Model Predictive Trajectory Tracking and Collision Avoidance for Reliable Outdoor Deployment of Unmanned Aerial Vehicles

Tomás Báca, Daniel Hert, Giuseppe Loianno, Martin Saska, Vijay Kumar

Abstract: We propose a novel approach for optimal trajectory tracking for unmanned aerial vehicles (UAV), using a linear model predictive controller (MPC) in combination with non-linear state feedback. The solution relies on fast onboard simulation of the translational dynamics of the UAV, which is guided by a linear MPC. By sampling the states of the virtual UAV, we create a control command for fast non-linear feedback, which is capable of performing agile maneuvers with high precision. In addition, the proposed pipeline provides an interface for a decentralized collision avoidance system for multi-UAY scenarios. Our solution makes use of the long prediction horizon of the linear MPC and allows safe outdoors execution of multi-UAV experiments without the need for in-advance collision-free planning. The practicality of the tracking mechanism is shown in combination with priority-based collision resolution strategy, which performs sufficiently in experiments with up to 5 UAVs. We present a statistical and experimental evaluation of the platform in both simulation and real-world examples, demonstrating the usability of the approach.