Gaussian Process Motion planning
Mustafa Mukadam, Xinyan Yan, Byron Boots
Abstract
Motion planning is a fundamental tool in robotics, used to generate collision-free, smooth, trajectories, while satisfying task-dependent constraints. In this paper, we present a novel approach to motion planning using Gaussian processes. In contrast to most existing trajectory optimization algorithms, which rely on a discrete state parameterization in practice, we represent the continuous-time trajectory as a sample from a Gaussian process (GP) generated by a linear time-varying stochastic differential equation. We then provide a gradient-based optimization technique that optimizes continuous-time trajectories with respect to a cost functional. By exploiting GP interpolation, we develop the Gaussian Process Motion Planner (GPMP), that finds optimal trajectories parameterized by a small number of states. We benchmark our algorithm against recent trajectory optimization algorithms by solving 7-DOF robotic arm planning problems in simulation and validate our approach on a real 7-DOF WAM arm.
BibTeX
@inproceedings{icra2016_gaussianprocessm,
title = {Gaussian Process Motion planning},
author = {Mustafa Mukadam and Xinyan Yan and Byron Boots},
booktitle = {ICRA 2016},
year = {2016}
}