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||Wilson, A. D., J. A. Schultz, A. Ansari, and T. D. Murphey|
|Conference Name||IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)|
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.