Robust Optimization-based Autonomous Dynamic Soaring with a Fixed-Wing UAV

TL;DR

The paper tackles autonomous dynamic soaring for fixed-wing UAVs by modeling wind shear with a sigmoidal vertical profile and formulating a robust trajectory optimization that accounts for wind-field uncertainty via multiple wind-field scenarios.A two-tier wind estimation architecture (local wind for control, global wind for planning) feeds into an offline robust OCP that yields a reference path, which is tracked by an INDI-based controller in real time.Key contributions include a robust path-optimization framework, a wind-agnostic path-following controller, and experimental validation in both simulation and flight tests that demonstrate robustness to wind estimation errors and small sim-to-real gaps.Overall, the work demonstrates that autonomous dynamic soaring under realistic wind conditions is achievable with a modular, wind-uncertainty-aware design, bringing DS closer to practical UAV endurance enhancements.

Abstract

Dynamic soaring is a flying technique to exploit the energy available in wind shear layers, enabling potentially unlimited flight without the need for internal energy sources. We propose a framework for autonomous dynamic soaring with a fixed-wing unmanned aerial vehicle (UAV). The framework makes use of an explicit representation of the wind field and a classical approach for guidance and control of the UAV. Robustness to wind field estimation error is achieved by constructing point-wise robust reference paths for dynamic soaring and the development of a robust path following controller for the fixed-wing UAV. The framework is evaluated in dynamic soaring scenarios in simulation and real flight tests. In simulation, we demonstrate robust dynamic soaring flight subject to varied wind conditions, estimation errors and disturbances. Critical components of the framework, including energy predictions and path-following robustness, are further validated in real flights to assure small sim-to-real gap. Together, our results strongly indicate the ability of the proposed framework to achieve autonomous dynamic soaring flight in wind shear.

Figures (11)

• Figure 1: Impression of one of the flight tests to validate tracking performance in strong wind, conducted at Chasseral, Switzerland.
• Figure 2: A simplified dynamic soaring cycle can be described by four phases: upward wind climb (1) and downwind descent (3) are energy gain phases, while high altitude turn (2) and low altitude turn (4) describe energy loss phases.
• Figure 3: Frame conventions used during this work. $\mathcal{I}$ denotes the inertial ENU frame, $\mathcal{B}$ denotes the body-fixed FRD frame and $\boldsymbol{\mathbf{v}}$ is the inertial velocity.
• Figure 4: Block diagram of the wind estimation. Measurements from the pitot, IMU and GPS are fused to estimate the parameters of a sigmoidal wind field function.
• Figure 5: Robust energy gain paths, colored by the initial velocity at the lowest point of the path.