This paper considers a patrol inspection scenario where multiple unmanned aerial vehicles (UAVs) are adopted to traverse multiple predetermined cruise points for data collection. The UAVs are connected to cellular networks and they would offload the collected data to the ground base stations (GBSs) for data processing within the constrained duration. This paper proposes a balanced task assignment strategy among patrol UAVs and an energy-efficient trajectory design method. Through jointly optimizing the cruise point assignment, communication scheduling, computational allocation, and UAV trajectory, a novel solution can be obtained to balance the multiple UAVs' task completion time and minimize the total energy consumption. Firstly, we propose a novel clustering method that considers geometry topology, communication rate, and offload volume; it can determine each UAV's cruise points and balance the UAVs' patrol task. Secondly, a hybrid Time-Energy traveling salesman problem is formulated to analyze the cruise point traversal sequence, and the energy-efficient UAV trajectory can be designed by adopting the successive convex approximation (SCA) technique and block coordinate descent (BCD) scheme. The numerical results demonstrate that the proposed balanced task assignment strategy can efficiently balance the multiple UAVs' tasks. Moreover, the min-max task completion time and total energy consumption performance of the proposed solution outperform that of the current conventional approach.

翻译：本文研究一种巡检场景，其中采用多架无人机遍历多个预定巡航点进行数据采集。无人机连接至蜂窝网络，并在有限时间内将采集的数据卸载至地面基站进行处理。本文提出了一种巡检无人机间的均衡任务分配策略及一种高能效轨迹设计方法。通过联合优化巡航点分配、通信调度、计算资源分配和无人机轨迹，可获得一种新颖的解决方案，以平衡多架无人机的任务完成时间并最小化总能耗。首先，我们提出一种考虑几何拓扑、通信速率和卸载量的新型聚类方法，该方法能确定每架无人机的巡航点并均衡各无人机的巡检任务。其次，构建了混合时间-能量旅行商问题以分析巡航点遍历序列，并采用逐次凸逼近技术和块坐标下降法设计高能效的无人机轨迹。数值结果表明，所提出的均衡任务分配策略能有效平衡多架无人机的任务。此外，所提方案在最小化最大任务完成时间和总能耗方面的性能均优于现有常规方法。