A Game Theoretic Approach for Safe and Distributed Control of Unmanned Aerial Vehicles

This paper presents a distributed methodology to produce collision-free control laws for an Unmanned Aerial Vehicle (UAV) fleet. We use a game theoretic framework, where UAVs accommodate for individual and fleet goals, while respecting safety requirements. The method combines Control Barrier Functions (CBFs) and a primal-dual algorithm for Nash equilibrium (NE) seeking in generalized games. Feedback is introduced by Model Predictive Control (MPC) and we analyze its stability properties. The combination of these tools allows for a distributed, collision-free pointwise equilibrium solution, despite the agents' coupling, due to common target tracking and the collision avoidance constraints. Our algorithmic results are supported theoretically and our method's efficacy is demonstrated via extensive numerical simulations.