期刊文献+

基于改进粒子群优化算法的路径规划 被引量:8

Path Planning Based on Improved Particle Swarm Optimization
下载PDF
导出
摘要 针对二维静态环境下移动机器人路径规划问题,该文提出一种改进的粒子群算法求解最优路径。首先,由于传统的粒子群算法初始化粒子时并未考虑到粒子初始位置是否占障碍物空间,没有对占障碍物空间的粒子进行处理,导致粒子初始有效性低下,全局寻优不准确和全局寻优时间长。然后,为解决此问题,在初始化时采用一种修正粒子算法,解决初始时粒子有效性低下的问题。比较传统粒子群算法和该文算法的仿真结果。仿真结果表明,采用这种方法极大限度地增大了初始粒子的有效性,使算法迭代时可以更加快速准确地得到全局最优路径,所提方法有效可行。 For mobile robot path planning problem in two-dimensional static environment,an improved particle swarm optimization algorithm is proposed to solve the optimal path. Firstly,because the traditional particle swarm optimization algorithm initializes the particles without considering whether the initial position of the particles occupies the obstacle space,there is no processing of the particles occupying the obstacle space,resulting in low initial validity of the particles,global optimization inaccuracy and global search. Excellent time. Then,in order to solve this problem,a modified particle algorithm is used in the initialization to solve the problem of low particle validity at the initial stage. Compare the traditional particle swarm optimization algorithm with the simulation results of the proposed algorithm. The simulation results show that the method can greatly increase the effectiveness of the initial particles,and the algorithm can obtain the global optimal path more quickly and accurately. The proposed method is effective and feasible.
作者 支金柱 黄姣茹 郭婧 李长红 ZHI Jin-zhu;HUANG Jiao-ru;GUO Jing;LI Chang-hong(School of Electronic Information Engineering,Xi’an Technological University,Xi’an 710021,China;Northwest Institute of Mechanical and Electrical Engineering,Xianyang 210000,China)
出处 《自动化与仪表》 2020年第4期34-38,共5页 Automation & Instrumentation
基金 国家重点研发计划项目(2016YFE0111900) 陕西省国际科技合作与交流项目(2017KW-009,2018KW-022)。
关键词 移动机器人 路径规划 粒子群算法 栅格法 mobile robot path planning particle swarm optimization grid method
  • 相关文献

参考文献4

二级参考文献48

  • 1于红斌,李孝安.基于栅格法的机器人快速路径规划[J].微电子学与计算机,2005,22(6):98-100. 被引量:63
  • 2陈贵敏,贾建援,韩琪.粒子群优化算法的惯性权值递减策略研究[J].西安交通大学学报,2006,40(1):53-56. 被引量:309
  • 3钟石泉,贺国光.有时间窗约束车辆调度优化的一种禁忌算法[J].系统工程理论方法应用,2005,14(6):522-526. 被引量:35
  • 4冯翔,陈国龙,郭文忠.粒子群优化算法中加速因子的设置与试验分析[J].集美大学学报(自然科学版),2006,11(2):146-151. 被引量:22
  • 5Kennedy J, Eberhart R. Particle Swarm Optimization[C]//Proc IEEE Int Conf on Neural Networks. Perth: Perth IEEE Press, 1995: 1942-1948.
  • 6Shi Yuhui, Eberhart R. Parameter Selection in Particle Swarm Optimization[C]//IEEE Proc of the 7th Annual Conf on Evolutionary Programming. Washington: Springer-Verlag, 1998: 591-600.
  • 7Kenneay J, Eherhart R, Sift Yuhui. Swarm Intelligence[M]. San Francisco: .Morgan Kaufman, 2001.
  • 8Wang Hui, Qian Feng. Improved PSO-based Multi-Objective Optimization Using Inertia Weight and Acceleration Coefficients Dynamic Changing, Crowding and Mutation [C] //Proceedings of the 7th World Congress on Intelligent Control and Automation. Chongqing: IEEE, 2008: 4479-4484.
  • 9Juan C, Cabrera F, Carlos A, et al. Handling Constraints in Particle Swarm Optimization Using a Small Population Size [C]//MICAI 2007: Advouees in Artificial Intelligence. Aguasealientes: IEEE, 2007: 41-45.
  • 10KENNEDY J, EBERHART R C. Particle swarmoptimization[C]//Proceedings of IEEE InternationalConference on Neural Networks. [S.l.]. IEEE, 1995:1942-1948.

共引文献212

同被引文献100

引证文献8

二级引证文献83

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部