NMPC-Lander: Nonlinear MPC with Barrier Function for UAV Landing on a Mobile Platform

TL;DR

This paper introduces NMPC-Lander, a control system combining Nonlinear Model Predictive Control and Control Barrier Functions to enable precise, safe autonomous drone landings on static and moving platforms, outperforming existing methods.

Contribution

The paper presents a novel control architecture integrating NMPC with CBF for UAV landing, demonstrating improved accuracy and safety on real hardware with mobile and static platforms.

Findings

Average landing position error of 9.0 cm and 11 cm for static and mobile platforms.

Outperforms B-spline + A* planning method by nearly three times in position tracking.

Demonstrates high robustness and practical effectiveness in real-world experiments.

Abstract

Quadcopters are versatile aerial robots gaining popularity in numerous critical applications. However, their operational effectiveness is constrained by limited battery life and restricted flight range. To address these challenges, autonomous drone landing on stationary or mobile charging and battery-swapping stations has become an essential capability. In this study, we present NMPC-Lander, a novel control architecture that integrates Nonlinear Model Predictive Control (NMPC) with Control Barrier Functions (CBF) to achieve precise and safe autonomous landing on both static and dynamic platforms. Our approach employs NMPC for accurate trajectory tracking and landing, while simultaneously incorporating CBF to ensure collision avoidance with static obstacles. Experimental evaluations on the real hardware demonstrate high precision in landing scenarios, with an average final position error of 9.0 cm and 11 cm for stationary and mobile platforms, respectively. Notably, NMPC-Lander outperforms the B-spline combined with the A* planning method by nearly threefold in terms of position tracking, underscoring its superior robustness and practical effectiveness.