Show simple item record

dc.contributor.authorHofmann, Andreas
dc.contributor.authorHelbert, Justin C.
dc.contributor.authorFernandez Gonzalez, Enrique
dc.contributor.authorSmith, Scott
dc.contributor.authorWilliams, Brian
dc.date.accessioned2017-01-05T15:10:31Z
dc.date.available2017-01-05T15:10:31Z
dc.date.issued2015-07
dc.identifier.urihttp://hdl.handle.net/1721.1/106198
dc.description.abstractCurrent motion planners, such as the ones available in ROS MoveIt, can solve difficult motion planning problems. However, these planners are not practical in unstructured, rapidly-changing environments. First, they assume that the environment is well-known, and static during planning and execution. Second, they do not support temporal constraints, which are often important for synchronization between a robot and other actors. Third, because many popular planners generate completely new trajectories for each planning problem, they do not allow for representing persistent control policy information associated with a trajectory across planning problems. We present Chekhov, a reactive, integrated motion planning and execution system that addresses these problems. Chekhov uses a Tube-based Roadmap in which the edges of the roadmap graph are families of trajectories called flow tubes, rather than the single trajectories commonly used in roadmap systems. Flow tubes contain control policy information about how to move through the tube, and also represent the dynamic limits of the system, which imply temporal constraints. This, combined with an incremental APSP algorithm for quickly finding paths in the roadmap graph, allows Chekhov to operate in rapidly changing environments. Testing in simulation, and with a robot testbed has shown improvement in planning speed and motion predictability over current motion planners.en_US
dc.description.sponsorshipBoeing Company (Contract MIT-BA-GTA-1)en_US
dc.language.isoen_US
dc.publisherAAAI Press/International Joint Conferences on Artificial Intelligenceen_US
dc.relation.isversionofhttp://ijcai-15.org/index.php/accepted-papersen_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceMIT web domainen_US
dc.titleReactive Integrated Motion Planning and Executionen_US
dc.typeArticleen_US
dc.identifier.citationHofmann, Andreas et al. "Reactive Integrated Motion Planning and Execution" Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015), Buenos Aires, Argentina, 25-31 July, 2015.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.mitauthorHofmann, Andreas
dc.contributor.mitauthorHelbert, Justin C.
dc.contributor.mitauthorFernandez Gonzalez, Enrique
dc.contributor.mitauthorSmith, Scott
dc.contributor.mitauthorWilliams, Brian
dc.relation.journalProceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015)en_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dspace.orderedauthorsHofmann, Andreas; Fernandez, Enrique; Helbert, Justin; Smith, Scott; Williams, Brianen_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0002-4787-4587
dc.identifier.orcidhttps://orcid.org/0000-0002-1737-950X
mit.licenseOPEN_ACCESS_POLICYen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record