Real-time Trajectory Synthesis for Information Maximization using Sequential Action Control and Least-Squares Estimation

TitleReal-time Trajectory Synthesis for Information Maximization using Sequential Action Control and Least-Squares Estimation
Publication TypeConference Paper
Year of Publication2015
AuthorsWilson, A. D., J. A. Schultz, A. Ansari, and T. D. Murphey
Conference NameIEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)
Pages4935-4940
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. 

DOI10.1109/IROS.2015.7354071
Publication PDF: