With the improvement of industrial automation levels, the demand for automatic assembly increases. In this work, a bolt fastening robotic system based on hybrid vision is proposed to enhance environmental adaptability and reduce costs. In its fastening strategy, the hole pose is determined accurately using hybrid vision, which uses the scene camera to obtain the rough pose and uses the end-effector camera to obtain the precision pose based on its high-quality images acquisition. After the positioning, the efficiency of the fastening process is significantly influenced by motion parameters of the robotic system, such as the angle between bolt and hole, the speed of the end-effector, and the rotation speed of the tool. The maximum allowable angle of bolt and hole is calculated. The M10 bolt can be smoothly inserted into the hole when angle is less than 2.92 degrees. The experiments were conducted to investigate how changes in these motion parameters collectively impact bolt fastening efficiency. Changes in the angle between the bolt and the hole do not affect the robotic bolt fastening efficiency. These results provide the significant theoretical basis for the selection of process parameters of the robotic system, considering the specific requirements of the bolt assembly task.