Show simple item record

dc.creatorMedina Vargas, Jose Andres
dc.date.accessioned2016-09-05T14:31:02Z
dc.date.available2016-09-05T14:31:02Z
dc.date.created2014-05
dc.date.issued2013-09-26
dc.date.submittedMay 2014
dc.identifier.urihttp://hdl.handle.net/1969.1/157594
dc.description.abstractMotion planning (MP) is the problem of finding a valid path (e.g., collision free) from a start to a goal state for a movable object. MP is a complex problem with a myriad of applications, ranging from robotics, to computer-aided design, to computational biology. Sampling-based planning deals with MP’s complexity by constructing a graph which approximates the planning space. Different sampling based planners have been developed to tackle specific scenarios, but none of these is best for every scenario, e.g., cluttered vs. free space vs narrow passage. Thus, adaptive methods were created to combine different samplers effectively to solve more complex and heterogeneous environments. Adaptive methods have been proposed that learn the best sampler for the entire space or that partition the space into simple and discrete region types, which are suited for particular samplers. These methods do not solve the problem of environments containing multiple complex areas that are difficult to automatically partition. In this thesis, we propose an alternative approach using neural networks to create an adaptive method that does not require regions. We replace the concept of regions with a visibility distribution, how “free” a node is, allowing our method to work for a wider range of interesting problems. Experiments show significant improvement in speed compared to methods that attempt to use a single sampler for a complex environment.en
dc.format.mimetypeapplication/pdf
dc.subjectmotionen
dc.subjectplanningen
dc.subjectneuralen
dc.subjectnetworken
dc.subjectnetworksen
dc.subjectalgorithmsen
dc.subjectAIen
dc.subjectartificialen
dc.subjectintelligenceen
dc.subjectparasolen
dc.subjectroboticsen
dc.titleNeural Network Approach to Feature Sensitive Motion Planningen
dc.typeThesisen
thesis.degree.departmentComputer Science and Engineeringen
thesis.degree.disciplineComputer Sci. & Engren
thesis.degree.grantorUndergraduate Research Scholars Programen
dc.contributor.committeeMemberAmato, Nancy M
dc.type.materialtexten
dc.date.updated2016-09-05T14:31:02Z


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record