Cooperative frontier-based exploration strategy for multi-robot system

In this paper, a cooperative exploration strategy of an unknown environment using a team of Micro-Aerial Vehicle (MAV) with embedded vision is proposed. The key problem is to cooperatively choose specific regions of the environment to be simultaneously explored and mapped by each robot in an optimized manner, in order to reduce exploration time and energy consumption. Further, target goals are assigned to robots by considering a trade-off between fast exploration and getting detailed grid maps, thus the robots can be efficiently distributed over the environment. Hence, robots communication skills are essential and usually the robots exchange their reconstructed maps for decision making. However, in this paper, the novelty is to exchange map frontier points between robots instead of the whole grid map in order to save communication bandwidth. Our approach is implemented and tested under ROS using the GAZEBO environment for multi-MAV simulation. In addition, we use three MAVs to show that the proposed approach efficiently spreads the robots in a cooperatively way into the environment, and also minimizes the exploration time. Furthermore, we propose an evaluation of our system performance using an Ad Hoc network to point out the saved exchanged data size and the ability of the multi-MAV system to cope with real network issues.