An RRT-Based path planner for use in trajectory imitation

Abstract

The authors propose a more robust robot programming; by demonstration system planner that produces a reproduction; path which satisfies statistical constraints derived from demonstration; trajectories and avoids obstacles given the freedom; in those constraints. To determine the statistical constraints a; Gaussian Mixture Model is fitted to demonstration trajectories.; These demonstrations are recorded through kinesthetic; teaching of a redundant manipulator. The GMM models the; likelihood of configurations given time. The planner is based; on Rapidly-exploring Random Tree search with the search tree; kept within the statistical model. Collision avoidance is included; by not allowing the tree to grow into obstacles. The system is; designed to act as a backup for if a faster reactive planner falls; within a local minima.; To illustrate its performance an experiment is conducted; where the system is taught to open a Pelican case using a; Barrett Whole Arm Manipulator (WAM). During reproduction; an obstacle is placed nearby the case to partially obstruct; the manipulator. The planner successfully avoided this obstacle; without drifting from the trends in the demonstrations