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.

翻译：提出了一种使工业机器人能够完成混凝土孔洞的轴孔装配任务的方法。该方法在搜索位置之间移动时，将销钉略微脱离墙面，以避免混凝土高摩擦系数的负面影响。它使用通过强化学习训练的深度神经网络，有效找到形状和表面光洁度变化（由于混凝土的脆性）的孔洞，无需分析建模或控制参数调整。该方法除了力和扭矩外，还将销钉向墙面方向的位移作为深度神经网络的输入之一。由于销钉靠近孔洞时位移会增加（得益于混凝土孔洞的倒角形状），因此它是输入深度神经网络的有用参数。通过在单个孔洞上训练深度神经网络500次并尝试查找12个未知孔洞来评估所提出的方法。评估结果显示，该深度神经网络使机器人能够以96.1%的平均成功率和12.5秒的平均执行时间找到未知孔洞。使用随机初始位置和不同类型的销钉进行的额外评估表明，训练后的深度神经网络能够很好地泛化到不同条件。对销钉位移输入影响的分析显示，利用该参数提高了深度神经网络的成功率。这些结果验证了所提出方法在建筑行业的有效性和适用性。