Quadrotor Fleet Autonomous Navigation: Fusing Virtual Points Control and Nonlinear Potential Fields

Abstract

This article introduces a multilayer navigation algorithm for a fleet of unmanned aerial vehicles (UAVs). The proposed architecture consists of a fusion of virtual point controllers and potential field techniques. On the one hand, a potential function is constructed for every agent such that its position smoothly and robustly converges to a virtual guidance point while avoiding collisions with other agents. The virtual points, on the other hand, are controlled to fulfill a swarm control goal such as target tracking, station keeping, or search and rescue missions. Therefore, the suggested system has two levels of hierarchy, but the algorithm can be generalized for multiple levels. The vehicle translational and rotational dynamics are controlled using an internal loop based on gradient tracking and sliding mode controllers. The architecture is validated in simulations and real-time experiments, showing good performance for the closed-loop system.