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

2025-05-06

AI Summary

This work addresses the challenge of high-precision, safe autonomous landing of UAVs on static and dynamic platforms—while simultaneously satisfying battery endurance and charging requirements. We propose a novel integrated NMPC-CBF framework that, for the first time, unifies trajectory tracking, dynamic platform motion compensation, and static obstacle avoidance within a single control formulation. The method employs nonlinear model predictive control (NMPC) for robust trajectory tracking and embeds control barrier functions (CBFs) to enforce real-time safety constraints. Hardware-in-the-loop experiments demonstrate mean landing errors of 9.0 cm on static platforms and 11.0 cm on dynamic platforms. Position tracking accuracy improves by nearly threefold over a B-spline + A* baseline. This work establishes a verifiable, safety-guaranteed control paradigm for resource-constrained UAV autonomous shipboard landing and mobile charging.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Enabling precise autonomous UAV landing on mobile platforms
• Integrating NMPC and CBF for safe obstacle avoidance
• Overcoming battery life and flight range limitations

Innovation

• Nonlinear MPC for precise trajectory tracking
• Control Barrier Functions for collision avoidance
• Integration of NMPC and CBF for safe landing