Unmanned aerial vehicle group target search method based on chaos lost pigeon flock optimization mechanism

Abstract

The invention discloses an unmanned aerial vehicle group target search method based on a chaos lost pigeon flock optimization mechanism. The method comprises the following steps of (1) environment map initialization: realizing search environment initialization by using rasterization, (2) performing cooperative path optimization on the unmanned aerial vehicle by adopting a chaos lost pigeon flock optimization mechanism, updating track point coordinates of the unmanned aerial vehicle at the next moment for a global optimal position, and guiding the unmanned aerial vehicle to fly to the most efficient search area, (3) broadcasting state information of the unmanned aerial vehicles: realizing information sharing among multiple unmanned aerial vehicles by adopting a communication mechanism to update the motion state of the unmanned aerial vehicles, and (4) target distribution: selecting the unmanned aerial vehicle with the highest matching degree to search the target to obtain an optimal target search scheme. The pigeon flock algorithm based on the chaos lost mechanism has obvious advantages in solving quality, and a chaos initialization strategy of the pigeon flock algorithm enables the algorithm to have high convergence speed and better convergence precision; the lost mechanism enables the algorithm to have a strong capability of jumping out of local optimum.

Description

technical field [0001] The invention relates to a method for collaborative search of multiple dynamic targets by multiple unmanned aerial vehicles based on a chaotic lost pigeon group optimization mechanism, which belongs to the technical field of unmanned aerial vehicle coordination Background technique [0002] In recent years, with the development of drone-related technologies, the application of drones has penetrated into all aspects of society. Due to its strong adaptability to the physical environment, low risk, low cost, and no casualties, it is an indispensable tool for critical and time-sensitive mission search and rescue. However, as the task environment becomes more and more complex, with the characteristics of all-round and large-scale, it is becoming more and more difficult for a single UAV to search the target area, and it is often impossible to complete all air search tasks in a short period of time. Therefore, studying the cooperation mechanism between multi...

AI Technical Summary

Problems solved by technology

Although the superiority of the pigeon swarm optimization algorithm is superior to other intelligent optimization algorithms, such as the particle swarm optimization algorithm and the differential evolution algorithm, it still has a common premature convergence problem.

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