Abstract

Small Unmanned Air Vehicles (UAVs) have many advantages, including low cost, and flexible deployment. And they play an important role to collect the sensing data of IoT (Internet of Things). However, limited by the load capability, it is a big challenge for them to perform long-term, large range, or far distance tasks. In order to tackle these challenges, we propose to use assembly UAVs, in which we can jointly optimize the resource management, especially the energy resource. The system model, energy cyclic cooperation, and one of the typical applications based on assembly UAVs are introduced. The energy cooperation problems are formulated and an in-air replenishing strategy (IA-RS) is proposed. Simulations show that the performances of the proposed IA-RS outperform those of the traditional on-ground replenishing strategy (OG-RS). The working time of task UAV (UAV-T) could reduce 8.3%-19.5%, and the freshness of the collected data and the collecting efficiency of the UAV-T can be improved. We also optimize the path of the replenishing UAVs (UAV-Rs). Simulations show that the proposed reinforcement learning (RL) algorithm has the best performances and acceptable complexity. Consequently, the efficiency of the IoT data collection task is improved by the proposed assembly UAVs.

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