摘要
针对蚁群算法在路径规划中盲目搜索、搜索速度慢和路径平滑性差等问题,提出一种改进的蚁群算法,以提高其搜索效果。基于A^(*)算法快速规划出初始路径,对蚁群初始信息素进行非均匀分配,提高算法收敛速度。在蚁群算法的状态转移概率公式中引入动态目标导向函数,同时在信息素更新策略中考虑路径转角数和路径匝数,通过优劣质蚂蚁的分层信息素更新来优化路径长度和平滑性。结合动态窗口法使机器人具备良好的局部动态避障功能,通过仿真实验验证了改进蚁群算法在规划和避障方面的良好性能。
To solve the problems of blind search,low search speed and poor path smoothness of ant colony algorithm in path planning,an improved ant colony algorithm was proposed to improve its search effect.The initial path was quickly planned based on the A^(*) algorithm,and the initial pheromone of the ant colony was non-uniformly distributed to improve the convergence speed of the algorithm.A dynamic goal-oriented function was introduced into the state transition probability formula of the ant colony algorithm,and the number of path corners and path turns were considered in the pheromone update strategy,and the path length and smoothness were optimized through hierarchical pheromone updates of good and bad ants.Combined with the dynamic window method,the robot has good local dynamic obstacle avoidance function,and the good performance of the improved ant colony algorithm in planning and obstacle avoidance is verified through simulation experiments.
作者
金将
王小平
臧铁钢
姜世阔
赵崟
JIN Jiang;WANG Xiao-ping;ZANG Tie-gang;JIANG Shi-kuo;ZHAO Yin(College of Mechanical and Electrical Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China)
出处
《计算机工程与设计》
北大核心
2025年第4期950-958,共9页
Computer Engineering and Design
关键词
机器人
路径规划
蚁群算法
信息素
栅格法
动态窗口法
局部避障
robot
path planning
ant colony algorithm
pheromones
grid method
dynamic window approach
local obstacle avoidance
