Unmanned aerial vehicle obstacle avoidance flight method based on digital space slices

Abstract

The invention discloses an unmanned aerial vehicle obstacle avoidance flight method based on digital spatial slices. The method comprises the following steps of: step 1, acquiring a picture shot by anunmanned aerial vehicle; 2, adjusting the resolution of the picture and performing processing to obtain a grey-scale image; 3, for the single grayscale image, extracting a plurality of slices from different regions of the picture to form a set I by using a digital spatial slicing algorithm; 4, simultaneously inputting the slice picture sets in the set I into a neural network, and outputting corresponding obstacle calibration information to form a set R; 5, estimating the position and direction of an obstacle according to the set R; 6, planning a flight mode of the unmanned aerial vehicle, andsetting a yaw angle and a speed; and step 7, enabling the flight control system of the unmanned aerial vehicle to perform motion control according to the acquired speed and yaw angle information. Themethod has a very good effect on tree detection in an under-forest environment, and hardware adopted by the method is light in weight and low in energy consumption, and therefore, the method is suitable for being used on a small unmanned aerial vehicle.

Description

Technical field [0001] The invention relates to the technical field of UAV obstacle avoidance flight technology, in particular to a UAV obstacle avoidance flight method based on digital space slices. Background technique [0002] In the past few decades, the methods of UAV avoidance have been extensively studied, including obstacle detection during flight and flight decision-making, that is, how UAVs fly to avoid obstacles. Current obstacle detection technologies include point cloud processing based on lidar, depth information processing based on depth cameras, and drone positioning and navigation algorithms based on visual SLAM. [0003] Due to the large mass of lidar, it will seriously affect the flight of the drone when it is mounted on the drone, and the drone is more bulky when flying. At the same time, processing the data of lidar and depth camera requires a great amount of calculation, so the power consumption is relatively large. At this time, in order to ensure the fligh...

AI Technical Summary

Problems solved by technology

[0005] At the same time, when flying in a complex environment, because there are too many obstacles in the environment and it is difficult to classify them, and there are many types of targets, the method of target detection cannot achieve good results.

[0006] In summary, the current method is difficult to implement on small UAVs, and the effect is poor when the environment is complex and changeable

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