Multi-unmanned aerial vehicle track planning method based on culture ant colony search mechanism

Abstract

The invention provides a multi-unmanned aerial vehicle (UAV) track planning method based on a culture ant colony search mechanism, which includes the following steps: (1) carrying out mesh generationon a standard space according to a grid method; (2) building a multi-UAV track planning model, including the number of UAVs, the start and end points and a threat model; (3) initializing the start point and the end point; (4) initializing an ant colony algorithm, including: initializing an ant colony and calculating a heuristic factor and a guide factor; and (5) assigning all ants to an initial node, and updating taboo knowledge; selecting next node for transfer according to the taboo knowledge and the state transfer probability until there is no optional node or a destination node is selected, updating historical knowledge, and updating pheromones according to the historical knowledge; and outputting a shortest path if the maximum number of iterations is achieved, and continuing the process until U multi-UAV optimal multi-path tracks are obtained. The problem that it is difficult to find the optimal flight tracks of unmanned aerial vehicles due to slow search and heavy computing burden is solved, and multi-UAV track planning is realized.

Description

technical field [0001] The present invention relates to a UAV track planning method, in particular to a multi-UAV track planning method. Background technique [0002] Unmanned aerial vehicle (UAV) refers to an unmanned aerial platform that does not carry any operator, uses aerodynamics to fly, can be autonomously or remotely piloted, can be expanded and recovered, and can carry payloads. With the development of science and technology and the progress of control technology. [0003] UAV trajectory planning is the key technology for UAVs to realize autonomous flight and autonomous operation. It means that UAVs design the optimal trajectory from the starting point to the target point according to the needs of the flight mission, and the requirements meet the maneuverability of UAVs. constraints and minimize the overall cost. The trajectory planning of the UAV is the flight plan for the successful completion of the mission, and it is one of the key technologies of the mission ...

AI Technical Summary

Problems solved by technology

The Voronoi diagram method first needs to express the flight environment as an optional path set composed of a series of trajectories according to certain rules, and then search the network graph for trajectories according to specific evaluation rules. Constraint conditions, can not search in the space outside the optional path set, and the accuracy of the particle swarm optimization algorithm is not high enough to find the optimal track under the condition of meeting a certain threat cost

Liu Changan et al. published "UAV Route Planning Based on Ant Colony Algorithm" in "Journal of Air Force Engineering University" (2004, Vol.5, No.2, pp.9–12) using ant colony algorithm for UAV Trajectory planning basically enables UAVs to reach the destination point with the minimum threat cost and the optimal trajectory, but the model is simple, only for single UAV trajectory planning

[0005] To sum up, in the existing research on UAV trajectory planning, the traditional heuristic algorithm has a slow search speed and a large amount of calculation, and it is difficult to find the optimal UAV trajectory.

Moreover, most of the existing UAV trajectory planning based on intelligent algorithms is a single UAV trajectory planning.

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