Reinforcement-Learning-Aided Safe Planning for Aerial Robots to Collect Data in Dynamic Environments

We study the data collection problem in an Internet of Things (IoT) network where an unmanned aerial vehicle (UAV) is utilized to aggregate data from a set of IoT devices. We formulate the scheduling and path planning problems for the UAV. The goal of the scheduling problem is to find the sequence of nodes that the UAV will visit to complete the data collection task in the shortest possible time, ensuring that it does not run out of energy during its mission. We express this problem as a mixed-integer nonlinear problem and propose an efficient algorithm to solve the aforementioned NP-hard problem in polynomial time. Path planning problem aims to find a collision-free path for the UAV. While the state-of-the-art schemes have focused on solving the path planning problem in static environments, we study the problem in a dynamic environment with moving obstacles. We develop an algorithm that works on both static and dynamic environments. Our method combines deep reinforcement learning (RL) with graph-based global path planning algorithms to find a collision-free path for the UAV. One important advantage of our RL-based method over the existing studies is its map independency, which allows us to transform the agent's learning from one environment to another. Via simulation studies, we show that our method is significantly effective in improving the safety of the path planning algorithms in dynamic environments.