On Learning Multi-UAV Policy for Multi-Object Tracking and Formation Control

Autonomous navigation and formation control of multi-UAV systems poses a significant challenge for the robotic systems that operate in partially-observable, dynamic and continuous environments. This paper addresses the problem of multi-UAV formation control while cooperatively tracking a set of moving objects. The objective of the multi-UAV system is to maintain the moving objects under their joint coverage along with aligning themselves in an optimal formation for maximizing the overall area coverage. We develop a multi-agent reinforcement learning model to learn a cooperative multi-UAV policy for the multi-object tracking and formation control. We design a reward function to encode the objectives of tracking, formation and collision avoidance into the model. The proposed deep reinforcement learning based model is deployed and tested against a baseline controller using the Gazebo simulator. The result indicates that the proposed model is robust against the tracking and alignment errors outperforming the baseline model.