发表机构
Southeast University; Xiamen University of Technology（东南大学; 厦门理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
DR-IPC提出一种集成规划与控制方法，结合路径引导与NMPC直接生成控制量，在扰动下显著提升四旋翼导航任务完成率并降低高度误差。
AI 中文摘要
在杂乱环境中基于LiDAR的四旋翼导航在外部扰动下仍然具有挑战性，特别是当避障运动生成和扰动抑制控制被分离在不同层中时。本文提出了扰动鲁棒集成规划与控制（DR-IPC），它将轻量级路径引导与非线性模型预测控制（NMPC）相结合，直接生成角速度和推力。一个互联的扩展卡尔曼滤波器和非线性扰动观测器联合提供滤波状态估计和重构扰动，用于NMPC预测。所提出的公式统一了非线性四旋翼动力学、执行器约束、局部运动生成和惩罚的安全飞行走廊残差，无需单独的轨迹优化阶段。Gazebo和MARSIM仿真以及室内和室外实验验证了DR-IPC在风、悬挂载荷、狭窄通道、球体冲击和动态障碍物反应性规避下的性能。在带扰动的多目标导航中，DR-IPC将Gazebo中完成的任务数从1/10提高到9/10，并在实验中将高度RMSE从0.34米降低到0.01米。完整系统在机载100 Hz下运行。补充视频可在项目页面（此https URL）获取，源代码将发布。
英文摘要
LiDAR-based quadrotor navigation in cluttered environments remains challenging under external disturbances, particularly when obstacle-aware motion generation and disturbance-rejection control are handled in separate layers. This article presents disturbance-resilient integrated planning and control (DR-IPC), which combines lightweight path guidance with nonlinear model predictive control (NMPC) to directly generate angular velocity and thrust. An interconnected extended Kalman filter and nonlinear disturbance observer jointly provide filtered state estimates and reconstructed disturbances for NMPC prediction. The resulting formulation unifies nonlinear quadrotor dynamics, actuator constraints, local motion generation, and penalised safe-flight-corridor residuals without requiring a separate trajectory-optimization stage. Gazebo and MARSIM simulations, together with indoor and outdoor experiments, validate DR-IPC under wind, suspended payloads, narrow passages, ball impacts and reactive avoidance of a dynamic obstacle. In multi-goal navigation with disturbances, DR-IPC increases the number of completed missions from 1/10 to 9/10 in Gazebo and reduces the altitude RMSE from 0.34 to 0.01 m in experiments. The complete system operates onboard at 100 Hz. Supplementary videos are available on the project page https://drpp316.github.io/DR-IPC-Page/, and the source code will be released.
Comments11 pages, 16 figures, 7 tables
