A neuro-based network for on-line topological map building and dynamic path planning

This paper presents a novel combination method for on-line topological map building and dynamic path planning. The proposed method consists of two main components: Bayesian Adaptive Resonance Associative Memory (Bayesian ARAM) and forward-backward propagation path planner. Bayesian ARAM incrementally clusters sensory information and generates topological map. The explored environment is described as a group of neurons (nodes) and edges. Each neuron (nodes) represents a distinct place and it is defined as multi-dimensional Gaussian distribution which does not require any prior knowledge of what a place is supposed to be to make it works in natural environment. The topological map is incrementally generated by Bayesian ARAM. The forward-backward propagation path planner consists of two process: forward propagation determines the possible path while backward propagation with neuron pruning eliminates inefficient neurons and determines the optimum pathway from current location to target location based on the generated map information. The effectiveness of our proposed method is validated by several standardized benchmark datasets.