摘要
针对传统栅格法的缺陷,提出了一种蜂巢栅格法对环境建模,同时采用分布均匀度自适应蚁群算法对移动机器人在复杂静态环境下进行路径规划。该算法利用蜂巢栅格安全性和有效性兼顾的属性对环境进行建模,通过聚度和信息权重来动态的调整路径选择概率和信息素更新;使得在算法加速收敛和防止早熟、停滞现象之间取得很好的平衡。与传统蚁群比较进行仿真实验,结果证明本算法在3项重要参数上都明显优于传统蚁群算法。从而说明,在任意已知静态障碍物的复杂环境下,本算法能准确快速的规划出安全的最优路径,结果较为满意。
To overcome the shortcomings of the traditional grid method,we use a honeycomb grid to model its environment and the distribution-uniform adaptive ant colony algorithm for mobile robot path planning in a complex static environment. The algorithm uses the safety and effectiveness of honeycomb grid through the cohesion and the information weights to dynamically adjust the path selection probability and pheromone updating,makes the algorithm quickly converge,avoids prematurity and stagnation and achieves a good balance. The simulation results demonstrate that the algorithm is significantly superior to the traditional ant colony algorithm in terms of three important parameters. Thus,in the environment of any known complex static obstacles,the algorithm can quickly,accurately and safely plan the optimal path of a mobile robot and produce satisfactory results.
出处
《机械科学与技术》
CSCD
北大核心
2016年第8期1308-1312,共5页
Mechanical Science and Technology for Aerospace Engineering
基金
教育部留学回国人员项目(DB201406147)资助
关键词
移动机器人
蜂巢栅格
蚁群算法
复杂静态环境
mobile robot
honeycomb grid
ant colony algorithm
complex static obstacle
