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dc.contributor.authorTordesillas, Jesus
dc.contributor.authorLopez, Brett T.
dc.contributor.authorCarter, John
dc.contributor.authorWare, John
dc.contributor.authorHow, Jonathan P.
dc.date.accessioned2021-11-08T19:53:25Z
dc.date.available2021-11-08T19:53:25Z
dc.date.issued2019-05
dc.identifier.urihttps://hdl.handle.net/1721.1/137805
dc.description.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.en_US
dc.language.isoen
dc.publisherIEEEen_US
dc.relation.isversionof10.1109/icra.2019.8794248en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcearXiven_US
dc.titleReal-Time Planning with Multi-Fidelity Models for Agile Flights in Unknown Environmentsen_US
dc.typeArticleen_US
dc.identifier.citationTordesillas, 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."
dc.contributor.departmentMassachusetts Institute of Technology. Aerospace Controls Laboratory
dc.contributor.departmentMassachusetts Institute of Technology. Department of Aeronautics and Astronautics
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2019-10-28T17:38:46Z
dspace.date.submission2019-10-28T17:38:55Z
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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