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dc.contributor.authorBlackmore, Lars
dc.contributor.authorOno, Masahiro
dc.contributor.authorWilliams, Brian Charles
dc.date.accessioned2013-09-24T18:55:14Z
dc.date.available2013-09-24T18:55:14Z
dc.date.issued2011-12
dc.date.submitted2011-02
dc.identifier.issn1552-3098
dc.identifier.issn1941-0468
dc.identifier.urihttp://hdl.handle.net/1721.1/80914
dc.description.abstractAutonomous vehicles need to plan trajectories to a specified goal that avoid obstacles. For robust execution, we must take into account uncertainty, which arises due to uncertain localization, modeling errors, and disturbances. Prior work handled the case of set-bounded uncertainty. We present here a chance-constrained approach, which uses instead a probabilistic representation of uncertainty. The new approach plans the future probabilistic distribution of the vehicle state so that the probability of failure is below a specified threshold. Failure occurs when the vehicle collides with an obstacle or leaves an operator-specified region. The key idea behind the approach is to use bounds on the probability of collision to show that, for linear-Gaussian systems, we can approximate the nonconvex chance-constrained optimization problem as a disjunctive convex program. This can be solved to global optimality using branch-and-bound techniques. In order to improve computation time, we introduce a customized solution method that returns almost-optimal solutions along with a hard bound on the level of suboptimality. We present an empirical validation with an aircraft obstacle avoidance example.en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (Grant IIS-1017992)en_US
dc.description.sponsorshipBoeing Company (Grant MIT-BA-GTA-1)en_US
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/tro.2011.2161160en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alike 3.0en_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/en_US
dc.sourceMIT web domainen_US
dc.titleChance-Constrained Optimal Path Planning With Obstaclesen_US
dc.typeArticleen_US
dc.identifier.citationBlackmore, Lars, Masahiro Ono, and Brian C. Williams. “Chance-Constrained Optimal Path Planning With Obstacles.” IEEE Transactions on Robotics 27, no. 6 (December 2011): 1080-1094.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Aeronautics and Astronauticsen_US
dc.contributor.mitauthorOno, Masahiroen_US
dc.contributor.mitauthorWilliams, Brian Charlesen_US
dc.relation.journalIEEE Transactions on Roboticsen_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.orderedauthorsBlackmore, Lars; Ono, Masahiro; Williams, Brian C.en_US
dc.identifier.orcidhttps://orcid.org/0000-0002-1057-3940
mit.licenseOPEN_ACCESS_POLICYen_US


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