Robust Integrated Planning and Control for Quadrotors in Dynamic Environments via NMPC with CBF Penalties

2026-05-31

AI Summary

This work addresses the challenges of infeasibility and unsafe operation in quadrotor trajectory planning and control under tight input constraints, external disturbances, and moving obstacles in dynamic environments. The authors propose an integrated strategy that combines nonlinear model predictive control (NMPC) with a control barrier function (CBF) formulated as an exponential soft penalty term, augmented by a high-gain disturbance observer and a Kalman filter to jointly achieve disturbance rejection and motion prediction of moving obstacles. Notably, this study presents the first real-hardware validation of the NMPC-CBF framework, where the soft CBF constraints significantly enhance optimization feasibility. Experimental results in both Gazebo simulations and physical trials demonstrate superior safety, robustness, and smoothness in obstacle avoidance compared to conventional NMPC and hard-constrained CBF approaches.

Abstract

This paper presents a new robust integrated planning and control (IPC) strategy for multirotor uncrewed aerial vehicles. We propose a nonlinear model predictive control (NMPC) formulation that embeds control barrier functions (CBFs) as exponential penalties, improving feasibility while ensuring smooth obstacle avoidance under tight input bounds. The penalty weights provide a practical tuning knob to trade off tracking accuracy against avoidance aggressiveness. We enhance the system robustness by employing a high-gain disturbance observer (HGDO) to estimate and compensate for external disturbances. We also incorporate a Kalman filter (KF) for computationally efficient, real-time prediction of obstacle motion, enabling avoidance of moving obstacles. Comparative studies against both conventional NMPC and NMPC with hard CBF constraints, validated in Gazebo and hardware experiments, demonstrate superior feasibility, safety, and robustness. To the best of our knowledge, this is the first hardware-validated NMPC-CBF IPC framework, offering a practical step toward safe quadrotor deployment in dynamic environments.

Problem

• quadrotor
• dynamic environments
• obstacle avoidance
• robust control
• integrated planning and control

Innovation

• NMPC
• Control Barrier Functions
• Integrated Planning and Control
• Disturbance Observer
• Dynamic Obstacle Avoidance