A Peg-in-hole Task Strategy for Holes in Concrete

André Yuji Yasutomi, Hiroki Mori, Tetsuya Ogata

研究成果: Conference contribution

7 被引用数 (Scopus)


A method that enables an industrial robot to accomplish the peg-in-hole task for holes in concrete is proposed. The proposed method involves slightly detaching the peg from the wall, when moving between search positions, to avoid the negative influence of the concrete's high friction coefficient. It uses a deep neural network (DNN), trained via reinforcement learning, to effectively find holes with variable shape and surface finish (due to the brittle nature of concrete) without analytical modeling or control parameter tuning. The method uses displacement of the peg toward the wall surface, in addition to force and torque, as one of the inputs of the DNN. Since the displacement increases as the peg gets closer to the hole (due to the chamfered shape of holes in concrete), it is a useful parameter for inputting in the DNN. The proposed method was evaluated by training the DNN on a hole 500 times and attempting to find 12 unknown holes. The results of the evaluation show the DNN enabled a robot to find the unknown holes with average success rate of 96.1% and average execution time of 12.5 seconds. Additional evaluations with random initial positions and a different type of peg demonstrate the trained DNN can generalize well to different conditions. Analyses of the influence of the peg displacement input showed the success rate of the DNN is increased by utilizing this parameter. These results validate the proposed method in terms of its effectiveness and applicability to the construction industry.

ホスト出版物のタイトル2021 IEEE International Conference on Robotics and Automation, ICRA 2021
出版社Institute of Electrical and Electronics Engineers Inc.
出版ステータスPublished - 2021
イベント2021 IEEE International Conference on Robotics and Automation, ICRA 2021 - Xi'an, China
継続期間: 2021 5月 302021 6月 5


名前Proceedings - IEEE International Conference on Robotics and Automation


Conference2021 IEEE International Conference on Robotics and Automation, ICRA 2021

ASJC Scopus subject areas

  • ソフトウェア
  • 制御およびシステム工学
  • 人工知能
  • 電子工学および電気工学


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