Multi-heterogeneous unmanned aerial vehicle task allocation method based on improved genetic algorithm

Abstract

The invention discloses a multi-heterogeneous unmanned aerial vehicle task allocation method based on an improved genetic algorithm, and belongs to the technical field of unmanned aerial vehicles. Themethod is characterized by establishing a task allocation optimization model by comprehensively considering multiple constraints such as resource consumption, task completion effect and load balancing, resource limitation and task priority; and encoding each feasible task allocation scheme into a complete chromosome by adopting a matrix encoding mode. Aiming at the problems that an existing genetic algorithm is insufficient in solving precision and too slow in solving speed, the concept of fuzzy elitibility is provided, all genetic operations are improved on the basis, the built optimizationmodel is solved through the improved genetic algorithm, and an optimal task allocation scheme is obtained within limited iteration times. The method has good universality in the field of multi-agent cooperative control, has the advantages of high solving speed and high solving precision, and can effectively solve the task allocation problem of a multi-heterogeneous unmanned aerial vehicle system with multiple constraints.

Description

technical field [0001] The invention belongs to the technical field of unmanned aerial vehicles, and in particular relates to a multi-heterogeneous unmanned aerial vehicle task assignment method based on an improved genetic algorithm. Background technique [0002] With its unique low cost and strong maneuverability, UAVs have made outstanding contributions in military, agricultural and commercial applications. However, due to the limited airborne resources, it is difficult for a single UAV to meet the increasingly complex mission requirements in most cases. Compared with a single UAV, a heterogeneous UAV system composed of UAVs with complementary functions has the advantages of high speed and high flexibility, and can ensure high efficiency and high reliability to complete tasks. In large-scale mission scenarios, UAV systems need to perform multiple tasks (such as reconnaissance missions, attack missions, and verification missions), and the optimization effect of task alloc...

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Problems solved by technology

However, since resource coordination among multiple drones is involved, the load balancing problem becomes intractable

In addition, in order to reduce unnecessary resource consumption on the premise of ensuring the effective execution of the task, it is necessary to add the evaluation of the task completion effect in the optimization model. Although the introduction of multiple constraints is conducive to improving the effectiveness of the solution, it also makes the task assignment Optimization problems become more difficult to solve. With the increase of task scale and task complexity, the convergence speed of the existing genetic algorithm becomes slower and the solution accuracy becomes lower, which will lead to low efficiency of task allocation. Therefore, it is necessary to design a method suitable for The multi-heterogeneous UAV task allocation method with complex task allocation background and high precision and fast solution ability is particularly critical

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