Real-Time Planning with Multi-Fidelity Models for Agile Flights in Unknown Environments
Name
1810.01035.pdf
Description
Accepted version
Size
6.26 MB
Format
Adobe PDF
Checksum (MD5)
cf7ecd9014056735ceb992a426ced0c2
Author(s)
Tordesillas, Jesus
Lopez, Brett T.
Carter, John
Ware, John
How, Jonathan P.
Date Issued
May 2019
Publisher
IEEE
Citation
Tordesillas, Jesus, Lopez, Brett T., Carter, John, Ware, John and How, Jonathan P. 2019. "Real-Time Planning with Multi-Fidelity Models for Agile Flights in Unknown Environments."
Version
Author's final manuscript
Abstract
© 2019 IEEE. Autonomous navigation through unknown environments is a challenging task that entails real-time localization, perception, planning, and control. UAVs with this capability have begun to emerge in the literature with advances in lightweight sensing and computing. Although the planning methodologies vary from platform to platform, many algorithms adopt a hierarchical planning architecture where a slow, low-fidelity global planner guides a fast, high-fidelity local planner. However, in unknown environments, this approach can lead to erratic or unstable behavior due to the interaction between the global planner, whose solution is changing constantly, and the local planner; a consequence of not capturing higher-order dynamics in the global plan. This work proposes a planning framework in which multi-fidelity models are used to reduce the discrepancy between the local and global planner. Our approach uses high-, medium-, and low-fidelity models to compose a path that captures higher-order dynamics while remaining computationally tractable. In addition, we address the interaction between a fast planner and a slower mapper by considering the sensor data not yet fused into the map during the collision check. This novel mapping and planning framework for agile flights is validated in simulation and hardware experiments, showing replanning times of 5-40 ms in cluttered environments.
MIT Department
Massachusetts Institute of Technology. Aerospace Controls Laboratory
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/icra.2019.8794248
