Learning-Based Formation Control of UAV-Fleet

In the context of wireless networked robotics, complex missions, such as autonomous Search and Rescue, may require the use of multiple Unmanned Aerial Vehicles (UAVs) to achieve higher efficiency and Quality of Service (QoS) while assuring mission-specific imperatives like the maximization of area covered in a single passage of the fleet over area subsections. In this paper, we present a learning-based formation control protocol that adapts the principle of Q-learning to pilot an autonomous fleet of networked UAVs to maintain formation throughout a mission where large quantities of data need to be exchanged. Also, the protocol tries to ensure rotational formation control by leveraging only the signal strength extrapolated from the UAV communications. A leader-follower model is used to control the fleet. One UAV serves as the leader, and the remaining as the followers. The followers use the Received Signal Strength Indicator (RSSI) values obtained from their neighbors to autonomously determine the leader's direction of movement and maintain formation orientation to avoid area coverage overlapping. We carried out several simulation experiments to evaluate the performance of the proposed scheme in terms of QoS and convergence time of the formation under varying velocities.