摘要
为了解决经典蚁群优化算法应用于移动机器人路径规划中存在综合寻优能力差、收敛速度慢和复杂环境中算法鲁棒性不强等问题,提出了一种基于位置和能耗启发的改进蚁群优化算法。综合考虑机器人行进路径长度、行进路径坡度和转弯带来的能耗问题,提出综合能耗启发因子;考虑路径起点与终点之间,直线距离最短,提出到起止点直线距离启发因子,引导蚂蚁往起止点直线附近路径靠近;提出到终点距离启发因子,引导蚂蚁往目标点方向行进。设计了综合三种启发因子的启发函数,优化状态转移计算方式。此外,通过引入动态信息素挥发因子、改进信息素增量、设计信息素限制等优化信息素更新策略。多种环境多次仿真实验结果对比分析表明,改进算法在寻优路径长度、路径高度均方差、综合性能等方面具有更加优秀的表现。
In order to solve the problems of poor comprehensive optimization ability,slow convergence speed,and weak algorithm robustness in the application of classic ant colony algorithm in mobile robot path planning,a modified ant colony optimization algorithm based on location and energy consumption heuristic is proposed.Considering the robot’s travel path length,travel path slope,and energy consumption caused by turning,a comprehensive energy consumption heuristic factor is proposed.Considering that between the starting point and the end point of path,straight-line distance is the shortest a straight-line distance heuristic factor is proposed to guide the ants to approach the path around the straight from starting point to the end point.A distance heuristic factor to the end point is proposed to guide the ants to move towards the target point.A heuristic function combining three heuristic factors is designed to optimize mode of the state transition calculation.In addition,by introducing dynamic pheromone evaporation factor,improving pheromone increment,and designing pheromone constraints,the pheromone update strategy is optimized.Comparative analysis of multiple simulated experiments in various environments shows that the improved algorithm has more excellent show in optimizing path length,path height variance,and comprehensive performance.
作者
李春青
黄勇萍
刘娟
LI Chunqing;HUANG Yongping;LIU Juan(College of Mathematics,Physics and Electronic Information Engineering,Guangxi Minzu Normal University,Chongzuo 532200,China;Unit 63892 of PLA,Luoyang 471003,China)
出处
《传感器与微系统》
CSCD
北大核心
2024年第10期132-136,共5页
Transducer and Microsystem Technologies
基金
2022年度广西高校中青年教师科研基础能力提升项目(2022KY0767)。
关键词
蚁群优化算法
路径规划
能耗启发因子
移动机器人
ant colony optimization algorithm
path planning
energy consumption heuristic factor
mobile robot
