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.

为了避免混凝土高摩擦系数的负面影响，本文提出了一种使工业机器人能够完成混凝土孔洞中的桩入洞任务的方法。该方法利用经过强化学习训练的深度神经网络来有效地找到具有不同形状和表面处理的孔洞，而无需进行分析建模或控制参数调整。经过评估，结果显示该神经网络具有 96.1％的平均成功率和 12.5 秒的平均执行时间，验证了该方法在建筑行业的有效性和适用性。