为提高煤矿巷道支护效率，改善掘进工作面自动化智能化程度，设计了一种集成悬臂式掘进机和六自由度机械臂的钻锚机器人，研究了钻锚孔孔序规划策略及钻臂轨迹规划方法。首先，基于改进的DH方法建立机械臂结构的运动学模型，分析其运动学正解和逆解，解算钻机空间位姿及对应的关节变量值；其次，基于蒙特卡罗随机数方法分析机械臂运动空间，求解双机械臂运动范围；基于钻锚作业工艺要求提出一种钻锚孔孔序规划策略，在不同条件下两机械臂按照一定锚固顺序完成顶板锚固作业任务；最后，基于五次多项式插值方法完成机械臂轨迹规划，以实现钻臂快速平稳达到目标位置。仿真结果表明，钻锚机器人能满足宽×高为6 000 mm×4 500 mm范围内的巷道支护要求，基于该孔序规划策略和钻臂轨迹规划方法，两钻机在进行顶板锚固作业任务时能有效避免干涉，实现准确、平稳运动至锚固点，完成锚固任务。研究结果为发展自动支护和智能掘进提供了一种思路和方法。

In order to improve the support efficiency of coal mine roadway and the automation intelligence of heading face, an anchor drilling robot integrating the cantilever roadheader and the six-degree-of-freedom manipulator was designed, and studied on the anchor drilling sequence planning strategy and the drilling boom trajectory planning method. Firstly, the kinematics model of the manipulator structure was established based on the improved DH method. On this basis, the forward and inverse kinematics solutions were analyzed, and the spatial position and the corresponding joint variable values of the drilling rig were calculated. Secondly, the manipulator space was analyzed based on Monte Carlo random number method, and the motion range of dual manipulators was solved. Then, the drilling sequence planning strategy was put forward based on the technical requirements of drilling and anchorage, so that the two manipulators could complete the roof anchoring task according to a specific anchoring order under different conditions. Finally, the trajectory planning of the manipulator was completed based on the quintic polynomial interpolation method, so that the drill boom could reach the target position quickly and smoothly. The simulation results show that the anchor drilling robot can meet the requirements of roadway support in the range of 6 000 mm wide × 4 500 mm high. Based on the hole sequence planning strategy and the drilling boom trajectory planning method, the two rigs can effectively avoid the interference in the roof anchorage operation, realize the accurate and smooth movement to the anchorage point and complete the anchorage task. The research results provide a way of thinking and method for the development of automatic support and intelligent mining.