Space-Time Functional Gradient Optimization for Motion Planning - Robotics Institute Carnegie Mellon University

Space-Time Functional Gradient Optimization for Motion Planning

Arunkumar Byravan, Byron Boots, Siddhartha Srinivasa, and Dieter Fox
Conference Paper, Proceedings of (ICRA) International Conference on Robotics and Automation, pp. 6499 - 6506, May, 2014

Abstract

Functional gradient algorithms (e.g. CHOMP) have recently shown great promise for producing locally optimal motion for complex many degree-of-freedom robots. A key limitation of such algorithms is the difficulty in incorporating constraints and cost functions that explicitly depend on time. We present T-CHOMP, a functional gradient algorithm that overcomes this limitation by directly optimizing in space-time. We outline a framework for joint space-time optimization, derive an efficient trajectory-wide update for maintaining time monotonicity, and demonstrate the significance of T-CHOMP over CHOMP in several scenarios. By manipulating time, T- CHOMP produces lower-cost trajectories leading to behavior that is meaningfully different from CHOMP.

BibTeX

@conference{Byravan-2014-7867,
author = {Arunkumar Byravan and Byron Boots and Siddhartha Srinivasa and Dieter Fox},
title = {Space-Time Functional Gradient Optimization for Motion Planning},
booktitle = {Proceedings of (ICRA) International Conference on Robotics and Automation},
year = {2014},
month = {May},
pages = {6499 - 6506},
}