Intelligent robot control method, system and device based on biological neural network

Abstract

The invention belongs to the field of computational neurosciences and intelligent robots, particularly relates to an intelligent robot control method, system and device based on a biological neural network, and aims to solve the problem that an existing hippocampus heuristic pulse biological neural network model cannot realize complex mode control of an intelligent robot. The method comprises thefollowing steps: constructing a feature extraction and feature association learning neural network based on the structure and function heuristics of a biological hippocampus sub-region, and processingan intelligent robot environment image; obtaining the behavior category of the intelligent robot based on the obtained feature vector by adopting a classification neural network; and obtaining an intelligent robot control command through the intelligent robot behavior category-control command relationship. According to the method, the anti-noise performance of the biological neural network modelis greatly improved, the correct rate of object sensing under a complex background and object recognition under a high-noise background is improved, the robustness of the network model is good, and aneffective decision-making method is provided for NAO intelligent robot control.

Description

technical field [0001] The invention belongs to the field of computational neuroscience and intelligent robots, and in particular relates to a biological neural network-based intelligent robot control method, system and device. Background technique [0002] With the continuous advancement of science and technology, the technology related to robots has also developed by leaps and bounds, and has begun to develop in the direction of intelligence. In traditional robot control technology, to make it act according to human intentions, it is usually to use remote control or a pre-trained program to control the robot to perform related actions. Visual jitter, blurring or other noise-like information pose great challenges to model stabilization control. Now some intelligent robot control methods based on bionic biological neural networks can already be separated from the remote control and the action program set in advance to realize bionic, intelligent, and highly robust intellige...

AI Technical Summary

Problems solved by technology

[0007] In order to solve the above-mentioned problems in the prior art, that is, the problem that the existing hippocampus-inspired pulse biological neural network model cannot realize the complex mode control of intelligent robots, the present invention provides a biological neural network-based intelligent robot control method, the intelligent robot Control methods include:

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