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2013 (Vol. 5, Issue: 23)
Article Information:

Robot Path Planning Based on Simulated Annealing and Artificial Neural Networks

Xianmin Wei
Corresponding Author:  Xianmin Wei 

Key words:  Energy function, markov chain, network weight, robot path planning, simulated annealing artificial neural network, ,
Vol. 5 , (23): 5384-5390
Submitted Accepted Published
November 12, 2012 January 11, 2013 May 28, 2013

As for the limitations of algorithms in global path planning of mobile robot at present, this study applies the improved simulated annealing algorithm artificial neural networks to path planning of mobile robot in order to better the weaknesses of great scale of iteration computation and slow convergence, since the best-reserved simulated annealing algorithm was introduced and it was effectively combined with other algorithms, this improved algorithm has accelerated the convergence and shortened the computing time in the path planning and the global optimal solution can be quickly obtained. Because the simulated annealing algorithm was updated and the obstacle collision penalty function represented by neural networks and the path length are treated as the energy function, not only does the planning of path meet the standards of shortest path, but also avoids collisions with obstacles. Experimental results of simulation show this improved algorithm can effectively improve the calculation speed of path planning and ensure the quality of path planning.
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  Cite this Reference:
Xianmin Wei, 2013. Robot Path Planning Based on Simulated Annealing and Artificial Neural Networks.  Research Journal of Applied Sciences, Engineering and Technology, 5(23): 5384-5390.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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