摘要
针对移动机器人路径规划的难题,运用了一种基于遗传模拟退火算法的移动机器人最优路径规划方法,对移动机器人的路径规划进行了设计,采用了栅格法对环境进行建模。为了提高路径规划的效率,采用了一种改进的避障算法来生成初始种群。将遗传算法与模拟退火算法相结合形成遗传模拟退火算法,新算法具有较强的全局和局部搜索能力。仿真实验结果证明算法相对于基本遗传算法的收敛速度、搜索质量和最优解输出概率方面有了明显的提高。
Aimed at the path planning problem of mobile robot, a genetically simulated annealing algorithm of optimum path planning for mobile robots is proposed. In this paper, path planning of mobile robot is designed,grid is used to make environmental modeling. An improved obstacle avoidance algorithm is introduced to generate the initial population in order to improve the path planning efficiency. This paper develops a genetic simulated annealing algorithm by combining the genetic algorithm with simulated annealing algorithm. The new algorithm has better capability of searching globally and locally. The simulation results demonstrate that the proposed algorithm has achieved considerable improvements, in convergence speed, search quality and optimal solution output rate compared to the basic genetic algorithm.
出处
《计算机仿真》
CSCD
北大核心
2009年第12期118-121,125,共5页
Computer Simulation
关键词
移动机器人
遗传算法
模拟退火算法
路径规划
Mobile robot
Genetic algorithm
Simulated annealing algorithm
Path planning
