Multiple-unmanned-aerial-vehicle multiple-ant-colony collaborative target searching method

Abstract

The invention discloses a multiple-unmanned-aerial-vehicle multiple-ant-colony collaborative target searching method. The multiple-unmanned-aerial-vehicle multiple-ant-colony collaborative target searching method comprises the following steps that S1, a grid method is adopted to divide and mark a searching sea area, and a target probability graph model is established; S2, a target function is established, and the unmanned aerial vehicle steering price, the unmanned aerial vehicle collision threat price and the searching probability are subjected to weighted summation; and S3, a multiple-ant-colony algorithm is adopted to conduct collaborative path optimizing design on multiple unmanned aerial vehicles, and by setting the maximum number N<max> of iteration times, the S32 and S33 are executed until the maximum number of iteration times is met and the optimal searching path is output. According to the multiple-unmanned-aerial-vehicle multiple-ant-colony collaborative target searching method, the probability graph characteristics of a target in the sea area are fully utilized to design the new ant colony pheromone comprising local initialization, global initialization and updating rules, thus through the ant algorithm, trajectory planning of the unmanned aerial vehicles can be quickly completed, the problem of repeated searching is avoided, the searching paths of the unmanned aerial vehicle cross, and the searching efficiency is improved.

Description

technical field [0001] The invention relates to the technical field of unmanned aerial vehicle search targets, in particular to a multi-unmanned aerial vehicle multi-ant colony cooperative search target method. Background technique [0002] In recent years, thanks to the rapid development of technologies such as sensors, microprocessors, and information processing, the functions of unmanned swarm systems have increased rapidly, and their application scope has also continued to expand. Because of its flexibility, scalability, and strong collaborative operation capabilities, UAV swarms have attracted more and more attention from academia, industry, and defense fields in their collaborative theory and application research. The multi-UAV cooperative search system can effectively improve the search efficiency, especially in complex sea conditions such as uncertainty and strong interference in the search area. one of the important directions. [0003] At present, the ant colony ...

AI Technical Summary

Problems solved by technology

First of all, the search time of a single UAV is long and the search efficiency is low; secondly, the flight safety of UAVs needs to be considered when multi-UAVs perform search tasks, but the current research aims to find the target with the greatest probability, and does not consider the search The cost of navigation in the process, drones will turn frequently, and the paths between drones will overlap.

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