Path planning method and device based on spiking neural network

Abstract

The invention discloses a path planning method and device based on an impulsive neural network. The method comprises steps of (1) dividing a map into a target region, a normal passing region and an obstacle region, modeling the map into the impulsive neural network, enabling each region to correspond to a neuron of the impulsive neural network, and enabling each neuron to be connected through twounidirectional synapses; (2) initializing neuron parameters and synaptic parameters corresponding to the target area; (3) at each moment, updating the membrane potential of each neuron according to the pulse condition of the incoming synapse and the self-charging current, sending a pulse signal when the membrane potential is greater than a potential threshold, transmitting the pulse signal to thenext neuron through the synapse, and returning the membrane potential to zero after the neuron transmits the pulse signal; (4) at each moment, updating a trace value of each synapse according to whether the pulse signal is transmitted or not, and updating the weight of the synapse according to the trace value; and (5) after the transmission of the pulse signal is finished, planning a path according to the weight value of the synapse to obtain a path result.

Description

technical field [0001] The invention belongs to the field of impulse neural networks, and in particular relates to a path planning method and device based on impulse neural networks. Background technique [0002] Artificial Neural Network (ANN, Artificial Neural Network) is a series of information processing systems that imitate the biological neural structure and its learning, memory and other functions. It has many advantages such as parallel processing, distributed information storage and autonomous learning. Since the concept of deep learning was put forward, the research of neural network has made rapid development, and it has been widely used in many fields, and has achieved remarkable results. [0003] Spiking Neural Network (SNN, Spiking Neural Network), known as the third-generation artificial neural network, is an artificial neural network based on discrete neural impulse information. Compared with the artificial neural network, the neurons in each layer of the sp...

AI Technical Summary

Problems solved by technology

Traditional path planning methods such as the Dijkstra algorithm or A* algorithm are difficult to perform a dynamic planning process, or perform a large number of calculations in the dynamic process, resulting in a sharp increase in time costs. For example, the patent application with the application publica

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