Randomized statistical path planning

This paper explores the use of statical learning methods on randomized path planning algorithms. A continuous, randomized version of A* is presented along with an empirical analysis showing planning time convergence rates in the robotic manipulation domain. The algorithm relies on several heuristics that capture a manipulator's kinematic feasibility and the local environment. A statistical framework is used to learn one of these heuristics from a large amount of training data saving the need to manually tweak parameters every time the problem changes. Using the appropriate formulation, we show that motion primitives can be automatically extracted from the training data in order to boost planning performance. Furthermore, we propose a randomized statistical path planning (RSPP) paradigm that outlines how a planner using heuristics should take advantage of machine learning algorithms. Planning results are shown for several manipulation problems tested in simulation.