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

TL;DR

NMPC-Lander integrates a full-state nonlinear model predictive controller with Control Barrier Functions to enable precise, safe autonomous quadrotor landings on static and moving platforms. The approach uses a 12-state quadrotor model, onboard CasADi/Ipopt optimization over a short horizon, and CBF-based safety constraints to handle obstacles during descent. Experimental results in Gazebo and real-world tests show final position errors typically in the single-digit centimeter range, outperforming a B-spline plus A* baseline, and demonstrating robustness to dynamic platform movement and obstacle presence. The work advances onboard autonomous landing capabilities with formal safety guarantees and practical applicability for battery charging and platform swapping missions, with future work targeting efficiency improvements and enhanced predictive models.

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.

Figures (8)

• Figure 1: NMPC-Lander Technology Demonstration: Drone performing autonomous landing using NMPC-CBF framework
• Figure 2: System architecture of the NMPC-Lander.
• Figure 3: Quadrotor Reference System
• Figure 4: Gazebo Simulation for NMPC-CBF Framework
• Figure 5: Comparison of predicted and observed linear positions and velocities