期刊文献+

蚁群算法在移动机器人路径规划中的应用 被引量:1

Application of ant colony algorithm in mobile robot path planning
下载PDF
导出
摘要 路径规则是移动机器人领域中较为关注的热点话题,蚁群算法当前在移动机器人路径规划中有着广泛应用空间和价值,路径规划的主要目的是实现在处于具有障碍物的工作环境中实现合理依据相关性能指标落实在模型空间当中寻找到最优的路线,从起点到目标位置的碰撞次数最少。但是不可否认,当前蚁群算法在移动机器人路径规划中的应用中依然存在各种问题有待完善,未能够将其利用优势全面展现出来。因此,本文将围绕蚁群算法在移动机器人路径规划中的应用为主题来展开分析,再提出促进在移动机器人路径规划中应用蚁群算法的可行性对策。 Path rule is a hot topic in the field of mobile robot.Ant colony algorithm has a wide application space and value in mobile robot path planning.The main purpose of path planning is to achieve reasonable realization in the working environment with obstacles,and find the optimal route in the model space according to the relevant performance indicators The number of collisions is the least.However,it is undeniable that there are still various problems in the application of ant colony algorithm in mobile robot path planning,which need to be improved,and the advantages of ant colony algorithm are not fully displayed.Therefore,this paper will focus on the application of ant colony algorithm in mobile robot path planning as the theme to carry out the analysis,and then put forward the feasible countermeasures to promote the application of ant colony algorithm in mobile robot path planning.
作者 段焜 Duan Kun(Jiangsu Vocational College of safety technology,Xuzhou Jiangsu,221011)
出处 《电子测试》 2020年第21期117-118,共2页 Electronic Test
关键词 蚁群算法 移动机器人路径规划 应用 ant colony algorithm mobile robot path planning application
  • 相关文献

参考文献5

二级参考文献44

  • 1黄翰,郝志峰,吴春国,秦勇.蚁群算法的收敛速度分析[J].计算机学报,2007,30(8):1344-1353. 被引量:72
  • 2Derek J Bennet, Colin R McInnes. Distributed control of multirobot systems using bifurcating potential fields[J].Robotics and Autonomous Systems, 2010,58 (3) : 256 - 264.
  • 3Dorigo M, Maniezzo V, Colomi A. Ant system: optimization by a colony of cooperating agent[ J]. IEEE Transactions on Systems, Man, and Cybernetics, 1996,26( 1 ) :29 - 41.
  • 4Lim Kwee Kim, Ong Yew-Soon,Lim Meng Hiot,et al.Hybrid ant colony algorithms for path planning in sparse graphs E J]. Soft Computing, 2008,12(10) :981 - 994.
  • 5Garcia M A Porta, Montiel Oscar, Casfillo Oscar, et al. Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation[ J]. Applied Soft Computing,2009,9(3) : 1102 - 1110.
  • 6Stutzle T, Hoos H H. Max-min ant system and local search for the travelling salesman problem[ A]. IEEE International Conference on Evolutionary Computation[ C ]. Indianapolis: IEEE Press, 1997.309 - 314.
  • 7Botee H M, Bonabeau E. Evolving ant colony optimization [J].Compex System, 1998,1 (2) : 149 - 159.
  • 8BI Xiao-jun,LUO Guang-xin. The improvement of ant colony algorithm based on the inver-over operator[ A]. IEEE International Conference on Mechatronics and Automation [C ]. Harbin: IEEE Press, 2007.2383 - 2387.
  • 9Kennedy J, Eberhart R C. Particle swarm optimization[ A]. IEEE International Conference on Neural Networks [ C ]. Piscataway, NJ: IEEE Press, 1995.1942 - 1948.
  • 10Asl,L B,Nezhad, V M. Improved particle swarm optimization for dual-channel speech enhancement [A]. International Conference on Signal Acquisition and Processing[C]. Bangalore, India: IEEE Press,2010.13- 17.

共引文献202

同被引文献9

引证文献1

二级引证文献2

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