Energy-Constrained Safe Path Planning for UAV-Assisted Data Collection of Mobile IoT Devices

Unmanned aerial vehicles (UAVs) are being broadly employed to assist in efficient data collection for Internet of Things (IoT) networks. Studies have been conducted to ensure the effectiveness and safety of UAVs in the data collection process. However, they only considered part of the challenges of energy consumption, collision avoidance, and mobility of IoT devices. In this article, we study a UAV path planning optimization problem for UAV-assisted data collection to maximize the amount of collected data. Different from these existing works, this optimization problem not only considers all these challenges, but also considers the kinematic and communication constraints. Moreover, in this problem, the duration required for the UAV to complete the mission is unknown, makes it more challenging to solve this problem through traditional optimization methods. We thus formulate the problem as a partially observable Markov decision process (POMDP) with a continuous action space and propose a proximal policy optimization-based algorithm to address it. Experiment results demonstrate that our algorithm has significant advantages over other baseline algorithms in terms of success rate, data collection rate, and collision rate.