用流函数建模障碍物，实现无人机复杂环境实时避障
Stream Function-Based Navigation for Complex Quadcopter Obstacle Avoidance
- 将障碍物抽象为无粘不可压缩流场中的二维刚体表面
- 结合模型预测控制与高阶屏障函数，实现近距离快速避障
- 适用于带360度激光雷达的无人机，适合动态障碍物场景
本文提出一种基于流函数的导航控制系统，用于无人机避障。障碍物被建模为无粘、不可压缩流场中的二维刚体表面。系统采用涡量面板法（VPM）生成轨迹，并引入安全裕度控制流场特性，结合实时感知在复杂部分可观测环境中导航。为克服VPM在相对距离处理和近距离高速障碍物规避上的不足，系统集成基于高阶控制屏障函数（HOCBF）的模型预测控制器（MPC），将VPM轨迹生成、状态估计与约束处理统一纳入滚动时域优化问题。障碍物边界通过最小包围椭圆（MBE）封闭，自适应卡尔曼滤波器（AKF）捕捉并预测障碍物运动，将其估计结果传递至MPC-HOCBF以实现快速避障动作。评估在搭载PX4的Clover无人机Gazebo仿真平台及配备360°激光雷达的真实实验中完成。
原文摘要 · Abstract (English)
This article presents a novel stream function-based navigational control system for obstacle avoidance, where obstacles are represented as two-dimensional (2D) rigid surfaces in inviscid, incompressible flows. The approach leverages the vortex panel method (VPM) and incorporates safety margins to control the stream function and flow properties around virtual surfaces, enabling navigation in complex, partially observed environments using real-time sensing. To address the limitations of the VPM in managing relative distance and avoiding rapidly accelerating obstacles at close proximity, the system integrates a model predictive controller (MPC) based on higher-order control barrier functions (HOCBF). This integration incorporates VPM trajectory generation, state estimation, and constraint handling into a receding-horizon optimization problem. The 2D rigid surfaces are enclosed using minimum bounding ellipses (MBEs), while an adaptive Kalman filter (AKF) captures and predicts obstacle dynamics, propagating these estimates into the MPC-HOCBF for rapid avoidance maneuvers. Evaluation is conducted using a PX4-powered Clover drone Gazebo simulator and real-time experiments involving a COEX Clover quadcopter equipped with a 360 degree LiDAR sensor.
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