A Constraint and Sampling-Based Approach to Integrated Task and Motion Planning

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dc.contributor.advisorChaudhuri, Swarat
dc.contributor.committeeMemberKavraki, Lydia E
dc.contributor.committeeMemberMcLurkin, James
dc.contributor.committeeMemberVardi, Moshe V
dc.creatorPrabhu, Sailesh Naveena
dc.date.accessioned2016-02-05T22:17:36Z
dc.date.available2016-02-05T22:17:36Z
dc.date.created2014-12
dc.date.issued2014-09-30
dc.date.submittedDecember 2014
dc.date.updated2016-02-05T22:17:36Z
dc.description.abstractThis thesis tackles the Integrated Task and Motion Planning (ITMP) Problem. The ITMP problem extends classical task planning with actions that require a motion plan. The agent seeks a sequence of actions and the necessary motions to achieve the goal. The user partially specifies the task plan by providing the actions' known parameters. An SMT solver, then, discovers values for the unkown parameters that satisfies constraints requiring the task plan to achieve the goal. The SMT solver utilizes an annotated Probabilistic Roadmap (PRM) to query for motion planning information. A sampling algorithm generates the PRM's vertices to permit a mobile manipulator to grasp numerous object configurations. Each iteration samples several base configurations and adds a base configuration to the PRM that increases the object configurations grasped from its vertices. Our results indicate that increasing the samples per iteration improves the probability the SMT solver discovers a satisfying assignment without adversely affecting the resulting task plan.
dc.format.mimetypeapplication/pdf
dc.identifier.citationPrabhu, Sailesh Naveena. "A Constraint and Sampling-Based Approach to Integrated Task and Motion Planning." (2014) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/88438">https://hdl.handle.net/1911/88438</a>.
dc.identifier.urihttps://hdl.handle.net/1911/88438
dc.language.isoeng
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.
dc.subjectIntegrated Task and Motion Planning
dc.subjectplanning from high-level specification
dc.subjectsynthesis
dc.subjectconstraint-based
dc.subjectsampling-based
dc.titleA Constraint and Sampling-Based Approach to Integrated Task and Motion Planning
dc.typeThesis
dc.type.materialText
thesis.degree.departmentComputer Science
thesis.degree.disciplineEngineering
thesis.degree.grantorRice University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science
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