A method of reconfiguring UAV swarm formation using genetic algorithm and dubins algorithm

Abstract

The present invention designs a method of utilizing genetic algorithm and Dubins algorithm to realize the formation reconstruction of UAV groups, the method specifically includes numbering UAVs, and establishing the position matching relationship of each UAV in the new formation, thereby completing Chromosome coding; improve the Dubins algorithm, establish a route planning model, and evaluate the distance of a wingman to complete the reconstruction flight; use the genetic algorithm to assign the reconstruction target position to each UAV. Compared with the existing formation reconstruction algorithm, the present invention can obtain a more stable route by decomposing the formation and reconstructing it into task allocation and route planning, and by taking the speed range and turning radius of the UAV into consideration, the generated route It is more reasonable and easier to apply to practical applications. The present invention also ensures that each UAV can obtain an assigned position by limiting variation and crossover, prevents omissions and misclassifications, and further improves the quality of task assignments.

Description

technical field [0001] The invention relates to route planning and target allocation of a UAV swarm during autonomous flight, and specifically designs a route construction method for UAV swarm formation reconstruction. Background technique [0002] With the development of UAV technology, the role of UAV in combat is becoming more and more prominent. The overall performance of multiple UAVs forming a formation to perform tasks such as cooperative detection, reconnaissance, and combat has been greatly improved compared with a single UAV. Multi-unmanned aerial vehicles (UAVs) formation flying to perform cooperative reconnaissance and combat missions can improve the success rate of a single combat mission to a certain extent, thus causing a research boom in various countries on multi-aircraft formation flying. During the formation flight of the UAV group, facing different mission requirements and threat types, and in order to obtain a better advantage against the enemy in air c...

AI Technical Summary

Problems solved by technology

However, these algorithms often ignore the performance constraints of UAVs in the process of use, such as the turning radius limit and speed limit of UAVs, etc., resulting in the calculated path may not be really used in practice

At the same time, when considering the optimization parameters, these algorithms usually take the shortest time or the shortest path as the optimization goal, but they do not take into account the speed and direction of the UAV when it reaches the target point.

As a result, even if the drone can form a preset formation at the moment of reaching the target point, the formation will collapse due to the difference in speed

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