Kinodynamic Steering using Supervised Learning in RRT

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.