Stream Function-Based Navigation for Complex Quadcopter Obstacle Avoidance

TL;DR

This paper introduces a stream function-based control system for quadcopter obstacle avoidance, combining vortex panel methods, model predictive control, and adaptive filtering to navigate complex environments in real-time.

Contribution

It presents a novel integration of stream function navigation with MPC and HOCBF for improved obstacle avoidance in complex, partially observed environments.

Findings

Successful simulation in Gazebo with PX4 drone

Real-time obstacle avoidance with a COEX Clover quadcopter

Effective handling of dynamic obstacles using AKF and MPC

Abstract

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.