现 (15)1.5本章小结 (15)第二章机器人路径规划和遗传算法 (16)2.1 路径规划研究概述 (16)2.1.1 路径规划分类 (16)2.1.2 路径规划问题特点 (16)2.1.3路径规划方法 (17)2.1.4 优化问题 (19)2．2遗传算法 (19)2.2.1遗传算法定义 (19)2.2.2 遗传算法特点 (19)2.2.3 编码 (20)2.2.4适应度函数 (20)2.2.5初始群体的选取 (21)第三章基于遗传算法的机器人路径的优化 (22)3.1.参数设定 (22)3.2环境建模 (22)3.3生成初始种群 (23)3.4 适应度函数设置 (24)3.5 选择操作 (25)3.5.1 什么是轮盘赌选择 (26)3.6 交叉操作 (28)3.7 终止条件 (29)第四章仿真及结果分析 (31)4.1 仿真程序 (31)4.2 仿真结果及分析 (35)致谢 (38)参考文献 (39)图列和表列图1 交叉操作示意图 (13)图2 变异操作示意图................................................................................ ..14 图3 机器人路径空间模型........................................................................... . 22 图4 初始种群产生流程图 (23)图5选择操作得流程图 (26)图6转盘法示意图 (27)表1 10个染色体种群按比例的选择过程 (27)图7交叉操作示意图 (28)图8整个优化过程流程图 (30)第一章绪论1.1课题的研究背景现代机器人的研究始于20世纪中期，其技术背景是计算机和自动化的发展，以及原子能的开发利用。