The cooperative control in complex multitasks using unmanned aerial vehicle and unmanned ground vehicle (UAV/UGV) is an important and challenging issue in the multirobot cooperative field. The main goal of the task studied in this paper is to minimize the time and energy consumed by the system to complete the assigned tasks. In this paper, a complex multitask problem using the hybrid UAV/UGV system is studied, which is divided into three stages, namely, the stage of finding the optimal locations of the relay stations for the UGV; the stage of solving the path planning problem for the UGV; and the stage of the task assignment for multi-UAVs. Furthermore, an improved integrated method is proposed to deal with the cooperative control problems in these three stages. Firstly, an adaptive clustering method is proposed to determine the locations of the UGV relay stations, and then an improved cuckoo search algorithm is used to find the shortest path for the UGV. Finally, a grouping method is presented to solve the multitask allocation problem of UAVs, based on an improved dynamic programming algorithm. In addition, some simulations are carried out and the results show that the proposed method has better performance when it comes to the time and energy consumption and can effectively guide the hybrid UAV/UGV system to carry out the complex multitasks.

中文翻译：

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一种多任务混合无人机系统的改进协同控制方法

背景技术使用无人机和地面无人机（UAV / UGV）的复杂多任务协作控制是多机器人协作领域中一个重要且具有挑战性的问题。本文研究的任务的主要目标是最大程度地减少系统完成分配的任务所需的时间和能量。本文研究了一种采用UAV / UGV混合系统的复杂多任务问题，分为三个阶段，即为UGV寻找中继站的最佳位置的阶段。UGV解决路径规划问题的阶段；以及多UAV的任务分配阶段。此外，提出了一种改进的集成方法来解决这三个阶段的协调控制问题。首先，提出了一种自适应聚类方法来确定UGV中继站的位置，然后使用一种改进的布谷鸟搜索算法来找到UGV的最短路径。最后，基于改进的动态规划算法，提出了一种分组方法来解决无人机的多任务分配问题。另外，进行了仿真实验，结果表明该方法在时间和能耗上具有较好的性能，可以有效地指导无人机/ UGV混合动力系统完成复杂的多任务任务。