Expanding Constrained Kinodynamic Path Planning Solutions through Recurrent Neural Networks

Simple item page
dc.contributor.advisorXu, Huanen_US
dc.contributor.authorShaffer, Joshua Allenen_US
dc.contributor.departmentAerospace Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2019-06-20T05:38:52Z
dc.date.available2019-06-20T05:38:52Z
dc.date.issued2019en_US
dc.description.abstractPath planning for autonomous systems with the inclusion of environment and kinematic/dynamic constraints encompasses a broad range of methodologies, often providing trade-offs between computation speed and variety/types of constraints satisfied. Therefore, an approach that can incorporate full kinematics/dynamics and environment constraints alongside greater computation speeds is of great interest. This thesis explores a methodology for using a slower-speed, robust kinematic/dynamic path planner for generating state path solutions, from which a recurrent neural network is trained upon. This path planning recurrent neural network is then used to generate state paths that a path-tracking controller can follow, trending the desired optimal solution. Improvements are made to the use of a kinodynamic rapidly-exploring random tree and a whole-path reinforcement training scheme for use in the methodology. Applications to 3 scenarios, including obstacle avoidance with 2D dynamics, 10-agent synchronized rendezvous with 2D dynamics, and a fully actuated double pendulum, illustrate the desired performance of the methodology while also pointing out the need for stronger training and amounts of training data. Last, a bounded set propagation algorithm is improved to provide the initial steps for formally verifying state paths produced by the path planning recurrent neural network.en_US
dc.identifierhttps://doi.org/10.13016/dgob-oe6g
dc.identifier.urihttp://hdl.handle.net/1903/22019
dc.language.isoenen_US
dc.subject.pqcontrolledEngineeringen_US
dc.subject.pqcontrolledRoboticsen_US
dc.subject.pqcontrolledAerospace engineeringen_US
dc.subject.pquncontrolledMachine learningen_US
dc.subject.pquncontrolledMotion/trajectory planningen_US
dc.subject.pquncontrolledNeural networksen_US
dc.subject.pquncontrolledOptimizationen_US
dc.subject.pquncontrolledPath planningen_US
dc.subject.pquncontrolledReinforcement learningen_US
dc.titleExpanding Constrained Kinodynamic Path Planning Solutions through Recurrent Neural Networksen_US
dc.typeThesisen_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Shaffer_umd_0117N_19941.pdf
Size:
1.79 MB
Format:
Adobe Portable Document Format
Download

Collections

UMD Theses and Dissertations
Aerospace Engineering Theses and Dissertations