Cellular automata for solving mazes
Dr. Dobb's Journal
Neural network dynamics for path planning and obstacle avoidance
Neural Networks
Neurosolver: Neuromorphic general problem solver
Information Sciences: an International Journal
Introduction to Neural and Cognitive Modeling
Introduction to Neural and Cognitive Modeling
A model of spatial map formation in the hippocampus of the rat
Neural Computation
Neural-network-based path planning for a multirobot system with moving obstacles
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews - Special issue on information reuse and integration
Path planning strategy for autonomous mobile robot navigation using Petri-GA optimisation
Computers and Electrical Engineering
Human typical action recognition using gray scale image of silhouette sequence
Computers and Electrical Engineering
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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.