dc.contributor.author | Orhagen, Ole Petter | en_GB |
dc.contributor.author | Thoresen, Marius | en_GB |
dc.contributor.author | Mathiassen, Kim | en_GB |
dc.date.accessioned | 2022-03-22T09:03:10Z | |
dc.date.accessioned | 2022-04-07T06:48:37Z | |
dc.date.available | 2022-03-22T09:03:10Z | |
dc.date.available | 2022-04-07T06:48:37Z | |
dc.date.issued | 2022-03-17 | |
dc.identifier.citation | Orhagen, Thoresen M, Mathiassen K: The Rapidly Exploring Random Tree Funnel Algorithm. In: IEEE .. The 8th International Conference on Mechatronics and Robotics Engineering (ICMRE), 2022. IEEE | en_GB |
dc.identifier.uri | http://hdl.handle.net/20.500.12242/3015 | |
dc.description | Orhagen, Ole Petter; Thoresen, Marius; Mathiassen, Kim.
The Rapidly Exploring Random Tree Funnel Algorithm. I: The 8th International Conference on Mechatronics and Robotics Engineering (ICMRE). IEEE 2022 ISBN 978-1-6654-8377-3. | en_GB |
dc.description.abstract | This paper shows the feasibility of combining robust
motion primitives generated through the Sums Of Squares
programming theory with a discrete Rapidly exploring Random
Tree algorithm. The generated robust motion primitives, referred
to as funnels, are then employed as local motion primitives,
each with its locally valid Linear Quadratic Regulator (LQR)
controller, which is verified through a Lyapunov function found
through a Sum Of Squares (SOS) search in the function space.
These funnels are then combined together at execution time by
the Rapidly-exploring-Random-Tree (RRT) planner, and is shown
to provide provably robust traversal of a simulated forest environment. The experiments benchmark the RRT-Funnel algorithm
against an RRT algorithm which employs a maximum distance
to the nearest obstacle heuristic in order to avoid collisions, as
opposed to explicitly handling uncertainty. The results show that
employing funnels as robust motion primitives outperform the
heuristic planner in the experiments run on both algorithms,
where the RRT-Funnel algorithm does not collide a single time,
and creates shorter solution paths than the benchmark planner
overall, although it takes a significantly longer time to find a
solution. | en_GB |
dc.language.iso | en | en_GB |
dc.subject | Ubemannede bakkekjøretøyer (UGV) | en_GB |
dc.subject | Reguleringsteori | en_GB |
dc.subject | Matematiske modeller | en_GB |
dc.subject | Autonome biler | en_GB |
dc.subject | Autonomi | en_GB |
dc.title | The Rapidly Exploring Random Tree Funnel Algorithm | en_GB |
dc.date.updated | 2022-03-22T09:03:10Z | |
dc.identifier.cristinID | 2011626 | |
dc.identifier.doi | 10.1109/ICMRE54455.2022.9734089 | |
dc.source.isbn | 978-1-6654-8377-3 | |
dc.type.document | Chapter | |