Sampling Recovery for Closed Loop Rapidly Expanding Random Tree using Brake Profile Regeneration
2015 (English)In: Intelligent Vehicles Symposium (IV), 2015 IEEE, IEEE , 2015, 101-106 p.Conference paper (Refereed)
In this paper an extension to the sampling based motion planning framework CL-RRT is presented. The framework uses a system model and a stabilizing controller to sample the perceived environment and build a tree of possible trajectories that are evaluated for execution. Complex system models and constraints are easily handled by a forward simulation making the framework widely applicable. To increase operational safety we propose a sampling recovery scheme that performs a deterministic brake profile regeneration using collision information from the forward simulation. This greatly increases the number of safe trajectories and also reduces the number of samples that produce infeasible results. We apply the framework to a Scania G480 mining truck and evaluate the algorithm in a simple yet challenging obstacle course and show that our approach greatly increases the number of feasible paths available for execution.
Place, publisher, year, edition, pages
IEEE , 2015. 101-106 p.
, IEEE Intelligent Vehicles Symposium, ISSN 1931-0587
RRT, Autonomous vehicles, motion planning
IdentifiersURN: urn:nbn:se:liu:diva-120929DOI: 10.1109/IVS.2015.7225670ISI: 000380565800018ISBN: 9781467372664OAI: oai:DiVA.org:liu-120929DiVA: diva2:849916
2015 IEEE Intelligent Vehicles Symposium (IV), June 28 - July 1, 2015. COEX, Seoul, Korea