摘要
在机器人路径规划过程中,针对传统蚁群算法易陷入局部最优、搜索时间过长等问题,该文提出了一种改进的蚁群算法。用栅格法建立环境模型,引入概率选择函数,降低死锁概率,增加路径平滑度;用Logistic模型的混沌扰动改进信息素的更新方式,提高路径规划的全局搜索能力及收敛速度。仿真结果表明,改进后的蚁群算法减少了路径长度,提高了全局搜索能力且减少了搜索时间。
In the process of robot path planning,in view of the traditional ant colony algorithm is easy to fall into local optimum,the problem such as the search time is too long,this paper proposes an improved ant colony algorithm.Environment model is established with grid method,and the introduction of probability choice function,reduce the probability of a deadlock,increasing path smoothness;Using the Logistic model of chaotic disturbance improved pheromone update methods,improve the global search ability and convergence speed of path planning.The simulation results show that the improved ant colony algorithm reduces the path length,improves the global search ability and reduces the search time.
作者
李静
高俊钗
LI Jing;GAO Jun-chai(College of Electronic and Information Engineering,Xi’an Technological University,Xi’an 710000,China)
出处
《自动化与仪表》
2020年第11期39-43,共5页
Automation & Instrumentation
基金
陕西省重点研发计划项目(2018GY-184)。
