Show simple item record

dc.contributor.authorDawson, Charles
dc.contributor.authorJasour, Ashkan
dc.contributor.authorHofmann, Andreas
dc.contributor.authorWilliams, Brian
dc.date.accessioned2022-09-21T15:45:22Z
dc.date.available2022-09-21T15:45:22Z
dc.date.issued2020
dc.identifier.urihttps://hdl.handle.net/1721.1/145544
dc.description.abstract© 2020 IEEE. Real-world environments are inherently uncertain, and to operate safely in these environments robots must be able to plan around this uncertainty. In the context of motion planning, we desire systems that can maintain an acceptable level of safety as the robot moves, even when the exact locations of nearby obstacles are not known. In this paper, we solve this chance-constrained motion planning problem using a sequential convex optimization framework. To constrain the risk of collision incurred by planned movements, we employ geometric objects called ϵ-shadows to compute upper bounds on the risk of collision between the robot and uncertain obstacles. We use these ϵ-shadow-based estimates as constraints in a nonlinear trajectory optimization problem, which we then solve by iteratively linearizing the non-convex risk constraints. This sequential optimization approach quickly finds trajectories that accomplish the desired motion while maintaining a user-specified limit on collision risk. Our method can be applied to robots and environments with arbitrary convex geometry; even in complex environments, it runs in less than a second and provides provable guarantees on the safety of planned trajectories, enabling fast, reactive, and safe robot motion in realistic environments.en_US
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionof10.1109/IROS45743.2020.9341193en_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.titleProvably Safe Trajectory Optimization in the Presence of Uncertain Convex Obstaclesen_US
dc.typeArticleen_US
dc.identifier.citationDawson, Charles, Jasour, Ashkan, Hofmann, Andreas and Williams, Brian. 2020. "Provably Safe Trajectory Optimization in the Presence of Uncertain Convex Obstacles." IEEE International Conference on Intelligent Robots and Systems.
dc.contributor.departmentMassachusetts Institute of Technology. Department of Aeronautics and Astronauticsen_US
dc.relation.journalIEEE International Conference on Intelligent Robots and Systemsen_US
dc.eprint.versionOriginal manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2022-09-21T15:42:04Z
dspace.orderedauthorsDawson, C; Jasour, A; Hofmann, A; Williams, Ben_US
dspace.date.submission2022-09-21T15:42:05Z
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record