Flight Performance

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Aerial Robots

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Flight Performance

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Quadrotor Control

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Quadrotor Flight

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Aerial Manipulation

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183 papers

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Robust Integrated Planning and Control for Quadrotors in Dynamic Environments via NMPC with CBF Penalties
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May 31, 2026 · cs.ROJ/K move · Enter open · S save

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

Zeinab Shayan, Mohammadreza Izadi, Reza Faieghi

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.

Quadrotor Flight Model Predictive Control Control Barrier Functions Obstacle Avoidance Planning Methods

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