Multi-Fidelity Black-Box Optimization for Time-Optimal Quadrotor Maneuvers

TL;DR

This paper introduces a multi-fidelity Bayesian optimization approach to generate time-optimal quadrotor trajectories, effectively balancing simulation and real-world data to improve flight speed and efficiency.

Contribution

It presents a novel multi-fidelity optimization framework that models feasibility constraints across analytical, simulation, and real-world data for high-speed quadrotor flight.

Findings

Trajectories achieved speeds up to 11 m/s.

Significantly faster trajectories than minimum-snap planning.

Reduced number of costly flight experiments.

Abstract

We consider the problem of generating a time-optimal quadrotor trajectory that attains a set of prescribed waypoints. This problem is challenging since the optimal trajectory is located on the boundary of the set of dynamically feasible trajectories. This boundary is hard to model as it involves limitations of the entire system, including hardware and software, in agile high-speed flight. In this work, we propose a multi-fidelity Bayesian optimization framework that models the feasibility constraints based on analytical approximation, numerical simulation, and real-world flight experiments. By combining evaluations at different fidelities, trajectory time is optimized while keeping the number of required costly flight experiments to a minimum. The algorithm is thoroughly evaluated in both simulation and real-world flight experiments at speeds up to 11 m/s. Resulting trajectories were found to be significantly faster than those obtained through minimum-snap trajectory planning.