某型无人机群的监视覆盖任务航路规划

Route Planning of Surveillance Coverage Mission for a Certain Type of UAV Group

摘要: 利用无人机群执行监视任务，在边界和区域管控、反恐防爆监视以及军事应用中具有很高的效费比。无人机群监视覆盖航路规划算法是提升无人机群监视任务效率和能力的核心算法。传统覆盖航路规划算法结果样式单一、对抗性环境下灵活性差，区域划分方法不便于计算机自动生成。本文提出了基于人工势场和遗传算法的监视覆盖航路规划算法，生成样式多样、监视任务执行中对抗性好的监视覆盖航路。在人工势场法的基础上，将激发势场的种子编码为二元组串形式的基因，通过交叉、变异、合并等算子的操作增加种子样式的多样性，从而规划出转弯少、监视时间间隔短、对抗性好的监视覆盖航路。最后通过算例对算法进行了验证，结果表明算法有效地满足了监视任务覆盖航路规划的需求。

Abstract: Using drones to carry out surveillance tasks has a high efficiency cost ratio in border and regional control, anti-terrorism and explosion-proof surveillance and military applications. The route planning algorithm of UAV group surveillance coverage is the core algorithm to improve the efficiency and capability of UAV group surveillance task. The traditional coverage route planning algorithm results in a single style, poor flexibility in the adversarial environment, and the area division method is not convenient for automatic computer generation. In this paper, a surveillance coverage route planning algorithm based on artificial potential field and genetic algorithm is proposed to generate a variety of surveillance coverage routes with good adversary in the execution of surveillance tasks. On the basis of the artificial potential field method, the seed of excitation potential field is encoded as a gene in the form of binary string, and the diversity of seed pattern is increased through the operation of crossover, mutation, merge and other operators, so as to plan a surveillance coverage route with fewer turns, short surveillance interval and good antagonism. Finally, an example is given to verify the algorithm, and the results show that the algorithm can effectively meet the needs of surveillance mission coverage route planning.