• DocumentCode
    2609750
  • Title

    Research on path planning and TSP based on genetic algorithm and Hopfield neural network

  • Author

    Yang, Lingxiao ; Zhou, Huanzhang

  • Author_Institution
    Henan Polytech. Univ., Jiaozuo, China
  • fYear
    2011
  • fDate
    27-29 June 2011
  • Firstpage
    657
  • Lastpage
    659
  • Abstract
    In the mobile robot technology, path planning is an important question. In this paper it gets the model information of global static environment by raster method, and by the genetic algorithm it can obtain the population diversity of the optimal planning path avoiding successfully obstacles and going through the designated points, then Hopfield neural network is used to solve the traveling salesman problem(TSP) and optimal round-trip problem of passing through the designed points, the proposed method is feasible and effective , the simulation results in the Matlab show better optimization effect.
  • Keywords
    Hopfield neural nets; genetic algorithms; mobile robots; path planning; travelling salesman problems; Hopfield Neural Network; TSP; genetic algorithm; global static environment; mobile robot technology; optimal round trip problem; path planning; raster method; traveling salesman problem; Computational modeling; Genetic algorithms; Hopfield neural networks; Mathematical model; Mobile robots; Optimization; Path planning; Hopfield neural network; genetic algorithm; mobile robot; path planning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Science and Service System (CSSS), 2011 International Conference on
  • Conference_Location
    Nanjing
  • Print_ISBN
    978-1-4244-9762-1
  • Type

    conf

  • DOI
    10.1109/CSSS.2011.5974120
  • Filename
    5974120