Autonomous environmental perception, path planning and dynamic landing method and system of unmanned aerial vehicle

Patent metadata

Field	Value
Publication number	US-12416931-B2
Application number	US-202519017245-A
Country	US
Kind code	B2
Filing date	Jan 10, 2025
Priority date	Aug 22, 2024
Publication date	Sep 16, 2025
Grant date	Sep 16, 2025

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.

First claim

What is claimed is:

1. An autonomous environmental perception, path planning and dynamic landing method of an unmanned aerial vehicle, comprising the following steps:
S 1 . obtaining depth information and two-dimensional plane information of an environment in real time by the unmanned aerial vehicle, and generating three-dimensional environmental information in space through a three-dimensional reconstruction algorithm based on the depth information and the two-dimensional plane information;
S 2 . determining a global starting point and a global end point, and generating an initial path according to the global starting point and the global end point;
S 3 . optimizing the initial path in real time based on a local path optimization algorithm to obtain a first optimized path, and judging in real time whether a perception threshold of the current position of the unmanned aerial vehicle is greater than a preset threshold; when the perception threshold is greater than the preset threshold, proceeding into S 4 ; otherwise, continuing to perform S 3 , until the global end point is reached, and proceeding into S 7 ;
wherein a calculation formula of the perception threshold is as follows:

τ
per

=

Γ
optimized

Γ
init

;

wherein τ per represents the perception threshold, Γ init represents the initial path within the field of view of the unmanned aerial vehicle, Γ optimized represents the first optimized path within the field of view of the unmanned aerial vehicle, and t represents the preset threshold, when τ per >τ, proceeding into S 4 ;
S 4 . perceiving the frontier of the field of view of the unmanned aerial vehicle, storing the perceived frontier in the form of space points and recording as frontier points, and performing distance optimization on the frontier points to obtain a frontier space point set P;
S 5 . according to the frontier space point set P, optimizing the initial path in real time based on a frontier-perceived path optimization method to obtain a second optimized path and a local end point;
S 6 . switching to optimizing the initial path in real time based on the local path optimization algorithm when the unmanned aerial vehicle advances along the second optimized path to the local end point; and
S 7 . carrying out dynamic landing based on a deep reinforcement learning algorithm after the unmanned aerial vehicle arrives at the global end point;
wherein in S 4 , performing distance optimization on the frontier points to obtain a frontier space point set P comprises:
constructing a cube area with a side length of 2 m with the frontier point as a central point, dividing the cube area into 8 sub-cube areas with a side length of m, and taking vertices of the sub-cube areas as collision judgment points to judge whether the distance between each collision judgment point and a target obstacle is less than a preset collision distance; and if the distance between at least one collision judgment point and the target obstacle is less than the present collision distance, deleting the frontier point;
traversing all frontier points by repeating the above steps, and constructing the frontier space point set P according to the frontier points finally retained;
wherein in S 5 , according to the frontier space point set P, optimizing the initial path in real time based on a frontier-perceived path optimization method to obtain a second optimized path and a local end point comprises:
S 51 . determining the local end point;
according to the view distance of the unmanned aerial vehicle, selecting the point on the initial path that is farthest from the current position of the unmanned aerial vehicle as the initial local end point; and if the initial local end point is within the obstacle range, selecting the second local end point by doubling the view distance of the unmanned aerial vehicle based on the initial local end point until the selected local end point is outside the obstacle range;
S 52 . determining decision indicators, and calculating a decision function according to the decision indicators; wherein the decision indicators comprise: adjacent point constraint, distance constraint, and direction constraint;

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Assignees

• Univ Jiliang China

Inventors

• Jiang Wensong
• Luo Zai
• Shi Linzhen
• Zhu Jie
• Yang Li
• Li Minyue
• Li Yaru
• Bian Dian
• He Yaxiong

Classifications

• G05D1/654

  Landing (docking at a base station G05D1/661) · CPC title

• G05D2101/15

  using machine learning, e.g. neural networks · CPC title

• G05D1/622 Primary

  Obstacle avoidance (predicting or avoiding probable or impending collision of road vehicles B60W30/08) · CPC title

• G05D2109/20

  Aircraft, e.g. drones · CPC title

• G05D2107/30

  Off-road · CPC title