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JRM Vol.36 No.2 pp. 263-272
doi: 10.20965/jrm.2024.p0263
(2024)

Paper:

Development of a Highly Efficient Trajectory Planning Algorithm in Backfilling Task for Autonomous Excavators by Imitation of Experts and Numerical Optimization

Ryuji Tsuzuki, Kosuke Hara, and Dotaro Usui

Robotics Technology Department, Technology Research Center, Sumitomo Heavy Industries, Ltd.
19 Natsushima-cho, Yokosuka, Kanagawa 237-8555, Japan

Received:
October 5, 2023
Accepted:
February 8, 2024
Published:
April 20, 2024
Keywords:
autonomous excavator, imitation learning, trajectory planning, numerical optimization, hierarchical network
Abstract

The objective of this study is to achieve high efficiency in autonomous hydraulic excavators by imitating the bucket trajectory operated by an expert. For this purpose, bucket trajectories of experts were collected, and a trajectory was planned using machine learning of a model that relates measured soil shapes to the bucket trajectories of the experts. In this study, we proposed a hierarchical model consisting of a model for estimating movement and a trajectory, with a focus on the fact that different trajectories are generated for the same soil shape as a result of the analysis of the skilled persons’ movements. The trajectory output from the model was replanned to have a smooth trajectory using numerical optimization. For the backfilling task, the error from the target shape and the amount of soil transported per movement were compared with those of an expert. The proposed method increased the error from the target shape by approximately 66%, while the amount of soil transported was approximately 58% of that of the experts.

Hierarchical imitation network

Hierarchical imitation network

Cite this article as:
R. Tsuzuki, K. Hara, and D. Usui, “Development of a Highly Efficient Trajectory Planning Algorithm in Backfilling Task for Autonomous Excavators by Imitation of Experts and Numerical Optimization,” J. Robot. Mechatron., Vol.36 No.2, pp. 263-272, 2024.
Data files:
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Last updated on May. 19, 2024