CLC number: TP242.6
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 0000-00-00
Cited: 9
Clicked: 8230
DU Xin, CHEN Hua-hua, GU Wei-kang. Neural network and genetic algorithm based global path planning in a static environment[J]. Journal of Zhejiang University Science A, 2005, 6(6): 549-554.
@article{title="Neural network and genetic algorithm based global path planning in a static environment",
author="DU Xin, CHEN Hua-hua, GU Wei-kang",
journal="Journal of Zhejiang University Science A",
volume="6",
number="6",
pages="549-554",
year="2005",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2005.A0549"
}
%0 Journal Article
%T Neural network and genetic algorithm based global path planning in a static environment
%A DU Xin
%A CHEN Hua-hua
%A GU Wei-kang
%J Journal of Zhejiang University SCIENCE A
%V 6
%N 6
%P 549-554
%@ 1673-565X
%D 2005
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2005.A0549
TY - JOUR
T1 - Neural network and genetic algorithm based global path planning in a static environment
A1 - DU Xin
A1 - CHEN Hua-hua
A1 - GU Wei-kang
J0 - Journal of Zhejiang University Science A
VL - 6
IS - 6
SP - 549
EP - 554
%@ 1673-565X
Y1 - 2005
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2005.A0549
Abstract: mobile robot global path planning in a static environment is an important problem. The paper proposes a method of global path planning based on neural network and genetic algorithm. We constructed the neural network model of environmental information in the workspace for a robot and used this model to establish the relationship between a collision avoidance path and the output of the model. Then the two-dimensional coding for the path via-points was converted to one-dimensional one and the fitness of both the collision avoidance path and the shortest distance are integrated into a fitness function. The simulation results showed that the proposed method is correct and effective.
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Open peer comments: Debate/Discuss/Question/Opinion
<1>
amir@bah<amirbahrami50@yahoo.com>
2015-07-20 14:24:19
a good paper
sanaz@deh<s.dehghanipur@gmail.com>
2013-05-16 17:31:57
neural network