Intelligent drill boom hole-seeking is a promising technology for enhancing drilling efficiency, mitigating potential safety hazards, and relieving human operators. Most existing intelligent drill boom control methods rely on a hierarchical control framework based on inverse kinematics. However, these methods are generally time-consuming due to the computational complexity of inverse kinematics and the inefficiency of the sequential execution of multiple joints. To tackle these challenges, this study proposes an integrated drill boom control method based on Reinforcement Learning (RL). We develop an integrated drill boom control framework that utilizes a parameterized policy to directly generate control inputs for all joints at each time step, taking advantage of joint posture and target hole information. By formulating the hole-seeking task as a Markov decision process, contemporary mainstream RL algorithms can be directly employed to learn a hole-seeking policy, thus eliminating the need for inverse kinematics solutions and promoting cooperative multi-joint control. To enhance the drilling accuracy throughout the entire drilling process, we devise a state representation that combines Denavit-Hartenberg joint information and preview hole-seeking discrepancy data. Simulation results show that the proposed method significantly outperforms traditional methods in terms of hole-seeking accuracy and time efficiency.

翻译：智能钻臂寻孔是一项有前景的技术，旨在提升钻孔效率、降低潜在安全风险并减轻操作人员负担。现有的大多数智能钻臂控制方法依赖于基于逆运动学的分层控制框架。然而，由于逆运动学的计算复杂性以及多关节顺序执行的低效性，这些方法通常耗时较长。为应对这些挑战，本研究提出了一种基于强化学习的集成式钻臂控制方法。我们开发了一个集成式钻臂控制框架，该框架利用参数化策略在每个时间步直接生成所有关节的控制输入，并充分利用关节姿态与目标孔位信息。通过将寻孔任务建模为马尔可夫决策过程，可直接采用当前主流的强化学习算法学习寻孔策略，从而无需逆运动学求解，并促进多关节协同控制。为提升整个钻孔过程的精度，我们设计了一种融合德纳维特-哈滕伯格关节信息与预览寻孔偏差数据的状态表示。仿真结果表明，本方法在寻孔精度与时间效率上显著优于传统方法。