The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Sep. 16, 2025

Filed:

Jan. 10, 2025
Applicant:

China Jiliang University, Hangzhou, CN;

Inventors:

Wensong Jiang, Hangzhou, CN;

Zai Luo, Hangzhou, CN;

Linzhen Shi, Hangzhou, CN;

Jie Zhu, Hangzhou, CN;

Li Yang, Hangzhou, CN;

Minyue Li, Hangzhou, CN;

Yaru Li, Hangzhou, CN;

Dian Bian, Hangzhou, CN;

Yaxiong He, Hangzhou, CN;

Assignee:

China Jiliang University, Hangzhou, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G05D 1/644 (2024.01); G05D 1/622 (2024.01); G05D 1/654 (2024.01); G05D 101/15 (2024.01); G05D 109/20 (2024.01);
U.S. Cl.
CPC ...
G05D 1/644 (2024.01); G05D 1/622 (2024.01); G05D 1/654 (2024.01); G05D 2101/15 (2024.01); G05D 2109/20 (2024.01);
Abstract

An autonomous environmental perception, path planning and dynamic landing method includes: obtaining three-dimensional environment information in real time; determining a global starting point and a global end point, and generating an initial path; optimizing the initial path based on a local path optimization algorithm to obtain a first optimized path; when a perception threshold of the current position of the unmanned aerial vehicle is greater than a preset threshold, optimizing the initial path based on a frontier-perceived path optimization method to obtain a second optimized path and a local end point; when the unmanned aerial vehicle advances to the local end point, switching to optimizing the initial path in real time based on the local path optimization algorithm; and when the unmanned aerial vehicle arrives at the global end point, carrying out dynamic landing based on a deep reinforcement learning algorithm.


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