运动规划
蚁群优化算法
计算机科学
路径(计算)
机器人
蚁群
移动机器人
算法
最短路径问题
人工智能
作者
Tao Wang,Lianyu Zhao,Yunhui Jia,Jutao Wang
出处
期刊:International Conference on Robotics and Automation
日期:2018-08-01
被引量:14
标识
DOI:10.1109/wrc-sara.2018.8584217
摘要
In the robot path planning, the basic ant colony algorithm is used to find the optimal path, there are some questions of long search time, low efficiency, and easily falling into local optimum. In this paper, the ant colony algorithm is improved for these problems. The introduction of artificial potential field method as the main means of path planning puts forward the principle of unbalanced initial pheromone. Different grid positions assign different initial pheromone and join pheromone trajectory smoothing strategy. Comparing the two kinds of ant colony algorithm and carrying on the simulation analysis, the improved ant colony algorithm is better than the basic ant colony mainly embodied in algorithm in searching ability, more efficient in algorithm and shorter the searching path. The experimental results show that the improved algorithm can improve the efficiency of the algorithm and restrain the algorithm from falling into the local optimum and realize the optimal path search of the robot so that the robot can quickly avoid the obstacle safely reaching the target point.
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