NEAT-NC: NEAT guided Navigation Cells for Robot Path Planning
For robotics and game AI, this work offers a bio-inspired path planning method that adapts to dynamic environments, but the improvements over existing NEAT are incremental.
NEAT-NC integrates navigation cells (place, grid, head direction, etc.) into the NEAT algorithm to improve path planning in dynamic environments. The approach evolves recurrent neural networks representing the hippocampus, showing adaptability in static and dynamic scenarios.
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.