Robust Integrated Planning and Control for Quadrotors in Dynamic Environments via NMPC with CBF Penalties
For quadrotor control in dynamic environments, this work provides a practical, hardware-validated NMPC-CBF framework that improves feasibility and robustness over existing methods.
This paper proposes a robust integrated planning and control strategy for quadrotors using NMPC with CBF penalties, achieving superior feasibility, safety, and robustness in dynamic environments, validated in Gazebo and hardware experiments.
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.