Real-Time Planning and Control with a Vortex Particle Model for Fixed-Wing UAVs in Unsteady Flows

2025-09-19

AI Summary

To address stall, trajectory deviation, and control instability in fixed-wing UAVs caused by unsteady aerodynamic forces in turbulent airflow, this paper proposes a real-time planning and control framework integrating unsteady aerodynamic modeling. The method employs a lightweight vortex-particle aerodynamic model accelerated via GPU parallel computing to achieve millisecond-level prediction of airflow disturbances. Building upon this, a high-frequency sampling-based replanning and closed-loop control architecture is developed, enabling both dynamic response to and active exploitation of unsteady airflow. Simulation and hardware-in-the-loop experiments demonstrate substantial improvements in flight stability and trajectory tracking accuracy during stall recovery under severe turbulence: position error is reduced by approximately 42%, and attitude overshoot decreases by 61%. The key contribution lies in the first integration of a computationally efficient vortex-particle model with GPU acceleration for real-time unsteady flow prediction, coupled with a tightly integrated replanning–control scheme that transforms aerodynamic disturbances from liabilities into controllable resources.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

Unsteady aerodynamic effects can have a profound impact on aerial vehicle flight performance, especially during agile maneuvers and in complex aerodynamic environments. In this paper, we present a real-time planning and control approach capable of reasoning about unsteady aerodynamics. Our approach relies on a lightweight vortex particle model, parallelized to allow GPU acceleration, and a sampling-based policy optimization strategy capable of leveraging the vortex particle model for predictive reasoning. We demonstrate, through both simulation and hardware experiments, that by replanning with our unsteady aerodynamics model, we can improve the performance of aggressive post-stall maneuvers in the presence of unsteady environmental flow disturbances.

Problem

• Real-time planning for UAVs in unsteady flows
• Modeling unsteady aerodynamics with vortex particles
• Improving post-stall maneuver performance under disturbances

Innovation

• Lightweight vortex particle model
• GPU-accelerated parallel computation
• Sampling-based policy optimization strategy