  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.
