基于惯性传感组件和 BP 神经网络的 防冲钻孔机器人钻具姿态解算

钻孔卸压是高地应力矿井治理冲击地压的首要措施，对实施钻孔作业的防冲钻孔机器人钻具姿态准确测量是保障钻孔位置及卸压效果的前提。为此，本文提出了基于惯性传感组件和BP神经网络的防冲钻孔机器人钻具姿态解算方法，通过设计惯性传感组件的空间阵列式布局方式(空间阵列式IMU),建立了空间阵列式IMU的数据融合模型及位姿解算模型，实现了钻具姿态的高精度解算。在此基础上，提出了基于BP神经网络的惯性传感组件误差补偿方法，建立了钻具姿态解算误差补偿模型，并通过钻具模拟运动的解算分析对空间阵列式IMU解算和误差补偿方法的可行性进行了验证。最后，通过搭建的防冲钻孔机器人钻具姿态监测实验平台，对不同方法的钻具解算结果进行对比分析。实验结果表明，在BP神经网络模型进行误差补偿后，本文所提方法解算出的钻具姿态精度明显提高，钻具方位角、倾角和横滚角的平均误差分别为0.099°、0.079°和0.045°,有效抑制了惯性传感组件的漂移和误差积累，且钻具姿态解算误差曲线没有出现发散现象。因此，该方法可以持续稳定地对防冲钻孔机器人钻具姿态进行可靠监测，具有较高的推广应用价值。

Drilling tool attitude calculation of drilling robot for rockburst prevention based on inertial sensing assembly and BP neural network

Borehole pressure relief is the primary measure to control rock bursts in high in-situ stress mines. Accurate measurement of drilling tool attitude of the drilling robot for rockburst prevention is the premise to ensure the drilling hole position and pressure relief effect. Therefore, this article proposes a drilling tool attitude calculation method based on inertial sensing assembly and BP neural network. By designing the spatial array layout of inertial sensing assembly (spatial array IMU), the data fusion model and the attitude calculation model of spatial array IMU are formulated, which could realize the high-precision calculation results of drilling tool attitude. On this basis, the error compensation method of inertial sensing units based on the BP neural network is proposed and the error compensation model of drilling tool attitude calculation is established. The feasibility of spatial array IMUs calculation and error compensation method is evaluated by analyzing the drilling tool simulation motion. Finally, the drilling tool attitude monitoring experimental platform of the drilling robot is established to compare and analyze the drilling tool calculation results of different methods. Experimental results show that after the error compensation of the BP neural network model, the attitude accuracy of the drilling tool calculated by the proposed method is significantly improved, and the average errors of azimuth, inclination, and roll angle are 0. 099°, 0. 079°, and 0. 045°, respectively. The compensation measures effectively restrain the drift and error accumulation of inertial sensing units, and there is no divergence in the error curve of drilling tool attitude calculation. Therefore, this method can continuously and reliably monitor the drilling tool attitude of drilling robot for rockburst prevention s and has high popularization and application value.