A novel solution for maze traversal problems using artificial neural networks

S. Srinivasan, D. P. Mital, S. Haque

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

In this paper we have addressed the problem of finding a path through a maze of a given size. The traditional ways of finding a path through a maze employ recursive algorithms in which unwanted or non-paths are eliminated in a recursive manner. Neural networks with their parallel and distributed nature of processing seem to provide a natural solution to this problem. We present a biologically inspired solution using a two level hierarchical neural network for the mapping of the maze as also the generation of the path if it exists. For a maze of size S the amount of time it takes would be a function of S (O(S)) and a shortest path (if more than one path exists) could be found in around S cycles where each cycle involves all the neurons doing their processing in a parallel manner. The solution presented in this paper finds all valid paths and a simple technique for finding the shortest path amongst them is also given. The results are very encouraging and more applications of the network setup used in this report are currently being investigated. These include synthetic modeling of biological neural mechanisms, traversal of decision trees, modeling of associative neural networks (as in relating visual and auditory stimuli of a given phenomenon) and surgical micro-robot trajectory planning and execution.

Original languageEnglish (US)
Pages (from-to)563-572
Number of pages10
JournalComputers and Electrical Engineering
Volume30
Issue number8
DOIs
StatePublished - Nov 1 2004

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science(all)
  • Electrical and Electronic Engineering

Keywords

  • Artificial neural networks
  • Maze generation
  • Neural model
  • Path finding

Fingerprint Dive into the research topics of 'A novel solution for maze traversal problems using artificial neural networks'. Together they form a unique fingerprint.

Cite this