面向复杂环境的集群无人机任务调度方法研究综述

A review of task scheduling methods for UAV swarm in complex environments

摘要:

近年来，无人机由于其成本低、速度快和灵活性强等优点，在军事和民用领域得到了广泛应用。集群无人机是由一组同构或异构无人机组成，通过个体自主决策和信息交互，实现感知互动、信息传递和协同工作。相较于单一无人机，集群无人机可利用其集群优势、自主优势及智能化优势完成复杂任务。然而，随着任务环境、需求和集群规模的不断变化，集群无人机任务调度问题成为备受关注的热点问题。总结近几年的代表性研究，列举了复杂环境下集群无人机任务调度面临的挑战：动态任务需求、复杂环境条件、不确定通信条件及资源受限。随后，按照调度算法的作用机理划分了当前主流的调度方法，即优化算法、演化算法、强化学习算法以及群体智能算法，并对上述方法的原理、研究现状进行了归纳总结。最后，对集群无人机任务调度的未来研究方向进行了展望。

Abstract:

In recent years, unmanned aerial vehicles (UAVs) have been widely used in military and civilian fields due to their advantages such as low cost, high speed, and flexibility. UAV swarm is composed of a group of homogeneous or heterogeneous UAVs, which achieve perception interaction, information transmission, and collaborative work through individual autonomous decision-making and information exchange. Compared to a single UAV, UAV swarm can utilize the collective capabilities, autonomous advantages and intelligent superiority to tackle complex tasks. However, with the continuous changes in task environment, requirements and cluster size, the issue of task scheduling for UAV swarm has become a hot topic of great concern. To this end, representative studies from recent years have been summarized, listing the challenges faced by UAV swarm task scheduling in complex environments, including dynamic task demands, complex environmental conditions, uncertain communication conditions and resource constraints. Subsequently, according to the working mechanism of scheduling algorithms, the current mainstream scheduling methods were divi-ded into optimization algorithms, evolutionary algorithms, reinforcement learning algorithms, and swarm intelligence algorithms. Moreover, the principles and current research status of these methods were summarized and concluded. Finally, the future research directions of task scheduling for UAV swarm in the field of UAV swarm task scheduling.