Unmanned aerial vehicle movement speed prediction method based on machine learning

A technology of motion speed and prediction method, which is applied in the direction of navigation through speed/acceleration measurement, navigation calculation tools, etc., and can solve problems such as difficulties and difficult estimation of model parameters

Active Publication Date: 2016-07-27
SHANGHAI JIAO TONG UNIV
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Problems solved by technology

However, in practical applications, it is difficult to implement because the model parameters are difficult to estimate.

Method used

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  • Unmanned aerial vehicle movement speed prediction method based on machine learning
  • Unmanned aerial vehicle movement speed prediction method based on machine learning
  • Unmanned aerial vehicle movement speed prediction method based on machine learning

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Embodiment Construction

[0038] The following will combine Figure 1 to Figure 5 The machine learning-based UAV motion speed prediction method provided by the present invention is described in detail, which is an optional embodiment of the present invention. It can be considered that those skilled in the art can, without changing the spirit and content of the present invention, Modify and polish it.

[0039] First of all, it should be pointed out that the present invention mainly describes the motion speed estimation method in the fixed height mode.

[0040] The fixed height state means that the height of the drone is kept constant and the drone can move freely in the horizontal direction. See the force analysis diagram figure 1 . According to simple derivation, the expression of movement translation speed in fixed height mode can be obtained:

[0041]

[0042] among them Local coordinate system The component of the orthogonal unit vector; D is the induced drag coefficient matrix; R is the local-inertial...

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Abstract

The invention provides an unmanned aerial vehicle movement speed prediction method based on machine learning. The unmanned aerial vehicle movement speed prediction method comprises the following steps: S0: controlling an unmanned aerial vehicle to complete flight, in the period, acquiring an optical flow value of the unmanned aerial vehicle by using an optical flow meter, acquiring posture information and acceleration information by using a gyroscope and an accelerometer; S1: acquiring flying speed information according to the optical flow value and the posture; S2: training acquired data by using a Libsvm tool, thereby obtaining the relationship of the flying speed information and the posture information and the acceleration information; S3: when the unmanned aerial vehicle is used practically, predicting by using the Libsvm tool according to the relationship confirmed in the step S2 and the posture information and the acceleration speed acquired at present, thereby obtaining the movement speed of the unmanned aerial vehicle.

Description

Technical field [0001] The invention relates to a technology for predicting the current movement speed of an unmanned aerial vehicle, in particular to a method for predicting the movement speed of an unmanned aerial vehicle based on machine learning. Background technique [0002] The unmanned aircraft is abbreviated as "UAV" and the English abbreviation is "UAV". It is an unmanned aircraft operated by radio remote control equipment and self-provided program control devices. Currently, the navigation of rotary-wing UAVs mainly relies on satellite navigation systems. [0003] In many environments (such as indoors, cities, canyons), the lack of satellite signals will make the UAV navigation failure. In this case, a feasible backup solution is to use the aircraft's own inertial navigation device for autonomous navigation. However, the current method of using inertial devices for navigation is to integrate the measured value of the accelerometer to obtain the speed and position inform...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01C21/20G01C21/18
CPCG01C21/18G01C21/20
Inventor 王荣志邹丹平裴凌刘佩林郁文贤徐昌庆
Owner SHANGHAI JIAO TONG UNIV
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