JRM Vol.36 No.2 pp. 273-283
doi: 10.20965/jrm.2024.p0273


Bayesian Optimization for Digging Control of Wheel-Loader Using Robot Manipulator

Motoki Koyama*,**, Hiroaki Muranaka*, Masato Ishikawa*, and Yuki Takagi*

*Department of Mechanical Engineering, Osaka University
2-1 Yamadaoka, Suita, Osaka 565-0871, Japan

**Manufacturing Engineering Development Center, Komatsu Ltd.
3-1-1 Ueno, Hirakata, Osaka 573-1011, Japan

October 15, 2023
February 9, 2024
April 20, 2024
Bayesian optimization, wheel-loader, excavation robot

Wheel loaders are construction machines that are mainly used for excavating and loading sedimented ground into dump trucks. The objects to be excavated range from large materials, such as blast rock and crushed stone, to small materials, such as gravel, slag, and coal ash. Therefore, the excavation operation of wheel loaders requires a high skill level to cope with various terrains and soil types. As worker numbers at quarry sites decline, developing highly automated technology to replace operators is crucial. In particular, the geometry of the ground to be excavated by the wheel loader changes with each excavation, so the control parameters must be adapted sequentially during automated excavation. In this study, we proposed an online learning method using Bayesian optimization to search for control parameters from multiple trials and modify them sequentially. In particular, we formulate a multi-objective optimization problem maximizing a weighted linear combination of the payload and workload as an objective function. To validate the proposed method, we constructed an environment in which repeated digging tests can be performed using a robot manipulator with a bucket attached. Through comparative tests between feed-forward control, in which the robot moves along a fixed trajectory independent of the digging reaction force, and off-line control, in which the robot modifies the digging trajectory in response to the digging reaction force, we compared the ability of these methods to cope with terrain volume that is different from that of the optimization trial.

Algorithm overview of Bayesian optimization

Algorithm overview of Bayesian optimization

Cite this article as:
M. Koyama, H. Muranaka, M. Ishikawa, and Y. Takagi, “Bayesian Optimization for Digging Control of Wheel-Loader Using Robot Manipulator,” J. Robot. Mechatron., Vol.36 No.2, pp. 273-283, 2024.
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Last updated on May. 19, 2024