Real-time trajectory synthesis for information maximization using Sequential Action Control and least-squares estimation
Andrew D. Wilson, Jarvis A. Schultz, Alex R. Ansari, Todd D. Murphey
Abstract
This paper presents the details and experimental results from an implementation of real-time trajectory generation and parameter estimation of a dynamic model using the Baxter Research Robot from Rethink Robotics. Trajectory generation is based on the maximization of Fisher information in real-time and closed-loop using a form of Sequential Action Control. On-line estimation is performed with a least-squares estimator employing a nonlinear state observer model computed with trep, a dynamics simulation package. Baxter is tasked with estimating the length of a string connected to a load suspended from the gripper with a load cell providing the single source of feedback to the estimator. Several trials are presented with varying initial estimates showing convergence to the actual length within a 6 second time-frame.
BibTeX
@inproceedings{iros2015_realtimetrajecto,
title = {Real-time trajectory synthesis for information maximization using Sequential Action Control and least-squares estimation},
author = {Andrew D. Wilson and Jarvis A. Schultz and Alex R. Ansari and Todd D. Murphey},
booktitle = {IROS 2015},
year = {2015}
}