Intelligent path planning for drilling arms in rock bolting in coal mines based on an improved RRT algorithm

Authors

• ZHANG Xuhui, School of Mechanical Enginerring, Xi’an University of Science and and Technology, Xi’an 710054, China;Shaanxi Key Laboratory of Intelligent Detection and Control for Mining Electromechanical Equipment, Xi’an 710054, ChinaFollow
• XIE Yanbin, School of Mechanical Enginerring, Xi’an University of Science and and Technology, Xi’an 710054, China
• LEI Mengyu, School of Mechanical Enginerring, Xi’an University of Science and and Technology, Xi’an 710054, China;Shaanxi Key Laboratory of Intelligent Detection and Control for Mining Electromechanical Equipment, Xi’an 710054, China
• SONG Chengzhi, School of Mechanical Enginerring, Xi’an University of Science and and Technology, Xi’an 710054, China
• DONG Zheng, School of Mechanical Enginerring, Xi’an University of Science and and Technology, Xi’an 710054, China
• WANG Zeyao, School of Mechanical Enginerring, Xi’an University of Science and and Technology, Xi’an 710054, China
• WANG Tenghui, School of Mechanical Enginerring, Xi’an University of Science and and Technology, Xi’an 710054, China
• TIAN Sihao, School of Mechanical Enginerring, Xi’an University of Science and and Technology, Xi’an 710054, China
• ZHANG Qinhuan, School of Mechanical Enginerring, Xi’an University of Science and and Technology, Xi’an 710054, China

Abstract

Objective Against the background of intelligent coal mining, automated underground rock bolting has become a core demand in enhancing the tunneling efficiency and safety of roadways. However, the path planning of drilling arms suffers from a low degree of automation, cumbersome posture adjustment, low operational stability, and insufficient drilling accuracy in complex underground environments. Methods To address these issues, this study developed an intelligent path planning method for drilling arms in rock bolting in coal mines based on an improved rapidly-exploring random tree (RRT) algorithm. First, a goal-biased guidance strategy was adopted to narrow the sampling range, and an update list based on collision detection was established for the efficient adaptive adjustment of the step size. Furthermore, an adaptive update strategy for the direction weight was proposed to achieve the flexible control of the sampling direction. Then, a joint constraint model of the drilling arm was constructed to efficiently solve the inverse kinematics of unconventional multi-degree-of-freedom (DOF) manipulators. Meanwhile, the joint constraints were incorporated into the sampling process for the purpose of the real-time restriction and dynamic modification of sampling nodes. Finally, the path quality was optimized through redundant node pruning and path smoothing, and a globally optimal path planning scheme was determined in combination with the joint constraint model. This enabled the drilling rig to remain perpendicular to the roadway roof while also improving the accuracy and operational efficiency of drilling.Results and Conclusion Compared to the RRT algorithm and its variants, the improved RRT algorithm proposed in this study delivered the optimal performance in the 3D path planning tailored for obstacle avoidance, yielding a search time of 0.18 s, a final path length of 338.04 mm, and a success rate of 100%. With the application of joint constraints, the improved RRT algorithm decreased the path length by 19.71% and shortened the search time by 48.89%. The 3D simulation experiments on the path planning reveal that compared to the RSA-RRT algorithm, the improved RRT algorithm reduced the path length by 45.19% and decreased the time consumed by 15.54%. Physical experiments confirm that the proposed algorithm exhibited distances between the drilling arm’s end-effector position and the target anchor hole ranging from 1.1 cm to 2.9 cm and the drilling angular errors of the drilling rig varying between 1.9° and 3.9°. These experimental results demonstrate that the improved RRT algorithm enables stable and reliable drilling accuracy, enjoying superior comprehensive performance and great engineering application value.

Keywords

rock bolting in coal mines, improved rapidly-exploring random tree (RRT) algorithm, joint constraint, inverse kinematics, adaptive adjustment, path planning

DOI

10.12363/issn.1001-1986.26.02.0104

Recommended Citation

ZHANG Xuhui, XIE Yanbin, LEI Mengyu, et al. (2026) "Intelligent path planning for drilling arms in rock bolting in coal mines based on an improved RRT algorithm," Coal Geology & Exploration: Vol. 54: Iss. 6, Article 23.
DOI: 10.12363/issn.1001-1986.26.02.0104
Available at: https://cge.researchcommons.org/journal/vol54/iss6/23

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