Print Email Facebook Twitter Kinodynamic Steering using Supervised Learning in RRT Title Kinodynamic Steering using Supervised Learning in RRT Author Moring, Stefan (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Delft Center for Systems and Control) Contributor Wisse, M. (mentor) Bharatheesha, M. (mentor) Spaan, M.T.J. (graduation committee) Alonso Mora, J. (graduation committee) Moerland, T.M. (graduation committee) Degree granting institution Delft University of Technology Programme Electrical Engineering | Signals and Systems Date 2018-01-12 Abstract With the need for robots to operate autonomously increasing more and more, the research field of motion planning is becoming more active. Usually planning is done in configuration space, which often leads to non feasible solutions for highly dynamical or underactuated systems. With kinodynamic planning motion can also be planned for this difficult class of systems. However, due to the difficult nature of the problem, computation time is an issue. RRT CoLearn is a novel variant on the original RRT algorithm that tries to decrease computation time by replacing computational heavy steps in the algorithm with supervised learning. In this thesis the performance of RRT CoLearn is investigated, and it is found that it does not work on multi-DOF systems. Furthermore a novel steering function is presented called Inverse Dynamics Learning, which is shown to converge over five times faster than RRT CoLearn and also converge on a highly non-linear 2-DOF system. Subject Motion PlanningRRTKinodynamicSupervised Learning To reference this document use: http://resolver.tudelft.nl/uuid:fdf13674-f2b5-4b4a-b03d-21299114642a Part of collection Student theses Document type master thesis Rights © 2018 Stefan Moring Files PDF mscThesis.pdf 3.28 MB Close viewer /islandora/object/uuid:fdf13674-f2b5-4b4a-b03d-21299114642a/datastream/OBJ/view