Real-time Trajectory Synthesis for Information Maximization using Sequential Action Control and Least-Squares Estimation
| Title: | Real-time Trajectory Synthesis for Information Maximization using Sequential Action Control and Least-Squares Estimation |
|---|---|
| Publication Type: | Conference Paper |
| Year of Publication: | 2015 |
| Authors: | A. D. Wilson, J. A. Schultz, A. Ansari, and T. D. Murphey |
| Conference Name: | IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) |
| Pages: | 4935-4940 |
| DOI: | 10.1109/IROS.2015.7354071 |
| 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.\ |
PDF: awilson_IROS2015.pdf
Videos: awilson_IROS2015.mp4
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