Abstract: Mammals have the ability to sense exogenous information and self-motion information according to the navigation cells in the brain for self-positioning and navigation,which provides a good biological model for the development of intelligent and adaptive UAV(unmanned aerial vehicle) navigation methods.In this paper,we study the neural mechanism of key navigation cells and the information fusion modelling method,and propose a brain-inspired navigation method based on place cell and grid cell information fusion to address the problems of low real-time loop detection and sparse detection points in the traditional vision-based brain-inspired cognitive map construction method.Firstly,we use continuous attractor neural network and isotropic Gaussian network to model grid cell and position cell respectively,and achieve path integration and position measurement,based on which we propose grid cell zeroing algorithm to improve the efficiency of grid cell wide range calculation.Secondly,the connection weight matrix of the two cell networks is obtained by Hebb-learning rule,which realises the real-time correction process of the position cell to the grid cell path integral.Finally,the 3D position of the UAV is obtained based on the neuron population vector weighted average as well as the grid cell vertex position processing decoding method.The experimental results show that the method proposed in this paper can accurately decode the three-dimensional position of the UAV in a wide range of space,which improves the accuracy compared with the traditional navigation and positioning algorithms,and further expands the application scope of the three-dimensional brain-inspired navigation method for UAVs.

Key words: brain-inspired navigation, place cell, grid cell, hebb learning, attractor neural network