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Unmanned aerial vehicle route planning method based on self-adaptive multi-modal fusion ant colony algorithm

An ant colony algorithm and trajectory planning technology, applied in the field of UAV trajectory planning, can solve the problems of long search time, slow convergence speed, easy to fall into local optimum, etc., to achieve enhanced search ability, fast convergence speed, and reduce diversity sexual effect

Active Publication Date: 2019-10-11
HEBEI GAODA TECH
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AI Technical Summary

Problems solved by technology

In addition to this, the ant colony algorithm also has disadvantages such as long search time, slow convergence speed, and easy to fall into local optimum.

Method used

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  • Unmanned aerial vehicle route planning method based on self-adaptive multi-modal fusion ant colony algorithm
  • Unmanned aerial vehicle route planning method based on self-adaptive multi-modal fusion ant colony algorithm
  • Unmanned aerial vehicle route planning method based on self-adaptive multi-modal fusion ant colony algorithm

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Embodiment Construction

[0052] In order to make the object, technical scheme and advantages of the present invention clearer, the following in conjunction with the attached Figure 1-Figure 2 and specific examples to clearly and completely describe the invention.

[0053] The present invention mainly combines self-adaptive and polymorphic ant colony algorithms, and the combined algorithm is applied to UAV track planning. , relying on scout ants and search ants, a global and local parallel search mode is formed, which improves the ability of the algorithm to find the global optimal value. It mainly solves the problem that the traditional ant colony algorithm has a small difference in pheromone concentration in the initial stage of track planning, the positive feedback effect is not obvious, the path search is blind, the convergence speed is relatively slow, and it is easy to fall into local optimum.

[0054] Such as figure 1 and figure 2 Shown a kind of unmanned aerial vehicle track planning metho...

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Abstract

The invention discloses an unmanned aerial vehicle route planning method based on self-adaptive multi-modal fusion ant colony algorithm, and belongs to the field of the unmanned aerial vehicle path planning. The multi-modal ant colony algorithm is imported in an ant colony algorithm, the self-adaptivity and the multi-modal ant colony algorithm are combined to form a global and local parallel search mode, the capacity of searching a global optimal value by the algorithm is improved, the search time is shortened, and the convergency speed is accelerated; on the basis of the traditional multi-modal ant colony algorithm, the self-adaptive parallel rule and the pseudo-random rule are imported, and a state transition rule and the self-adaptive conversion probability are proposed, the self-adaptive information updating strategy is imported, and the problem that the method is easy to trap into the local optimum in the search process can be avoided by adopting the method disclosed by the invention.

Description

technical field [0001] The invention relates to an unmanned aerial vehicle track planning method based on an adaptive polymorphic fusion ant colony algorithm, which belongs to the field of unmanned aerial vehicle track planning. Background technique [0002] Ant colony algorithm refers to the Ant Colony Algorithm (ACO) by simulating the collective search of ants for food in nature. Ant foraging is a heuristic bionic algorithm based on groups, not a single ant autonomously looking for food sources. Foraging behavior depends on communication between ants or between an individual ant and its environment, based on the use of chemicals produced by the ants, called pheromones. Here's how ants work: First, when the ants reach a decision point where they have to decide to turn left or right, the ants randomly choose the next path and deposit the pheromone on the ground because they don't know which is the best choice . After a brief selection, the difference in the amount of pher...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01C21/00G01C21/20G06N3/00
CPCG01C21/20G01C21/005G06N3/006
Inventor 甄然张春悦吴学礼
Owner HEBEI GAODA TECH
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