Autonomous Precision Pouring From Unknown Containers
Monroe Kennedy, Karl Schmeckpeper, Dinesh Thakur, Chenfanfu Jiang, Vijay Kumar, Kostas Daniilidis
Abstract
We autonomously pour from unknown symmetric containers found in a typical wet laboratory for the development of a robot-assisted, rapid experiment preparation system. The robot estimates the pouring container symmetric geometry, then leverages simulated pours as priors for a given fluid to pour precisely and quickly in a single attempt. The fluid is detected in the transparent receiving container by combining weight and vision. The change of volume in the receiver is a function of the geometry of the pouring container, the pouring angle, and rate. To determine the volumetric flow rate, the profile for maximum containable volume for a given angle is estimated along with the time delay of the fluid exiting the container. A trapezoidal trajectory generation algorithm prescribes the desired volumetric flow rate as a function of the estimation accuracy. A hybrid control strategy is then used to attenuate volumetric error. Three methods are compared for estimating the volume-angle profile, and it is shown that a combination of online system identification and leveraged model priors results in reliable performance. The major contributions of this work are a system capable of pouring quickly and precisely from varying symmetric containers in a single attempt with limited priors, and a novel fluid detection method. This system is implemented on the Rethink Robotics Sawyer and KUKA LBR iiwa manipulators.
BibTeX
@inproceedings{ral2019_autonomousprecis,
title = {Autonomous Precision Pouring From Unknown Containers},
author = {Monroe Kennedy and Karl Schmeckpeper and Dinesh Thakur and Chenfanfu Jiang and Vijay Kumar and Kostas Daniilidis},
booktitle = {RA-L 2019},
year = {2019}
}