计算机科学 ›› 2018, Vol. 45 ›› Issue (11A): 97-100.

• 智能计算 • 上一篇    下一篇

室内环境下基于最优路径规划的PSO-ACO融合算法

刘俊, 徐平平, 武贵路, 彭杰   

  1. 东南大学移动国家重点实验室 南京210096
  • 出版日期:2019-02-26 发布日期:2019-02-26
  • 通讯作者: 徐平平(1957-),女,教授,博士生导师,主要研究方向为5G无线网络、绿色通信、SDN和车载网络,E-mail:xpp@seu.edu.cn
  • 作者简介:刘 俊(1992-),男,硕士生,主要研究方向为物联网应用,E-mail:yolosliu@163.com;武贵路(1986-),男,博士生,主要研究方向为5G无线网络和车载网络;彭 杰(1994-),男,硕士生,主要研究方向为5G无线网络。
  • 基金资助:
    本文受东南大学移动国家重点实验室开放研究基金资助课题(61771126)资助。

PSO-ACO Fusion Algorithm Based on Optimal Path Planning in Indoor Environment

LIU Jun, XU Ping-ping, WU Gui-lu, PENG Jie   

  1. National Mobile Communications Laboratory,Southeast University,Nanjing 210096,China
  • Online:2019-02-26 Published:2019-02-26

摘要: 为了使移动机器人在室内障碍物环境下寻找到达指定目的地的最优路径,提出了一种基于粒子群算法(PSO)和蚁群算法(ACO)的改进路径规划的PSO-ACO融合算法。PSO-ACO融合算法针对粒子群算法中粒子容易早熟引起的局部最优问题,采用蚁群算法获得全局最优解;同时有效地解决了粒子群算法中粒子多样性、种类少,以及蚁群算法中初始化信息素匮乏及耗时过多的问题。仿真结果表明,与粒子群算法和蚁群算法相比,PSO-ACO融合算法在提高算法的全局搜索能力和搜索速度的前提下,极大地改善了算法寻找最优解的能力,实现了最优路径的规划。

关键词: PSO-ACO融合算法, 粒子群算法, 室内环境, 蚁群算法, 最优路径规划

Abstract: In order to find the optimal path for mobile robot to reach the specified destination in indoor obstacle environment,an improved PSO-ACO fusion algorithm based on particle swarm optimization (PSO) and ant colony algorithm (ACO) was proposed.In PSO-ACO fusion algorithm,ant colony algorithm is used to obtain the global optimal solution for the local optimal problem caused by premature particle in particle swarm optimization algorithm.At that same time,the problem of small variety of particles in the PSO algorithm and lack of initialization pheromone and time consume in the ACO algorithm are effectively solved.Simulation results show that PSO-ACO fusion algorithm can greatly improve the ability of searching the optimal solution and realize the optimal path planning under the premise of improving the global search ability and search speed of the algorithm compared with particle swarm optimization and ant colony algorithm.

Key words: Ant colony algorithm, Indoor environment, Optimal path planning, Particle swarm optimization, PSO-ACO fusion algorithm

中图分类号: 

  • TP242.2
[1]XU R,MIAO D,LIU L,et al.An Optimal Travel Route Plan for Yangzhou Based on the Improved Floyd Algorithm[C]∥2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber,Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData).Exeter,2017:168-177.
[2]HASUIKE T,TSUBAKI H,KATAGIRI H,et al.A Flexible Tour Route Planning Problem with Time-Dependent Parameters Considering Rescheduling Based on Current Conditions[C]∥2013 IEEE International Conference on Systems,Man,andCyberneti-cs.Manchester,2013:2091-2096.
[3]CHEN X,ZHOU M,HUANG J,et al.Global path planning using modified firefly algorithm[C]∥2017 International Symposium on Micro-NanoMechatronics and Human Science (MHS).Nagoya,Japan,2017:1-7.
[4]ZHU B,LI C,SONG L,et al.A* algorithm of global path planning based on the grid map and V-graph environmental model for the mobile robot[C]∥2017 Chinese Automation Congress (CAC).Jinan,2017:4973-4977.
[5]CHEN D,LU Q,YIN K,et al.A method for solving local minimum problem of local path planning based on particle swarm optimization[C]∥2017 Chinese Automation Congress (CAC).Jinan,2017:4944-4949.
[6]CHEN Y,LU Q,YIN K,et al.PSO-based receding horizon control of mobile robots for local path planning[C]∥IECON 2017-43rd Annual Conference of the IEEE Industrial Electronics So-ciety.Beijing,2017:5587-5592.
[7]LEE D,JEONG J,KIM Y H,et al.An improved artificial potential field method with a new point of attractive force for a mobile robot[C]∥2017 2nd International Conference on Robotics and Automation Engineering(ICRAE).Shanghai,2017:63-67.
[8]XUE Y H,LIU H P.Optimal Path Planning for Service Robot in Indoor Environment[C]∥2010 International Conference on Intelligent Computation Technology and Automation.Changsha,2010:850-853.
[9]KENNEDY J,EBERHART R C.Particle swarm optimization[C]∥Proceedings of IEEE International Conference on Neural Network.1995:1942-1948.
[10]HASAN R A,AMOHAMMED M,TAPU S N,et al.A comprehensive study:Ant Colony Optimization (ACO) for facility layout problem[C]∥2017 16th RoEduNet Conference Networking in Education and Research(RoEduNet).2017:1-8.
[11]DORIGO M,MANIEZZO V,COLORNI A.The ant system:optimization by a colony of cooperating agents[J].IEEE Transactions on Systems Man and Cybernetics Part B:Cybernetics,1996,26(1):29-41.
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