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
Link To Document :
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