Autonomous Excavation of Rocks Using a Gaussian Process Model and Unscented Kalman Filter
Name
ICRA_2020___Automated_rock_scooping.pdf
Description
Accepted version
Size
1.46 MB
Format
Adobe PDF
Checksum (MD5)
d7a455406c0d1a8c18d1fc5eed3ea8f6
Author(s)
Sotiropoulos, Filippos E.
Asada, Haruhiko
Date Issued
February 2020
Journal
IEEE Robotics and Automation Letters
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Sotiropoulos, Filippos E. and H. Harry Asada. "Autonomous Excavation of Rocks Using a Gaussian Process Model and Unscented Kalman Filter." IEEE Robotics and Automation Letters 5, 2 (April 2020): 2491 - 2497 © 2020 IEEE
Version
Final published version
Abstract
In large-scale open-pit mining and construction works, excavators must deal with large rocks mixed with gravel and granular soil. Capturing and moving large rocks with the bucket of an excavator requires a high level of skill that only experienced human operators possess. In an attempt to develop autonomous rock excavators, this letter presents a control method that predicts the rock movement in response to bucket operation and computes an optimal bucket movement to capture the rock. The process is highly nonlinear and stochastic. A Gaussian process model, which is nonlinear, nonparametric, and stochastic, is used for describing rock behaviors interacting with the bucket and surrounding soil. Experimental data is used directly for identifying the model. An Unscented Kalman Filter (UKF) is then integrated with the Gaussian process model for predicting the rock movements and estimating the length of the rock. A feedback controller that optimizes a cost function is designed based on the rock motion prediction and implemented on a robotic excavator prototype. Experiments demonstrate encouraging results towards autonomous mining and rock excavation.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/lra.2020.2972891
