用非线性模型预测控制实现无人机室内外避障，实时稳定飞行
Custom Non-Linear Model Predictive Control for Obstacle Avoidance in Indoor and Outdoor Environments
- 基于动态模型与B样条插值生成平滑轨迹，支持多种路径类型
- 在室内外实验中实现无碰撞导航，抗干扰能力强
- 适用于计算资源受限的无人机平台，适合实际部署
复杂环境中的自主飞行需无人机（UAV）实时完成轨迹跟踪与避障。尽管已有诸多控制策略采用线性近似，但在障碍物密集场景下，仍需解决无人机非线性动力学带来的挑战。本文针对DJI Matrice 100无人机，提出一种非线性模型预测控制（NMPC）框架，结合动态模型与B样条插值生成平滑参考轨迹，确保最小偏差并满足安全约束。该框架支持多种轨迹类型，采用基于惩罚项的代价函数提升紧凑机动下的控制精度。通过CasADi实现高效实时优化，在计算资源受限条件下仍保持稳健运行。仿真与真实室内外实验表明，该方法能有效应对扰动，实现平滑、无碰撞导航。
原文摘要 · Abstract (English)
Navigating complex environments requires Unmanned Aerial Vehicles (UAVs) and autonomous systems to perform trajectory tracking and obstacle avoidance in real-time. While many control strategies have effectively utilized linear approximations, addressing the non-linear dynamics of UAV, especially in obstacle-dense environments, remains a key challenge that requires further research. This paper introduces a Non-linear Model Predictive Control (NMPC) framework for the DJI Matrice 100, addressing these challenges by using a dynamic model and B-spline interpolation for smooth reference trajectories, ensuring minimal deviation while respecting safety constraints. The framework supports various trajectory types and employs a penalty-based cost function for control accuracy in tight maneuvers. The framework utilizes CasADi for efficient real-time optimization, enabling the UAV to maintain robust operation even under tight computational constraints. Simulation and real-world indoor and outdoor experiments demonstrated the NMPC ability to adapt to disturbances, resulting in smooth, collision-free navigation.
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