基于混合鱼群算法的移动机器人路径规划研究

发布时间：2018-03-08 09:28

本文选题：人工鱼群算法　切入点：混合算法　出处：《安徽工程大学》2017年硕士论文　论文类型：学位论文

【摘要】：在移动机器人导航技术的研究中,路径规划作为一个必不可少的组成部分,具有十分重要研究意义。移动机器人的路径规划是从起始位置开始,按照预先设置的条件,移动到目标位置且避开所有障碍物的最短路径。群智能优化算法是模拟生物的各种行为,把行为转换成对数学问题的求解与优化,将自然界中的个体转化成优化空间中的点进行求解,把种群对环境的适应力转变成求解数学模型中的目标函数。本文针对群智能算法中的人工鱼群算法进行改进,得到混合鱼群算法并将其应用于移动机器人的路径规划。主要研究内容有如下三个方面:首先,针对移动机器人的研究背景和研究意义进行了详细的阐述;分析了国内外移动机器人路径规划方法的研究现状和发展趋势;对移动机器人环境建模方法进行了分析比较,基于栅格法理论,建立了移动机器人路径规划的环境模型。其次,将粒子群算法的线性递减惯性权重策略引入到人工鱼群算法,提出一种新的粒子群与人工鱼群的混合算法,该算法提高了人工鱼的搜索效率和最优解的精确度,通过经典的旅行商问题测试了算法的性能;并将该混合算法应用于移动机器人的路径规划,通过数值仿真说明了本文提出算法的优越性和有效性。最后,在传统人工鱼群算法中引入多策略混合机制,利用加权平均距离策略,扩大了人工鱼的视野范围;采用对数函数作为步长的移动因子,克服了传统固定步长的缺陷;进一步利用高斯变异策略扩大了种群的多样性;同时给出了基于多策略混合人工鱼群算法的移动机器人路径规划步骤,实验仿真结果表明了该方法迭代速度更快、寻优效果更好。
[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.
【学位授予单位】：安徽工程大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP18;TP242

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