蚁群优化算法
模拟退火
运动规划
遗传算法
算法
计算机科学
自适应模拟退火
机器人
路径(计算)
地形
任意角度路径规划
数学优化
人工智能
数学
机器学习
地理
地图学
程序设计语言
作者
Lanfei Wang,Jun Guo,Qu Wang,Jiangming Kan
标识
DOI:10.1109/cyberc.2018.00081
摘要
Robot path planning is the key to robot navigation. We implemented the robot path planning based on ant colony algorithm and genetic algorithm, and proposed simulated annealing genetic algorithm. Under the condition that there is not much difference in running time (within 3 seconds), planning results of different terrains, start and end points based on ant colony algorithm(with 200 iterations)and simulated annealing genetic algorithm show that, the optimal path outputted by simulated annealing genetic algorithm is better than the optimal path outputted by ant colony algorithm in terms of avoiding obstacles; The simulated annealing genetic algorithm has shorter average optimal path length than ant colony algorithm in multiple tests, the average path length is reduced by 6.85%.
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