To address the issues of time-consuming and labor-intensive skull grinding during craniotomy surgeries, as well as the dependency of surgical quality on the operator's experience, a surgical robotic skull grinding method was proposed. This method was integrated with an optical navigation system to achieve precise real-time reconstruction of the skull grinding area. A hybrid hierarchical bounding box was established to spatially constrain the reconstructed model, clarifying the grinding area while ensuring surgical safety. Within the bounding box space, a three-dimensional full-coverage path planning algorithm was developed to generate the grinding trajectory. By combining the rigid body tool of the navigation system, the registration of the robotic arm's coordinate system and the skull coordinates was achieved, guiding the robotic arm to the surgical entry point. The spatial relationship was established by aligning the TCP origin of the robotic arm with the optical positioning marker point of the navigation system, completing the spatial registration between the robotic arm and the navigation system. Finally, the grinding trajectory in the robotic arm's workspace was generated. Experimental validation using a skull model demonstrated that the method was able to achieve an intelligent, efficient, and stable skull grinding process.