Cooperative path planning for multiple MAVs operating in unknown environments

In recent years, Micro Aerial Vehicles (MAVs) have become widely available and are successfully used in many real scenarios. While the early applications like surveillance mostly utilized single MAVs or a group of multiple, yet non-cooperative MAVs, recent research is more focused on a group of cooperating MAVs. A typical example is the payload transport task, where multiple MAVs carry a single object. This problem has been studied mainly from the control theory point of view, providing robust control to cooperating MAVs using the dynamics of the whole system. Real applications, however, require operating in unknown environments with obstacles, which needs motion planning. In this paper, we propose a novel motion planning method for multiple MAVs operating in unknown environments. The proposed work is based on the Sensor-based Random Trees method (SRT), which was originally intended for exploration of unknown environments. We extend the method for online path planning of multi MAVs. In the proposed method, each MAV makes a motion plan and exchanges key waypoints with other MAVs to ensure that their mutual positions satisfy the mission constraints. The performance of the method is demonstrated in various simulation experiments.