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.

翻译：在复杂环境中导航要求无人机与自主系统能够实时执行轨迹跟踪与避障任务。尽管众多控制策略已有效采用线性近似方法，但处理无人机非线性动力学特性，尤其是在障碍物密集环境中的控制问题，仍是亟待深入研究的关键挑战。本文针对DJI Matrice 100平台提出一种非线性模型预测控制框架，通过采用动力学模型与B样条插值技术生成平滑参考轨迹，在满足安全约束的前提下确保最小轨迹偏差。该框架支持多种轨迹类型，并采用基于惩罚的代价函数以保证紧凑机动中的控制精度。利用CasADi工具实现高效实时优化，使无人机在严格计算约束下仍能保持鲁棒运行。仿真与真实场景的室内外实验表明，该非线性模型预测控制框架能够有效适应环境扰动，实现平滑无碰撞的自主导航。