Intelligent unmanned operational aircraft self-adapting fairway planning method based on ant colony satisfactory decision-making

A combat aircraft and air route planning technology, applied in the direction of instruments, calculation models, biological models, etc., can solve problems such as time-consuming, stagnation, and long search time, and achieve good real-time and rapid results

Inactive Publication Date: 2008-03-05
BEIHANG UNIV
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Problems solved by technology

However, the use of genetic algorithms in route planning may be relatively time-consuming and generally not suitable for real-time planning. However, the current computing system is serial, and genetic algorithms have implicit parallelism, which makes them very useful. great development potential
The disadvantage of this algorithm is that it is difficult to select genetic

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  • Intelligent unmanned operational aircraft self-adapting fairway planning method based on ant colony satisfactory decision-making
  • Intelligent unmanned operational aircraft self-adapting fairway planning method based on ant colony satisfactory decision-making
  • Intelligent unmanned operational aircraft self-adapting fairway planning method based on ant colony satisfactory decision-making

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Embodiment Construction

[0127] The present invention is an adaptive route planning method based on satisfactory decision-making ant colony intelligent unmanned combat aircraft. To adapt to route planning, the specific implementation steps are as follows:

[0128] (1) Parameter initialization. Let time t=0 and cycle times Nc=0, α=2; β=3; ρ=0.7; Q=100; A=150; NC_max=10, put 20 ants on the starting point of the unmanned combat aircraft , let the initialization information τ of each edge (i, j) on the directed graph ij (t)=1, and the initial time Δτ k (r, s) = 0, Δτ e (r, s) = 0;

[0129] (2) Number of cycles Nc←Nc+1;

[0130] (3) search for ant serial number k=1;

[0131] (4) Search for ant serial number k←k+1;

[0132] (5) Tabu table index number tabu of ants k = 1;

[0133] (6) Tabu table node number tabu k ← j;

[0134] (7) Calculate the cost of all candidate path nodes according to formulas (7) and (8), and the ant individual calculates the probability of a candidate node according to the...

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Abstract

The method comprises: creating a air-way planning model of a unmanned combat air vehicle (UCAV); using a shortest air-way and minimum detectable air-way weighted method to compute the cost function used as the performance index of describing the air-way; after using ant swarm intelligence to find the current candidate path node for the UCAV, using the satisficing-decision making principle to evaluate the satisficing degree of each candidate path node so as to select the satisfied candidate node; meanwhile, using a 'wheel of fortune' modified policy to improve the global search capacity of the algorithm; after each ant completes the selection of its own candidate air-way, making a overall correction for the biological information elements at each side.

Description

(1) Technical field [0001] Unmanned Aerial Combat Vehicle (Unmanned Aerial Combat Vehicle) is a powered, controllable, capable of carrying a variety of mission equipment, performing a variety of combat missions and reusable unmanned tactical aircraft. Due to its zero casualty risk and high mobility and other advantages, it has attracted great attention from the military of various countries. As a key component of the UAV mission planning system, Path Planning aims to calculate and select the optimal or suboptimal flight route within an appropriate time, so as to rationally allocate the combat resources of UAVs. , to achieve the maximum combat effectiveness of unmanned combat aircraft plays a vital role. At present, the research on route planning technology at home and abroad is further developing in the direction of intelligence, real-time, and realizability, but it is basically still in the initial research stage. Ant Colony Algorithm (Ant Colony Algorithm) is a newly devel...

Claims

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Application Information

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IPC IPC(8): G06N3/00G06Q10/00
Inventor 段海滨周锐魏晨余亚翔陈宗基
Owner BEIHANG UNIV
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