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An Asymptotically Optimal Path Planning Method with Cubic Bézier Spline

dc.contributor.authorFei, Zifan
dc.contributor.copyright-releaseNot Applicableen_US
dc.contributor.degreeMaster of Applied Scienceen_US
dc.contributor.departmentDepartment of Mechanical Engineeringen_US
dc.contributor.ethics-approvalNot Applicableen_US
dc.contributor.external-examinern/aen_US
dc.contributor.graduate-coordinatorRobert Baueren_US
dc.contributor.manuscriptsNot Applicableen_US
dc.contributor.thesis-readerClifton Johnstonen_US
dc.contributor.thesis-readerYuan Maen_US
dc.contributor.thesis-supervisorYa-Jun Panen_US
dc.date.accessioned2023-07-31T16:59:29Z
dc.date.available2023-07-31T16:59:29Z
dc.date.defence2023-07-19
dc.date.issued2023-07-28
dc.description.abstractThis dissertation introduces a novel path planning algorithm for robotics, known as Informed SRRT#. Our algorithm integrates a local planner from SRRT, accommodating both external and internal constraints. We introduce two extra lines at the Bézier spline's endpoints, which facilitates the rewiring process. A minimum of three state connections need adjustment during rewiring to meet kinematic constraints. The effectiveness of the proposed method is demonstrated through various channels: Python-based simulations, Gazebo/Rviz --- a robot simulator and visualization tool in Robot Operating System, and real-world scenarios. In real-world experiments, the algorithm successfully maneuvered TurtleBot3 past obstacles in the physical map, leading to a smooth, streamlined and optimal navigation approach. Our results reveal that the new algorithm identifies shorter paths than SRRT while achieving the same number of node sampling iterations. However, these enhancements come with a trade-off, as the computational time of this method is slightly higher compared to traditional methods.en_US
dc.identifier.urihttp://hdl.handle.net/10222/82745
dc.language.isoenen_US
dc.subjectRoboticsen_US
dc.titleAn Asymptotically Optimal Path Planning Method with Cubic Bézier Splineen_US

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