Multi-UAV cooperation target searching method based on multi-group ant colony algorithm

Abstract

The invention discloses a multi-UAV cooperation target searching method based on a multi-group ant colony algorithm, and the method comprises the steps: carrying out the modeling of a search environment of an UAV; and carrying out the multi-UAV target searching according to the modeling environment through employing the multi-group ant colony algorithm. One ant colony plays a role in mutual guide,and different ant colonies achieve the mutual repulsion, so the method can improve the diversity of solutions and the cooperation performance of multiple UAVs, and also can select the direction wherea target is probably located as much as possible, thereby avoiding the greedy selection and improving the selection diversity. The method also can achieve the dynamic adjustment of the search benefitafter the discovery of a target, avoids the repeated search of the target because of the impact from a target expectation factor, selects an optimal path of each generation for pheromone updating, and promotes the algorithm to approach to a better path. The method achieves the path cooperation of the UAV when the UAVs perform the searching, avoids the phenomena of the excessive paths and repeatedsearching of the target caused by the impact from the pheromone and target inspiring factors, reduces the search cost, and improves the search efficiency.

Description

technical field [0001] The invention relates to a multi-UAV cooperative target search method, in particular to a multi-UAV cooperative target search method based on a multi-group ant colony algorithm. Background technique [0002] UAV-related technologies play an important role in civilian and scientific research fields, and are increasingly used in aerial video shooting, agricultural and forestry drug spraying, regional map surveying, alpine geological survey, high-voltage line inspection, mountain express delivery, post-disaster emergency rescue, etc. more extensive. In addition, under the influence of artificial intelligence, electronic communication technology, the Internet, image recognition, sensors, and the Internet of Things, UAVs are not limited to single-plane flight, but between multiple UAVs, multi-UAVs and smart cars. An unmanned control system that works collaboratively is established. In the entire collaborative system, the drones, the drones and the smart ca...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a multi-unmanned aerial vehicle collaborative target search method based on the multi-group ant colony algorithm, to solve the multi-unmanned aerial vehicles due to the influence of pheromone in the ant colony algorithm and the influence of factors such as target expectations. When searching for targets, paths overlap, targets are searched repeatedly, drones collide, etc.

[0005] The present invention is aimed at the shortcoming that the ant colony algorithm lacks the negative feedback mechanism in the existing research and cannot better realize multi-UAV cooperation, and the Voronoi diagram is also more suitable for the characteristics of single-machine track planning, and the designed ants of the same group have mutual The role of guidance, different groups of ants are mutually exclusive, the precise combination of ants can increase the diversity of solutions, and the integration of algorithms such as dynamic adjustment of target income and time-space coordination can further increase the synergy between multiple drones In order to solve the problems caused by the influence of pheromone in the ant colony algorithm and the influence of target expectation factors, the paths of multiple drones overlap when searching for targets, the target is repeatedly searched, and the problems of drone collisions are constantly improved. Algorithms that enable it to collaboratively plan the routes of multi-UAVs in target search in a fixed area

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