摘要
针对寻找机器人在障碍环境下到达特定目标最短路径问题,提出一种基于改进蚁群算法的路径规划方法。该算法通过赋予蚂蚁类似于人的方向感,使其具备局部路径思考能力,同时在蚁群算法中引入确定性选择和随机性选择相结合的方法,以及确定性选择概率和信息素挥发系数自适应调整策略,极大地改善了蚁群算法的全局搜索能力和搜索速度,并且显著地提高了算法寻找最优解的能力。在基于栅格地图的仿真测试条件下,该方法在不同问题规模和障碍条件下,均能达到很好的优化结果,并且满足实时路径规划的搜索速度要求。
In order to solve the problem of finding the shortest path to a specific target for robots, this paper presented a novel path planning method based on improved ant colony algorithm. The method made the ant have local path thinking ability by endowing it with sense of direction similar to human beings. The thought of combining deterministic selection and random selection, as well as adaptive adjustment strategy of deterministic selection probability and pheromone evaporation coefficient, were also adopted. These means greatly improved the global search ability of the ant colony algorithm and accelerated the search speed, as well as improved its ability to find optimal solution. In the simulation environment based on grid map, under different conditions of problem size and obstacle distribution, the method could always find the optimization results and meet the speed requirement for real-time path planning.
出处
《机械与电子》
2013年第7期61-64,共4页
Machinery & Electronics
基金
中国科学院科技创新基金项目(CXJJ-10-M16)
关键词
路径规划
障碍环境
改进蚁群算法
path planning
obstacle environ- ment
improved ant colony algorithm
