Waypoint Optimization Using Bayesian Optimization: A Case Study in Airborne Wind Energy Systems

TL;DR

This paper introduces a Bayesian optimization-based framework for online waypoint adaptation in airborne wind energy systems, improving flight path efficiency through data-driven modeling and real-time optimization.

Contribution

It presents a novel, computationally efficient Bayesian optimization approach using basis parameters for adaptive flight path planning in airborne wind energy systems.

Findings

Framework successfully adapts flight paths in simulation

Gaussian Process models effectively predict objective function

Method demonstrates potential for real-time optimization

Abstract

We present a data-driven optimization framework that aims to address online adaptation of the flight path shape for an airborne wind energy system (AWE) that follows a repetitive path to generate power. Specifically, Bayesian optimization, which is a data-driven algorithm for finding the optimum of an unknown objective function, is utilized to solve the waypoint adaptation. To form a computationally efficient optimization framework, we describe each figure-$8$ flight via a compact set of parameters, termed as basis parameters. We model the underlying objective function by a Gaussian Process (GP). Bayesian optimization utilizes the predictive uncertainty information from the GP to determine the best subsequent basis parameters. Once a path is generated using Bayesian optimization, a path following mechanism is used to track the generated figure-$8$ flight. The proposed framework is validated on a simplified $2$-dimensional model that mimics the key behaviors of a $3$-dimensional AWE system. We demonstrate the capability of the proposed framework in a simulation environment for a simplified $2$-dimensional AWE system model.