A Data-Driven Approach to Prediction and Optimal Bucket-Filling Control for Autonomous Excavators
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 obta...
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2020
|
| Online Access: | https://hdl.handle.net/1721.1/128006 |
| _version_ | 1826202927608365056 |
|---|---|
| author | Sandzimier, Ryan Joseph. Asada, Haruhiko |
| author2 | Massachusetts Institute of Technology. Department of Mechanical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Mechanical Engineering Sandzimier, Ryan Joseph. Asada, Haruhiko |
| author_sort | Sandzimier, Ryan Joseph. |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T12:27:14Z |
| format | Article |
| id | mit-1721.1/128006 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T12:27:14Z |
| publishDate | 2020 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1280062022-09-28T08:03:15Z A Data-Driven Approach to Prediction and Optimal Bucket-Filling Control for Autonomous Excavators Sandzimier, Ryan Joseph. Asada, Haruhiko Massachusetts Institute of Technology. Department of Mechanical Engineering 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. 2020-10-15T16:01:15Z 2020-10-15T16:01:15Z 2020-01 2020-09-21T16:18:59Z Article http://purl.org/eprint/type/JournalArticle 2377-3766 2377-3774 https://hdl.handle.net/1721.1/128006 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 en http://dx.doi.org/10.1109/lra.2020.2969944 IEEE Robotics and Automation Letters Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) Prof. Asada via Elizabeth Soergel |
| spellingShingle | Sandzimier, Ryan Joseph. Asada, Haruhiko A Data-Driven Approach to Prediction and Optimal Bucket-Filling Control for Autonomous Excavators |
| title | A Data-Driven Approach to Prediction and Optimal Bucket-Filling Control for Autonomous Excavators |
| title_full | A Data-Driven Approach to Prediction and Optimal Bucket-Filling Control for Autonomous Excavators |
| title_fullStr | A Data-Driven Approach to Prediction and Optimal Bucket-Filling Control for Autonomous Excavators |
| title_full_unstemmed | A Data-Driven Approach to Prediction and Optimal Bucket-Filling Control for Autonomous Excavators |
| title_short | A Data-Driven Approach to Prediction and Optimal Bucket-Filling Control for Autonomous Excavators |
| title_sort | data driven approach to prediction and optimal bucket filling control for autonomous excavators |
| url | https://hdl.handle.net/1721.1/128006 |
| work_keys_str_mv | AT sandzimierryanjoseph adatadrivenapproachtopredictionandoptimalbucketfillingcontrolforautonomousexcavators AT asadaharuhiko adatadrivenapproachtopredictionandoptimalbucketfillingcontrolforautonomousexcavators AT sandzimierryanjoseph datadrivenapproachtopredictionandoptimalbucketfillingcontrolforautonomousexcavators AT asadaharuhiko datadrivenapproachtopredictionandoptimalbucketfillingcontrolforautonomousexcavators |