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Autonomous landing method for unmanned aerial vehicle

A technology of unmanned aerial vehicles and landing points, which is applied in the field of autonomous landing of unmanned aerial vehicles, can solve the problems of unsatisfactory efficiency, type and accuracy of safe autonomous landing, and achieve strong practical value and flexibility, wide practicability, and safety The effect of landing precision

Active Publication Date: 2017-12-08
深圳草莓创新技术有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

During the actual flight of the UAV, the corresponding ground conditions below it are much more complicated, such as pedestrians, vehicles, roads, roofs, farmland, trees, water surfaces, etc. , can no longer meet the efficiency, type and accuracy requirements of safe and autonomous landing of UAVs during flight

Method used

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  • Autonomous landing method for unmanned aerial vehicle
  • Autonomous landing method for unmanned aerial vehicle
  • Autonomous landing method for unmanned aerial vehicle

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specific Embodiment approach

[0025] In order to further elaborate the technical solution described in the present invention, in conjunction with the accompanying drawings, the specific implementation of the present invention is as follows:

[0026] Such as figure 1 As shown, the present invention provides a method for autonomous landing of a UAV. The landing method uses a camera on the UAV to capture ground images, and uses the image semantic segmentation algorithm model obtained through deep learning model training to identify the types and types of each area of ​​the ground image. Boundary, using the ground type and the size of the ground area to judge the best landing point, the specific process of the method is as follows:

[0027] Step 1: Take ground images through the drone equipped with a camera;

[0028] Step 2: Identify the types and boundaries of each region of the captured ground image through the image semantic segmentation algorithm model;

[0029] The image semantic segmentation algorithm ...

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Abstract

The invention discloses an autonomous landing method for an unmanned aerial vehicle. According to the autonomous landing method, a camera is carried on the unmanned aerial vehicle to shoot ground images, an image semantic partitioning algorithm model obtained through training of a deep learning model is adopted to identify types and boundaries of all areas of the ground images, and the best landing pint is judged through ground types and ground area sizes. When emergency circumstances occur to the unmanned aerial vehicle in air, the complex ground environment can be automatically identified through the autonomous landing method, after selection, the unmanned aerial vehicle efficiently and accurately lands on a safe place, and the flight safety of the unmanned aerial vehicle is greatly promoted.

Description

technical field [0001] The invention belongs to the technical field of unmanned aerial vehicle flight safety control. Specifically, it relates to a method for autonomously landing an unmanned aerial vehicle. Background technique [0002] Multi-rotor UAVs have been more and more widely used in recent years, such as image capture, disaster detection, power inspection, agricultural plant protection and other fields. In the future, UAV logistics will also truly enter the lives of ordinary people. Since drones and their equipment are often expensive, if an accident occurs during flight, it will not only seriously damage the drone and equipment, but also cause unpredictable damage to ground facilities. If it hurts pedestrians, the consequences will be unimaginable . At present, technical solutions for drone flight safety have emerged one after another, such as: using various sensors for active obstacle avoidance, using communication systems to inform each other of the current po...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): B64D45/08G06K9/00
CPCB64D45/08G06V20/13G06V20/10B64U70/00
Inventor 侯亮王大亮刘芷宁杨概张凤阁
Owner 深圳草莓创新技术有限公司
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