AN ADAPTIVELY SAMPLED PATH PLANNER USING WAYPOINTS: AN ANY-ANGLE VARIANT

Simple item page
dc.contributor.advisorMartins, Nuno Cen_US
dc.contributor.authorGefen, Yonatanen_US
dc.contributor.departmentElectrical Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2014-10-17T05:33:49Z
dc.date.available2014-10-17T05:33:49Z
dc.date.issued2014en_US
dc.description.abstractThis thesis develops a low-cost grid-based path planner that intrinsically supports smooth, curved vehicle dynamics. There are many advantages to grid-based planners, including working natively in the digital space of most sensors, and efficiency in low dimensional space. However, discrete planners create jaggedness in most paths. Further, the dimensionality must be limited for efficiency, usually by limiting vehicle steering angles to a small finite set. The algorithm presented here, Waypoint-A*, extends A* to produce low-cost curved trajectories, taking the dynamics of the vehicle into account explicitly post-planning. Considering the path generated by A* as composed of a set of waypoints, Waypoint-A* calculates the minimum-cost heading on a continuum, to direct the vehicle to the waypoint at the location resulting in the lowest total cost. Smoothness of these curves is invariant to terrain resolution and computation.en_US
dc.identifierhttps://doi.org/10.13016/M2GC8T
dc.identifier.urihttp://hdl.handle.net/1903/15947
dc.language.isoenen_US
dc.subject.pqcontrolledElectrical engineeringen_US
dc.subject.pqcontrolledArtificial intelligenceen_US
dc.subject.pquncontrolledAny-Angleen_US
dc.subject.pquncontrolledDifferential Constraintsen_US
dc.subject.pquncontrolledDiscrete Planneren_US
dc.subject.pquncontrolledNon-Holonomicen_US
dc.subject.pquncontrolledPath Planningen_US
dc.titleAN ADAPTIVELY SAMPLED PATH PLANNER USING WAYPOINTS: AN ANY-ANGLE VARIANTen_US
dc.typeThesisen_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Gefen_umd_0117N_15468.pdf
Size:
2.29 MB
Format:
Adobe Portable Document Format
Download

Collections

UMD Theses and Dissertations
Electrical & Computer Engineering Theses and Dissertations