[1]高岳林,武少华.基于自适应粒子群算法的机器人路径规划[J].郑州大学学报(工学版),2020,41(04):46-51.[doi:10.13705/j.issn.1671-6833.2020.01.004]
　GAO Yuelin,WU Shaohua.Robot Path Planning Based on Adaptive Particle Swarm Optimization[J].Journal of Zhengzhou University (Engineering Science),2020,41(04):46-51.[doi:10.13705/j.issn.1671-6833.2020.01.004]
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基于自适应粒子群算法的机器人路径规划()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
41卷
期数:
2020年04期
页码:
46-51
栏目:
出版日期:
2020-08-12

文章信息/Info

Title:
Robot Path Planning Based on Adaptive Particle Swarm Optimization
作者:
高岳林; 武少华
1. 北方民族大学宁夏智能信息与大数据处理重点实验室2. 宁夏大学数学统计学院
Author(s):
GAO Yuelin1; WU Shaohua2
1.Ningxia Key Laboratory of Intelligent Information and Big Data Processing,North Minzu University,Yinchuan 750021,China;2.School of Mathematics and Statistics,Ningxia University,Yinchuan 750021,China
关键词:
粒子群算法; 模拟退火算法; 机器人路径规划; 三次样条插值
Keywords:
particle swarm algorithm' target="_blank" rel="external">">particle swarm algorithm; simulated annealing algorithm; Robot path planning; cubic spline interpolation; 
DOI:
10.13705/j.issn.1671-6833.2020.01.004
文献标志码:
A
摘要:
针对粒子群算法在解决机器人路径规划中存在易陷入局部最优，路径搜索后期收敛速度慢以及路径不平滑的问题，提出一种基于模拟退火的改进自适应的粒子群算法，该算法结合了模拟退火算法的粒子群算法的优点，路径搜索前期路径搜索速度快，路径搜索过程中路径具有概率突跳对的能力，能够有效地避免陷入局部最优路径，而且利用3次样条插值使路径平滑，路径搜索后期路径收敛精度也很高。仿真结果表明，该算法在不同障碍物模型中均能够快速找到最短的平滑路径，而且效果优于传统方法。
Abstract:
Particle swarm optimization algorithm was easy fall into local optimum,the convergence speed was slow in the late path search,and the path was not smooth in the robot path planning.An improve simulated annealing adaptive particle swarm optimization algorithm was proposed.The algorithm combined the advantages of simulated annealing and particle swarm optimization.In the early stage of the algorithm route search was fast,and the algorithm had the ability of sudden jump in the path search process,which could effectively avoid falling into the local optimal path.Using cubic spline interpolation smooth the path,and the convergence precision of the late search path was high.The simulation results showed that the algorithm could quickly find the shortest smooth path in different obstacle models,and the path effect was better than the traditional method.

参考文献/References:

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更新日期/Last Update: 2020-10-07