Inverse kinematics solution of an unsupervised learning drilling boom

Intelligent control of the hole-seeking process of a rock drilling rig boom is crucial for enhancing the accuracy and efficiency of rock drilling operations. The inverse kinematics solution (IKS) is the core of achieving precise and rapid hole-seeking control of the boom. However, existing analytical and numerical techniques are inadequate in fulfilling the accuracy and time efficiency requirements for IKS in complex drilling boom scenarios. Conventional neural network (NN) approaches heavily depend on labeled data comprising target borehole sets derived from the activities of each joint and forward kinematics. The distribution of drill endpoints generated by this data cannot cover the entire workspace, resulting in the low reliability of the solution. To overcome these challenges, this study introduces an unsupervised learning-based NN method for IKS emphasizing safety constraints. This innovative method differs from traditional approaches in that it does not depend on labeled data. Instead, it utilizes the desired end position of the drilling boom as the network input. The network generates an eight-dimensional joint vector, and the actual drill end pose is derived through forward kinematics calculations on this vector. Then, the difference between the actual and desired drill end poses is used as the optimization objective, driving the network updates through gradient descent. The advantage of this method lies in eliminating the need for complex joint label data required in supervised learning IKS and using the differences in drill end poses directly as optimization objectives, which helps improve the accuracy of IKS. Meanwhile, a critical innovation of this study is integrating a safety collision penalty into the objective function of the solution, ensuring that the network’s output for joint positions adheres to specific environmental limitations. If the actual distance falls below the safety threshold, penalty terms are incorporated into the objective function, allowing for the adjustment of weights to maintain a balance between the precision demands of hole drilling and the design requirements of the safety constraints. This method improves the accuracy of IKS and significantly reduces its collision rate. Experimental results reveal that the mean hole-seeking error obtained using this unsupervised learning-based method achieves a mean hole-seeking error of 5–7 mm in IKS, a significant improvement of approximately 70.72% over supervised learning methods. Moreover, introducing safety constraints has successfully reduced the collision rate in IKS solutions by 90.28% without sacrificing the accuracy of the solutions. © 2024 Science Press. All rights reserved.