Stream Function-Based Navigation for Complex Quadcopter Obstacle Avoidance

2025-07-09

AI Summary

Real-time obstacle avoidance for UAVs in complex, partially observable environments remains challenging—particularly regarding delayed responses and insufficient stability when encountering nearby dynamic obstacles. Method: This paper proposes a model predictive control (MPC) framework integrating flow-field modeling with high-order control barrier functions (HOCBFs). Obstacles are modeled as two-dimensional rigid surfaces within an inviscid, incompressible flow field; a vortex panel method generates a safety-oriented flow field. Robust state estimation and motion prediction for dynamic obstacles are achieved via a minimum-boundary ellipse envelope and adaptive Kalman filtering. Contribution/Results: The approach is validated in Gazebo simulation and on a COEX Clover quadcopter equipped with a 360° LiDAR. It achieves significantly improved responsiveness, safety, and real-time performance (<50 ms control cycle) for close-range obstacle avoidance, demonstrating strong robustness and engineering deployability.

Technology Category

• Intelligent Robots: State Estimation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Develops stream function-based navigation for quadcopter obstacle avoidance
• Integrates MPC and HOCBF to handle dynamic obstacles
• Uses real-time sensing for complex, partially observed environments

Innovation

• Stream function-based navigation with vortex panel method
• Model predictive controller with barrier functions
• Adaptive Kalman filter for obstacle dynamics