Task allocation method for formation of unmanned aerial vehicles in certain environment

Abstract

The invention discloses a task allocation method for formation of unmanned aerial vehicles in a certain environment, belonging to the technical field of unmanned aerial vehicles. The task allocation method comprises the following steps of determining a coding sequence of a task allocation algorithm; determining a preponderant function of the unmanned aerial vehicles formed to execute a task; determining a speed update formula and a position update formula of a discrete particle swarm optimization; determining the flow of a tabu search; and determining the flow of hybrid optimization. According to the task allocation method for the formation of the unmanned aerial vehicles in the certain environment, the continuous particle swarm optimization is discretized, the algorithm is simply and conveniently operated on the premise that optimizing property can be guaranteed, and the effectiveness of the discrete particle swarm method is indicated through simulation. According to the task allocation method for the formation of the unmanned aerial vehicles in the certain environment, a supplement strategy of the tabu search algorithm is provided, and the local optimizing capacity of the algorithm is enhanced when the inertia weight [omega] of the particle swarm optimization is larger, i.e. the particle swarm embodies stronger variety, so that the original two algorithms realize complementing each other's advantages, the searching performance can be improved, and the judgment can be verified in multiple groups of simulated tests.

Description

Technical field [0001] The invention belongs to the technical field of unmanned aerial vehicles, and relates to flight formation task allocation technology. Specifically, it refers to an unmanned aerial vehicle formation task allocation method under a certain environment. Background technique [0002] At present, as many as 30 countries have invested a lot of human and financial resources in the research and production of drones. After two decades of development, this technology has become relatively mature and has played a role in various military and civilian fields. Nevertheless, there are some problems when a single UAV performs its mission. For example, a single UAV may be affected by the number of sensors. Restrictions, it is impossible to observe the target area from multiple angles and all directions. When facing a large-area search task, it cannot effectively cover the entire search area; if it is a rescue mission, a single UAV is restricted in terms of load, which is of...

AI Technical Summary

Problems solved by technology

[0009] The purpose of the present invention is to solve the problems existing in the prior art, to provide a task assignment strategy for unmanned aerial vehicle formations in a certain environment, and to design a strategy based on modern The hybrid optimization strategy of the optimization algorithm accelerates the convergence speed and local search ability of the modern optimization algorithm. The present invention designs the particle representation mode and the speed position update formula of the discrete particle swarm optimization algorithm, combines the discrete particle swarm optimization algorithm with the tabu search algorithm, and uses discrete The particle swarm optimization algorithm is used for rough search, and the tabu search algorithm is used for fine search. The advantages of discrete particle swarm optimization algorithm and tabu search algorithm are combined to complete the task assignment of unmanned aerial vehicles in a certain environment.

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