Position Estimation Based on Grid Cells and Self-Growing Self-Organizing Map.

Baozhong Li,Yanming Liu,Hailin Li

doi:10.1155/2019/3606397

Abstract

As the basis of animals' natal homing behavior, path integration can continuously provide current position information relative to the initial position. Some neurons in freely moving animals' brains can encode current positions and surrounding environments by special firing patterns. Research studies show that neurons such as grid cells (GCs) in the hippocampus of animals' brains are related to the path integration. They might encode the coordinate of the animal's current position in the same way as the residue number system (RNS) which is based on the Chinese remainder theorem (CRT). Hence, in order to provide vehicles a bionic position estimation method, we propose a model to decode the GCs' encoding information based on the improved traditional self-organizing map (SOM), and this model makes full use of GCs' firing characteristics. The details of the model are discussed in this paper. Besides, the model is realized by computer simulation, and its performance is analyzed under different conditions. Simulation results indicate that the proposed position estimation model is effective and stable.

Highlights

Rapid development of unmanned vehicles has raised the research of autonomous navigation in recent years [1]
The relationship between the firing of grid cells (GCs) and the vehicle’s position is discussed, and we mainly focus on the position estimation performance that the proposed model can decode from GCs’ firing patterns
The analysis of this model is based on the comparison between the position information encoded by GCs’ firing patterns and the position information decoded by the proposed model

Summary

Introduction

Rapid development of unmanned vehicles has raised the research of autonomous navigation in recent years [1]. E above-mentioned models are mainly designed to simulate or reproduce the firing patterns of cells to explore the mechanism of cells’ firing from the biological point of view, not for the sake of practical navigation application or improvement of navigation performance. E encoding of spatial environments and positions by navigation-related cells’ firing patterns is just the first step in the process of perceived information processing. Is paper puts forward a neural network model to decode the GCs’ encoding information based on the improved traditional Kohonen self-organizing map (SOM), the details of which can be found in the section of Supplementary Materials. Erefore, this study is beneficial to explore the neural mechanism of animals’ navigation behavior as well as to provide important reference to develop bionic autonomous navigation architecture for unmanned vehicles.

Data Model

Position Estimation with SGSOM

Results and Analysis

Conclusions and Discussion