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

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...

AI Technical Summary

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 factors and control parameters, and the algorithm may sometimes converge prematurely and stagnate.

However, the basic ant colony algorithm has shortcomings such as long search time and easy to fall into local optimal solutions. Therefore, it must be improved when applying the ant colony algorithm to solve the problem of adaptive route planning for unmanned combat aircraft.

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