煤矿安全高效生产受掘进设备智能化水平的制约，现阶段煤矿井下巷道永久支护主要依靠人工操作，人工支护效率低、危险性高，严重影响了煤矿井下巷道成型的速度。煤矿井下环境恶劣，巷道掘进智能化面临更高的挑战性，集中体现在巷道锚固支护的自动化控制。本文以钻锚机器人双钻臂为研究对象，提出一种钻锚机器人双钻臂轨迹规划及协同控制方法，有效提高巷道支护效率，具有重要的技术推动和研究意义。

钻锚机器人六自由度钻臂运动学及动力学分析是研究轨迹规划及协同控制的基础。通过对钻臂结构进行分析，采用改进的D-H（Denavit-Hartenberg）法求解出钻臂的运动学参数，推导其运动学正解和逆解方程，然后使用蒙特卡洛（Monte Carlo）随机采样法求解钻锚机器人双钻臂可达工作空间，最后对钻臂进行动力学建模与仿真，为后续轨迹规划及控制奠定基础。

针对锚固过程依赖人工操作性强、智能化程度低等问题，本文提出一种基于改进快速扩展随机树算法（RRT，Rapidly exploring random tree）的钻锚机器人钻臂轨迹规划方法。在煤矿井下高维复杂环境中，利用 RRT 算法实现钻臂自主动态避障及轨迹规划。为解决 RRT 算法运行速度慢的问题，引入人工势场因子，并构建障碍点和目标点的虚拟力场，计算可达工作空间的势场图，提高采样点靠近目标点的速度。采用贪心算法以及三次 B 样条曲线拟合法，去除冗余点并进行光滑路径处理，提高钻臂轨迹质量。仿真表明采用改进 RRT 算法生成路径的效率更高、更加平滑，进而提高支护过程的智能化程度。

为缩短钻锚机器人钻臂锚固时间，提高支护效率，并指导双臂协同作业，研究双钻臂作业分配及孔序规划策略。基于图像视觉伺服控制方法，在闭环控制环节中将视觉图像数据作为钻臂控制系统反馈信息，研究巷道锚固过程中工艺流程、作业分配关系及双臂的耦合程度，建立以时间最优为基础的钻锚机器人双臂协同控制模型，完成钻锚机器人自动钻孔、找孔，最终实现钻锚机器人双钻臂协同控制。

最后，搭建钻锚机器人钻臂轨迹规划及协同控制系统平台，验证理论研究成果。实验结果表明，在煤矿井下复杂环境中，本文所提出的钻锚机器人双钻臂轨迹规划及协同控制方法均可行且精确，有效解决了巷道锚固支护效率低、危险性高、智能化程度低等问题，提升钻锚机器人钻孔与锚固效率，实现了钻锚机器人锚固自动化，对推动综掘面智能化有重大意义。

The safe and efficient production of coal mine is restricted by the intelligent level of tunneling equipment. At present, the permanent support of underground roadway in coal mine mainly relies on manual operation. The efficiency of manual support is low and the risk is high, which seriously affects the forming speed of underground roadway in coal mine. The underground environment of  coal mine is harsh, and the intelligentization of roadway excavation is facing higher challenges, which is mainly reflected in the automatic control of roadway anchoring support. In this paper, the double drilling arm of the drilling and anchoring robot is taken as the research object, and a trajectory planning and cooperative control method of the double drilling arm of the drilling and anchoring robot is proposed to effectively improve the efficiency of roadway support, which has important technical promotion and research significance.

The kinematics and dynamics analysis of the six-degree-of-freedom drilling arm of the drilling and anchoring robot is the basis for studying trajectory planning and cooperative control. By analyzing the structure of the drill arm, the improved D-H ( Denavit-Hartenberg ) method is used to solve the kinematic parameters of the drill arm, and the forward and inverse kinematics equations are derived. Then, the Monte Carlo random sampling method is used to solve the reachable workspace of the double drill arm of the drilling and anchoring robot. Finally, the dynamic modeling and simulation of the drill arm are carried out, which lays a foundation for subsequent trajectory planning and control.

Aiming at the problem that the anchoring process relies on manual operation and low intelligence, this paper proposes a trajectory planning method for the drilling arm of the drilling anchor robot based on the improved Rapidly Exploring Random Tree ( RRT ) algorithm. In the high-dimensional complex environment of coal mine, the RRT algorithm is used to realize the autonomous dynamic obstacle avoidance and trajectory planning of the drilling arm. In order to solve the problem of slow running speed of the RRT algorithm, the artificial potential field factor is introduced, and the virtual force field of the obstacle point and the target point is constructed. The potential field diagram of the reachable workspace is calculated to improve the speed of the sampling point near the target point. The greedy algorithm and the cubic B-spline curve fitting method are used to remove redundant points and smooth the path to improve the quality of the drill arm trajectory. The simulation shows that the improved RRT algorithm is more efficient and smoother to generate the path, thus improving the intelligence of the support process.

Aiming at the problems that the drilling arm drilling in the anchoring process of coal mine mainly depends on manual operation and low intelligence, this paper proposes a drilling arm trajectory planning method of drilling and anchoring robot based on improved rapid expansion random tree ( RRT ) algorithm. In the high-dimensional complex environment of underground coal mine, the RRT algorithm is introduced to realize the autonomous dynamic obstacle avoidance and trajectory planning of the drilling arm. At the same time, in view of the slow running speed of the RRT algorithm, the artificial potential field factor is introduced to construct the virtual force field of the obstacle point and the target point, and the potential field diagram of the whole reachable workspace is calculated to improve the speed of the sampling point near the target point. Aiming at the problem of rough path generated by RRT algorithm, greedy algorithm and cubic B-spline curve fitting method are used to remove redundant points and smooth path processing to improve the quality of drill arm trajectory. The feasibility and effectiveness of the improved RRT algorithm are verified by simulation experiments. The improved RRT algorithm is more efficient and smoother in generating paths. It can improve the intelligence of the support process by realizing the integration and automation of drilling and anchoring, and provide reliable support for the intelligence of coal mines.

In order to shorten the anchoring time of the drilling arm of the drilling and anchoring robot, improve the support efficiency, and guide the dual-arm cooperative operation, the dual-arm operation allocation and hole sequence planning strategy are studied. Based on the image visual servo control method, the visual image data is used as the feedback information of the drilling arm control system in the closed-loop control link. The process flow, job distribution relationship and the coupling degree of the two arms in the roadway anchoring process are studied. The dual-arm cooperative control model of the drilling and anchoring robot based on time optimization is established to complete the automatic drilling and hole finding of the drilling and anchoring robot. Finally, the dual-arm cooperative control of the drilling and anchoring robot is realized.

Finally, the trajectory planning and collaborative control system platform of the drilling arm of the drilling anchor robot is built to verify the theoretical research results. The experimental results show that in the complex environment of coal mine, the trajectory planning and cooperative control method of double drill arm of drilling and anchoring robot proposed in this paper are feasible and accurate, which effectively solves the problems of low efficiency, high risk and low intelligence of roadway anchoring support, improves the drilling and anchoring efficiency of drilling and anchoring robot, realizes the anchoring automation of drilling and anchoring robot, and is of great significance to promote the intelligence of fully mechanized excavation face.