用惩罚项融合障碍物规避，让四旋翼在动态环境更安全稳定。
Robust Integrated Planning and Control for Quadrotors in Dynamic Environments via NMPC with CBF Penalties
- NMPC中引入CBF指数惩罚，平衡避障与跟踪精度。
- 高增益观测器补偿外部干扰，实测提升鲁棒性。
- 结合卡尔曼滤波预测动障，适合真实飞行场景。
本文提出一种新型鲁棒的多旋翼无人机集成规划与控制（IPC）策略。采用非线性模型预测控制（NMPC）框架，将控制屏障函数（CBF）作为指数惩罚项嵌入，确保在严苛输入约束下仍能实现平滑避障并提升可行性。惩罚权重提供可调参数，用于权衡轨迹跟踪精度与避障激进程度。通过高增益干扰观测器（HGDO）估计并补偿外部扰动，增强系统鲁棒性；同时结合卡尔曼滤波（KF）实现计算高效的动障运动实时预测，支持对移动障碍物的规避。在Gazebo仿真与硬件实验中，与传统NMPC及硬约束CBF-NMPC对比，本方法在可行性、安全性与鲁棒性上均表现更优。据我们所知，这是首个经过硬件验证的NMPC-CBF IPC框架，为四旋翼在动态环境中的安全部署迈出实用一步。
原文摘要 · Abstract (English)
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.
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