A Data-Driven Approach to Prediction and Optimal Bucket-Filling Control for Autonomous Excavators
Name
Data_Driven_Approach_to_Bucket_Filling_Control_SANDZIMIER_FINAL.pdf
Description
Accepted version
Size
1.45 MB
Format
Adobe PDF
Checksum (MD5)
474ae489b38f99af5021cd91391588d2
Author(s)
Sandzimier, Ryan Joseph.
Asada, Haruhiko
Date Issued
January 2020
Journal
IEEE Robotics and Automation Letters
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Sandzimier, Ryan J. and H. Harry Asada. "A Data-Driven Approach to Prediction and Optimal Bucket-Filling Control for Autonomous Excavators." IEEE Robotics and Automation Letters 5, 2 (April 2020): 2682 - 2689 © 2016 IEEE
Version
Author's final manuscript
Abstract
We develop a data-driven, statistical control method for autonomous excavators. Interactions between soil and an excavator bucket are highly complex and nonlinear, making traditional physical modeling difficult to use for real-time control. Here, we propose a data-driven method, exploiting data obtained from laboratory tests. We use the data to construct a nonlinear, non-parametric statistical model for predicting the behavior of soil scooped by an excavator bucket. The prediction model is built for controlling the amount of soil collected with a bucket. An excavator collects soil by dragging the bucket along the soil surface and scooping the soil by rotating the bucket. It is important to switch from the drag phase to the scoop phase with the correct timing to ensure an appropriate amount of soil has accumulated in front of the bucket. We model the process as a heteroscedastic Gaussian process (GP) based on the observation that the variance of the collected soil mass depends on the scooping trajectory, i.e., the input, as well as the shape of the soil surface immediately prior to scooping. We develop an optimal control algorithm for switching from the drag phase to the scoop phase at an appropriate time and for generating a scoop trajectory to capture a desired amount of soil with high confidence. We implement the method on a robotic excavator and collect experimental data. Experiments show promising results in terms of being able to achieve a desired bucket fill factor with low variance.
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.2020.2969944
