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Unmanned aerial vehicle path planning method based on artificial potential field and ant colony algorithm

A technology of artificial potential field method and ant colony algorithm, which is applied in the field of UAV path planning based on artificial potential field and ant colony algorithm, can solve the problem that the artificial potential field method is difficult to calculate repulsion, and achieve increased robustness, expansion The effect of finding space

Pending Publication Date: 2020-10-16
SHANGHAI MARITIME UNIVERSITY
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Problems solved by technology

[0004] In order to solve the above technical problems, avoid falling into local minimum points and effectively avoid large obstacles, and solve the problem that the artificial potential field method is difficult to calculate the repulsion force, an improved algorithm combining the artificial potential field method and the ant colony algorithm is proposed. , the global path planning is guided by the artificial potential field method, and the local path planning is guided by the ant colony algorithm

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  • Unmanned aerial vehicle path planning method based on artificial potential field and ant colony algorithm
  • Unmanned aerial vehicle path planning method based on artificial potential field and ant colony algorithm
  • Unmanned aerial vehicle path planning method based on artificial potential field and ant colony algorithm

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[0035] The present invention will be further described below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solutions of the present invention more clearly, and cannot be used to limit the protection scope of the present invention.

[0036] Step 1: Define the operating space of the UAV as a potential energy field, and use the artificial potential field method to obtain the initial path;

[0037] Step 1.1: Set the coordinates of the UAV as X(x,y) and the coordinates of the target point X t (x t ,y t ), the obstacle coordinate is X o (x o ,y o ), assuming that the drone is only affected by gravity, and the gravitational coefficient is k, then the gravitational field is:

[0038]

[0039] where R is the dangerous distance between the drone and the obstacle;

[0040] Step 1.2: When the distance between the drone and the obstacle is greater than R, the drone moves to the target position under the gravitation...

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Abstract

The invention provides an unmanned aerial vehicle path planning method based on an artificial potential field and an ant colony algorithm. The method comprises the following steps: S1, defining the operation space of an unmanned aerial vehicle as a potential energy field, and obtaining an initial path through an artificial potential field method; S2, when a large obstacle is detected, rasterizingthe map, and determining an obstacle avoidance starting node and an obstacle avoidance ending node; S3, planning an obstacle avoidance route through an ant colony algorithm; and S4, after the unmannedaerial vehicle completes obstacle avoidance, continuing to adopt the artificial potential field method to perform path planning, and repeating the steps until the unmanned aerial vehicle reaches thetarget point.

Description

technical field [0001] The invention relates to the technical field of UAV path planning, in particular to a UAV path planning method based on artificial potential field and ant colony algorithm. Background technique [0002] UAVs have significant advantages such as strong mobility, light weight, small size, low cost, and high space utilization. They are increasingly used in various fields, especially in sudden disasters in mountainous areas, such as earthquakes, mudslides, etc. In a disaster that organizes the arrival of people in a short period of time, drones can respond quickly and undertake the task of transporting emergency supplies such as medical rescue packages, which is of great significance to saving people's lives and property. [0003] At present, the widely used planning methods include A* algorithm, artificial potential field method, neural network algorithm, fuzzy algorithm, grid method, ant colony algorithm, dynamic window algorithm and so on. Most of these...

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Application Information

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IPC IPC(8): G06Q10/04G06N3/00
CPCG06Q10/047G06N3/006
Inventor 唐刚冀香震邵长专
Owner SHANGHAI MARITIME UNIVERSITY
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