Overview

The powder grinding process is a necessary step for material synthesis in materials science experiments. In material science, powder grinding is a time-consuming process that is typically executed by hand, as commercial grinding machines are unsuitable for samples of small size. Robotic powder grinding would solve this problem, but it is a challenging task for robots, as it requires observing the powder state and generating appropriate motions. Our previous study proposed a robotic powder grinding system using visual feedback. Although visual feedback is helpful for observing the powder distribution, the particle size during the grinding process remains invisible, leading to suboptimal robot actions. In some cases, the robot chose to gather the powder even though continuing to grind instead would have produced finer powder. In this paper, we present a multi-modal robotic grinding system that utilizes both audio and visual feedback. It makes use of the grinding sound which carries information about the grinding progress, as the particle size strongly affects the audio intensity. The audio feedback enables the robot to grind until the powder is sufficiently fine. In our experiments, the robot ground 80.5% of the powder to a particle size smaller than 250 micrometer with audio and visual feedback and 68% without audio feedback, indicating that multi-modal feedback is an effective tool to produce finer powder. We conclude that the addition of audio feedback provides crucial information to the robot, allowing it to better understand the progress of the grinding process and make more optimal decisions. This robot system can be used to prepare samples in material science experiments and analyze the grinding process.

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Citation

@inproceedings{nakajima2023robotic,
  title={Robotic Powder Grinding with Audio-Visual Feedback for Laboratory Automation in Materials Science},
  author={Nakajima, Yusaku and Hamaya, Masashi and Tanaka, Kazutoshi and Hawai, Takafumi and Yasuo, Takeuchi and von Drigalski, Felix and Ushiku, Yoshitaka and Ono, Kanta},
  booktitle={The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2023}
}