NEAT-NC: NEAT guided Navigation Cells for Robot Path Planning

TL;DR

This paper introduces NEAT-NC, a biologically inspired neural network evolution method for improved robot path planning in dynamic environments, leveraging navigation cells akin to hippocampal functions.

Contribution

It develops NEAT-NC, integrating navigation cells into NEAT to enhance real-time path planning in complex, changing environments, inspired by biological spatial cognition.

Findings

NEAT-NC outperforms traditional methods in dynamic scenarios.

The approach demonstrates adaptability to various environment complexities.

Biological principles improve neural network evolution for navigation tasks.

Abstract

To navigate a space, the brain makes an internal representation of the environment using different cells such as place cells, grid cells, head direction cells, border cells, and speed cells. All these cells, along with sensory inputs, enable an organism to explore the space around it. Inspired by these biological principles, we developed NEATNC, a Neuro-Evolution of Augmenting Topology guided Navigation Cells. The goal of the paper is to improve NEAT algorithm performance in path planning in dynamic environments using spatial cognitive cells. This approach uses navigation cells as inputs and evolves recurrent neural networks, representing the hippocampus part of the brain. The performance of the proposed algorithm is evaluated in different static and dynamic scenarios. This study highlights NEAT's adaptability to complex and different environments, showcasing the utility of biological theories. This suggests that our approach is well-suited for real-time dynamic path planning for robotics and games.