An Intelligent Multitarget AAV Coverage-Aware Navigation Method for Low-Altitude IoT

Cellular-connected autonomous aerial vehicles (AAVs) act as aerial users to accomplish navigation missions while maintaining a cellular connection, which are of great importance for developing low-altitude Internet of Things (IoT) systems. However, the full aerial cellular coverage is an extreme challenge. To tackle this issue, existing coverage-aware navigation studies focus on designing AAV flight trajectories to avoid weak coverage areas while completing the mission, where considerable efforts of deep-reinforcement-learning-(DRL)-based methods have been conducted. However, they lack adaptability to various targets, which limits their practical applicability in real-world scenarios. In this article, we propose a novel DRL-based multitarget coverage-aware navigation (MTCN) framework, where the AAV learns a navigation policy to minimize the weighted sum of navigation time and the expected communication outage duration. MTCN allows the AAV to efficiently navigate to arbitrary targets without the need of the separate policy training for each target. Furthermore, to improve the sample efficiency of the MTCN, we introduce the radio map-enhanced MTCN (RM-MTCN) framework, which leverages a virtual radio map to generate supplementary navigation training data. RM-MTCN can significantly reduce the need for extensive real-world interactions. Extensive experimental results demonstrate that both MTCN and RM-MTCN achieve generalized navigation behaviors across different targets while avoiding weak communication coverage areas. It is also shown that RM-MTCN exhibits faster learning speed and better performance compared to MTCN, which indicates the effectiveness of leveraging the virtual radio map during the policy training.