Multi-fidelity black-box optimization for time-optimal quadrotor maneuvers
Name
2006.02513.pdf
Description
Accepted version
Size
1.1 MB
Format
Adobe PDF
Checksum (MD5)
e4da494bb7fb6c849668f7c8b2c19bd2
Author(s)
Ryou, Gilhyun
Tal, Ezra
Karaman, Sertac
Date Issued
2021
Journal
International Journal of Robotics Research
Publisher
SAGE Publications
Citation
Ryou, Gilhyun, Tal, Ezra and Karaman, Sertac. 2021. "Multi-fidelity black-box optimization for time-optimal quadrotor maneuvers." International Journal of Robotics Research, 40 (12-14).
Version
Author's final manuscript
Abstract
We consider the problem of generating a time-optimal quadrotor trajectory for highly maneuverable vehicles, such as quadrotor aircraft. The problem is challenging because 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 complex aerodynamic and electromechanical phenomena, 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 the number of costly flight experiments is kept to a minimum. The algorithm is thoroughly evaluated for the trajectory generation problem in two different scenarios: (1) connecting predetermined waypoints; (2) planning in obstacle-rich environments. For each scenario, we conduct 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.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1177/02783649211033317
