US 2026/0099150 A1Published application

Drone, drone training method, and drone control method

AssigneeWistron Corporation

InventorChih Hung WU

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Abstract

A drone training method includes: inputting a plurality of wind speed components into a corresponding plurality of fuzzy functions, to generate a plurality of wind speed membership values respectively; selecting one from the plurality of wind speed membership values corresponding to each of the wind speed components, and generating a rule value based on the wind speed membership values corresponding to each of the wind speed components; inputting the plurality of wind speed components into one of inference functions, where each rule value corresponds to one of the inference functions as a weight respectively, and calculating a function sum of a plurality of inference functions corresponding to each of the wind speed components; generating a regression model after calculating an error function base on each offset component and the function sum corresponding to each of the wind speed components and optimizing the error function.

Claims20

Dependent claims

A drone training method, applicable to a drone, wherein the drone comprises a wind speed sensor and a position sensor, and the drone training method comprises:

receiving a plurality of wind speed components generated by the wind speed sensor and a plurality of offset components generated by the position sensor, wherein the wind speed components correspond to the offset components one by one;
inputting each of the wind speed components into a corresponding plurality of fuzzy functions, to generate a plurality of wind speed membership values respectively;
selecting one from the plurality of wind speed membership values corresponding to each of the wind speed components, and generating a rule value based on the wind speed membership values corresponding to each of the wind speed components;
inputting the plurality of wind speed components into inference functions, wherein each rule value corresponds to one of the inference functions as a weight respectively, and calculating a function sum of a plurality of inference functions corresponding to each of the wind speed components;
and
generating a regression model after calculating an error function base on each offset component and the function sum corresponding to each of the wind speed components and optimizing the error function.

3 more elements

Dependencies are inferred from the claim text.

Facts

Application: 19/010,574Filed 6 Jan 2025
Priority: TW 1131383398 Oct 2024
Published: 9 Apr 2026
Term ends: Not estimatedThe source records don’t support an estimate.

Classification7 codes

G05D 1/606
Intended control result: compensating for or utilising external environmental conditions, e.g. wind or water currents
G05D 1/24
Control system inputs: arrangements for determining position or orientation
G05D 2101/15
Using artificial intelligence [AI] techniques: using machine learning, e.g. neural networks
G05D 2109/20
Types of controlled vehicles: aircraft, e.g. drones

Family

No related US applications are recorded in the indexed filing history.

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