## 2 Methods for Improving Robot Processing Accuracy  
To improve the accuracy of robotic machining, two main approaches are being studied to overcome the above problems. The first approach is optimizing the structure of industrial robots to improve their inherent stiffness properties. [PERSON] et al. developed a new type of industrial robot and improved its stiffness using a hybrid structural design with open-loop and closed-loop motion chains [34]. Specialized end-effectors, such as antennas and pressure feet, were designed to improve the machining stability and stiffness of the robot during drilling and boring operations [35; 36]. Manufacturers of industrial robots have also optimized the mechanism design so industrial robots meet the stiffness requirements of production processing. For example, ABB produced IRB 6660 for press tending and machining, which has an additional parallel arm to increase the stiffness of the robot and a special dual-bearing design to overcome the fluctuating process forces during machining [37]. KUKA offered KR 500 FORTEC MT (machining tooling), which was designed for high-payload milling applications [38]. In addition, some researchers have proposed improving the structural stiffness of robots by optimizing the materials used for robot joints and manipulator arms [39; 40; 41]. The second approach is to compensate for deformation during processing with a static stiffness model. The principle of compensation control is shown in Figure 2. First, an external force is acquired and transformed with sensors installed on industrial robots. When combined with the transformed data and joint angles directly acquired from the robot system, the stiffness model outputs the deformation after calculation to calibrate the robot [23]. In general, robot stiffness parameters are not provided by the manufacturers of industrial robots. For this reason, a stiffness model must be developed using dedicated experimental research [42]. The stages of compensation execution can be divided into online compensation and offline compensation, with the main difference being whether the error model input needs to be obtained online. Model-based online error compensation relies on the acquisition of the actual position of a robot, which includes the 3D/6D Cartesian coordinates of the robot tool central point (TCP) or each joint angle. The TCP position is normally measured using optical metrology equipment such as a laser tracker, coordinate measurement machine (CMM), and 3D camera systems [43; 44; 45]. The joint angle is measured using a secondary encoder mounted at the output of each joint [9]. Then, real-time compensation is carried out based on the errors between the actual and theoretical coordinates [25; 27; 46; 47; 48; 49; 50]. Since the measurement accuracy of the metrology equipment is much higher than the positioning accuracy of robots, this method has the advantage of high precision. At the same time, some scholars combine the algorithm model with the stiffness evaluation to improve the accuracy of the stiffness evaluation [51; 52], including the controller strategy, recursive algorithm, etc. For example, [PERSON] et al. combined a residual-based estimator of the torque at flexible transmission and a recursive least squares stiffness estimator based on a parametric model to conduct an online stiffness evaluation. Without a joint torque sensor and speed measurement, this method exhibits good robustness [53]. However, it also presents the following problems [54; 55; 56]:  
* Online error compensation is difficult to implement, has high hardware requirements, high sensor costs, and a long operating time. * Sensors have some inherent limitations in the application process due to sensor data noise, communication delays between the sensor and the robot controller, and the interference suppression bandwidth of the robot EE. * The measurement system is susceptible to external factors, which greatly reduce the measurement accuracy.