## 1 Introduction  
In recent years, robots have been increasingly applied to the field of automated manufacturing, thus improving both working efficiency and quality. In the assembly process of mechanical manufacturing, threaded assembly is one of the most common methods [1]. In particular, the screw-unfastening process is always performed when workpieces require maintenance to extend the product life through re-manufacturing effectively [2]. In modern industry, screw fastening and unfastening tasks have been typically completed in three ways: through manual operation using a wrench or screwdriver, as shown in Figure 1a; through automatic operation using a Cartesian robot, as shown in Figure 1b; and through automatic operation using an articulated robot, as shown in Figure 1c. Disassembly tasks in complex three-dimensional (3D) structured and unstructured environments have been mostly completed by humans. As humans perceive information on environmental parameters and screw pose mainly by eyes, the peg-in-hole, fastening, and unfastening states are typically analyzed through a sense of touch by hands [34]. Although humans can perform the loosening of screws under different complex states, this approach has the disadvantages of low efficiency, massive labor, and difficult control of the disassembly effect. To address these problems, automatic operation methods have been developed. Compared to a Cartesian robot, an articulated robot is more applicable to complex 3D environments, for instance, for different loose states located on the same surface and screws located on different surfaces, as shown in Figure 2. A force or torque sensor has been commonly used to perceive interaction information during the operation. Moreover, to improve the automation level, many studies have adopted control methods of automatic peg-in-hole, fastening, and unfastening actions guided by industrial robots. In the following, some of the recent representative studies are presented to show the contributions of this work. To address the peg-in-hole problem, some studies have installed passive compliance devices at the end effector of a robot to perform the action [5], thus compensating for the location error between the peg and hole. However, the complaint mechanism has a complex, difficult, and high-cost design and cannot be used for the precise control of the force and torque due to high structural flexibility, which limits screw assembly operation. A number of studies used robotic vision sensors to measure relative posture between the peg and hole or other workpieces in a 3D environment. [PERSON] [6] presented an industrial robotic system of industrial robot equipped with a triangulation scanner in the assembly process. And accurate positioning of the elements in 3D was recognized based on point clouds. [PERSON] [7] proposed a robotic assembly system under the guidance of the 3D models over the internet, which used a monocular camera to collect a sequence of images in different poses. [PERSON] [8] developed a robot assembly system with two manipulators and a structured light camera. The component pose at the end of a manipulator is calculated in 3D space. The peg-in-hole action is performed using a robotic rigid end effector based on geometric and physical models, as well as the position or force feedback [9; 10]. In addition to the peg-in-hole problem, the control of fastening and unfastening operations represents the core limitation to the successful and high-quality system operation. Certain studies have solved this problem using a combination of vision and force and torque sensors, obtaining a good effect in the screw-fastening tasks [11; 12]. Compared with the screw-fastening process, a robotic vision-based screw-unfastening process requires measuring the screw pose with a hexagonal shape, not a circular hole. The measurement accuracy is crucial for the success and quality of the unfastening process. Additionally, automatic processes of the assembly and disassembly actions are not completely the opposite.