Autonomous full transversal wall-climbing robot

Abstract

The invention relates to an autonomous full traversal wall-climbing robot. The autonomous full traversal wall-climbing robot relates to the method comprising the steps that an area to be detected of the wall-climbing robot is divided into a plurality of grids by using a grid method, and each grid is used as a neuron to obtain a biological excitation neural network map; the activity of neurons around the current position of a wall-climbing robot body is calculated; a neuron of which the activity is greater than that of the neurons at the current position is selected as the neuron to be selected; for each neuron to be selected, the heuristic value of peripheral neurons of each neuron to be selected to the each neuron to be selected is calculated; according to the model of motion energy consumption, the motion energy consumption of the robot body moving from the current position to the corresponding position of the neuron to be selected is calculated; and the selected neuron with the minimum motion energy consumption is selected as the next target position of the robot body. The autonomous full traversal wall-climbing robot can autonomously complete the full traversal task in a low energy consumption way.

Description

technical field [0001] The invention belongs to the technical field of wall-climbing robots, in particular to an autonomous full-traversal wall-climbing robot. Background technique [0002] Common mobile robot full traversal planning methods include template model method, unit decomposition method, biologically inspired neural network algorithm, etc. The template model method completes the traversal of the region by continuously searching the module library, but due to the lack of overall planning of the environment, the path planning by this method has a high repetition rate. The unit decomposition method divides the area to be traversed into several sub-areas according to the distribution of obstacles, and completes the traversal of the entire area by traversing each sub-area. The traversal algorithm based on the unit decomposition method can simplify complex problems, but there is no better method to solve the connection problem between sub-regions. The bio-inspired neu...

AI Technical Summary

Problems solved by technology

This method is labor-intensive, the level of intelligence is not high, and there will be problems that some areas are not traversed due to remote operation and the traversal route is unreasonable, and it will further cause excessive energy consumption.

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