## 1 Introduction  
In the process of tunnel construction, workers often need to face an extreme construction environment, which brings a great challenge for the traditional tunnel construction methods, but also provides a broad practice space for intelligent construction and construction in tunnel engineering [1]. Meeting the complex requirements of desired tasks involves addressing the critical issue of collision avoidance for rock drilling robotic arms in intelligent construction processes. The issue of real-time collision avoidance path planning for a rock drilling robotic arm to evade two dynamic obstacles needs to be solved. How to ensure the real-time aspect of collision avoidance planning while also making sure that the planned actions allow the robotic arm to reach the target position, not just to avoid obstacles, is the main problem to be resolved. The drilling robotic arm possesses seven degrees of freedom (DOF), and is computationally very expensive with higher DOFs and obstacles [2]. Existing algorithms mainly focus on planning a new collision-free path applications [3], which is not appropriate for real-time collision avoidance. However, the artificial potential field method and deep learning approaches are considered to solve this problem. The artificial potential field (APF) method, a local algorithm, can effectively generate full-body collision avoidance actions. Deep reinforcement learning (DRL) emerges as a promising approach, with its trained action models capable of global optima identification. But, each of these methods has a problem with real-time collision avoidance planning:  
1. The artificial potential field has the problem of falling into local minima [4]. 2 DRL training is inefficient because of the need to avoid multiple obstacles. To actualize collision avoidance, a myriad of studies have been conducted, among which the research on artificial potential field (APF) emerges as a significant direction [5, 6, 7, 8, 9]. To solve the collision avoidance problem of the end-effector, it converts the distance between the end-effector of the robotic arm and the obstacle into a repulsive force [10, 11]. Because in more scenarios it is necessary to ensure that the whole body of the robotic arm will not collide with the obstacle, the whole-body collision avoidance of the robotic arm has been considered in some studies. [PERSON] et al. [12] estimate the distance between the obstacle and the robot skeleton in real time using deep visual perception, thereby establishing a multi-joint repulsive force model. Additionally, there is also some research on robots without visual recognition. Control points are set on the robotic arm, and full-body collision avoidance is performed by calculating the repulsive force from the control point to the obstacle, and local minima are avoided by virtual obstacles [13, 14]. However, the method of avoiding local minima through virtual obstacles requires entering the local minima position first, and after determining the position as local minima, setting the virtual obstacle to make the robotic arm leave the current position. This method is very inefficient for real-time motion planning. Generally speaking, there is almost no artificial potential field-based method that can provide real-time path planning to avoid local minimum positions. To address the above problem reinforcement learning provides a new solution to this problem due to its global planning capability. In the study conducted by [PERSON] et al. [15], a Deep Deterministic Policy Gradient (DDPG) algorithm is used for collision avoidance training for a three-degree-of-freedom robotic arm to solve the problem of collision of the robotic arm under static obstacles. [PERSON] et al.