Path Planning with Multiple Rapidly-exploring Random Trees for Teams of Robots

This paper addresses the problem of real-time planning and control of a group of aerial vehicles flying in environments with obstacles and subject to disturbances and uncertainties. We present a novel methodology to the coordination of multiple vehicles based on a classical technique known as Closed Loop Rapidly-exploring Random Trees (CL-RRT). We use a distributed processing framework and communication to optimize the plans being executed by each agent of the team, providing better solutions to the execution step of the CL-RRT. This technique endows each agent with the capability to navigate through the environment while avoiding collisions with obstacles as well as with other team agents. The keypoint is a prediction scheme executed by each robot that infers the behavior of the entire team at each time step. We show the applicability of the method by planning and executing waypoint navigation tasks for a group of Micro Unmanned Aerial Vehicles (MUAVs) -modelled in SE (3) -in outdoor environments.