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Brain-like navigation method based on multi-scale grid cell path integration

A grid cell and navigation method technology, applied in neural learning methods, navigation computing tools, biological neural network models, etc., can solve problems such as poor robustness and inaccurate navigation

Active Publication Date: 2021-04-13
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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AI Technical Summary

Problems solved by technology

[0006] In order to solve the poor robustness and inaccurate navigation problems existing in the satellite rejection and unknown complex environment of the existing UAV navigation method, the present invention proposes a brain-inspired navigation method based on multi-scale grid cell path integration

Method used

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  • Brain-like navigation method based on multi-scale grid cell path integration
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  • Brain-like navigation method based on multi-scale grid cell path integration

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Experimental program
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specific Embodiment approach

[0081] 1. Obtain the self-motion information (speed / heading angle) of the UAV

[0082] The vision processing module receives the images collected by the drone's visual sensor in real time, and calculates the linear velocity and heading angle of the drone in the forward and height directions according to the pixel value changes of two adjacent frames of images.

[0083] 2. Construction of a 3D mesh cell network model

[0084] The three-dimensional grid cell network model is constructed based on the three-dimensional attractor neural network. The input is the linear velocity and heading angle of the UAV in the forward and height directions, and the output is the discharge rate wave packet of the grid cell. The topology of the network is as follows: figure 2 As shown, the grid model is:

[0085]

[0086] in, is the activity matrix composed of the firing rates of all cells in the three-dimensional grid cell network at the current moment, γ is the remainder matrix, is the ...

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Abstract

The invention relates to a brain-like navigation method based on multi-scale grid cell path integration, and belongs to the field of navigation positioning and artificial intelligence. According to the method, a position neural mechanism is calculated by referring to mammal intracerebral olfactory cortex multi-scale grid cell network path integration and a hippocampus position cell cluster network. The method comprises the following steps: firstly, constructing a three-dimensional multi-scale grid cell network model based on an exponential gain factor and a three-dimensional attractor neural network, and encoding self-motion information (speed / heading) of the unmanned aerial vehicle into a multi-scale grid cell discharge rate wave packet; then, constructing a position cell cluster neural network model, and decoding the multi-scale grid cell discharge rate wave packet into unmanned aerial vehicle three-dimensional position information; the invention provides a robust, accurate and intelligent brain-like navigation method in a three-dimensional and large-scale space, which can be used for satellite denial and intelligent autonomous navigation and positioning of an unmanned aerial vehicle in an unknown complex environment.

Description

technical field [0001] The invention relates to a brain-like navigation method based on multi-scale grid cell path integration, which belongs to the field of navigation positioning and artificial intelligence. Background technique [0002] UAV is an abbreviation for a power-driven, unmanned, and reusable aircraft. It has broad application prospects in military and civilian fields such as reconnaissance, search and rescue, flight performances, and surveying and mapping. Navigation, as one of the core technologies of drones, is the premise and basis for the smooth operation of drones. [0003] Currently, drones flying in complex environments such as satellite denial and unknown environments mainly use the real-time positioning and map building (SLAM) system based on vision / lidar / inertial sensors for navigation and positioning. Due to the need to pre-build accurate SLAM mathematics The model cannot better adapt to complex and unknown environments and realize accurate intellige...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01C21/20G06N3/04G06N3/08
CPCG01C21/20G06N3/04G06N3/08
Inventor 杨闯熊智刘建业华冰晁丽君陈雨荻王雅婷戴嘉伟
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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