## 1 Introduction
[31] investigated an initial position optimization method for the welding robot based on the digital twin model, which found the initial welding position with the best operational smoothness through a particle swarm algorithm to ensure the welding efficiency. Although the above methods have significant effects on the machining of small parts, the optimization effects are limited for large-size parts machining, and may even prevent the configuration optimization of the robot due to operating range limitations [32; 33]. On these grounds, a robot system with mobile platform is gradually adopted to manufacture large structural parts. [PERSON] et al. [11] used the additional axis of the robot to divide the position of the wing drilling, and optimized the robot configuration based on the static stiffness model, which effectively ensured the operation stiffness of the robot in the whole task. Moreover, when the automated guided vehicle (AGV) is used as the robot fixed platform, the flexibility of the robot system can be further improved, which ensures that the robot has redundant DOF to performing the task [34]. Nevertheless, due to the poor stiffness of AGV, its influence on the stiffness performance of the robot system should be considered when performing the configuration optimization, and the complex structure greatly increases the difficulty of stiffness modelling. The machining vibration of the robot depends on its dynamic performance. Due to the weak stiffness and asymmetric structure of the robot, it is more prone to chatter (self-excited vibration), which is one of the biggest obstacles in the pursuit of machining quality [35; 36]. In order to prevent the chatter during milling, [PERSON] et al. [18] adopted the stable lobe diagram under different robot configurations established by the inverse distance weighted model for configuration optimization. Likewise, [PERSON] [17] and [PERSON] et al. [37] analyzed the machining vibration and optimized the whole milling trajectory based on the robot dynamics model, realizing the avoidance of machining chatter with non-process parameters adjustment. When no chatter occurs, the vibration response of the workpiece should be reduced as much as possible to further improve the surface machining quality. [PERSON] et al. [38] took the spindle vibration response as the index and combined it with genetic algorithm to optimize the robot configuration, and reduced the vibration response by changing the configuration to realize the improvement of the drilling quality. Considering the above studies, it can be seen that the robot configuration optimization based on the dynamics performance can directly reduce the vibration response and improve the machining quality. However, the existing studies are mainly aimed at the vibration suppression of the robot machining system with the open-loop structure, and the clamping robot has not been sufficiently investigated. In addition, when the robot acts as a fixture to fix the cantilever workpiece on aerospace products, the workpiece forms an approximately closed-loop structure with the clamping robot system, which increases the difficulty of analyzing the dynamics performance and selecting the appropriate robot configuration. Inspired by the above mentioned studies, this paper combines dynamics response prediction and configuration optimization to improve the vibration suppression effect for the cantilever workpiece. In order to enable the clamping robot to suppress the vibration of the workpiece at different positions on the space station, the AGV is used as the carrier. The established dynamics model of the AGV-robot-gripper-workpiece (ARGW) system can predict the vibration response of the workpiece in the clamped state. Then, the influence of different configurations of the clamping robot caused by different clamping directions, different workpieces, and movement of the AGV on the vibration response of the workpiece were analyzed.