## 1 Introduction
[PERSON] [13] and [PERSON] [14] analyzed and reviewed a large number of automatic fastening actions, which cannot be directly conducted or adjusted. Some of the related studies focused on operational tools, which are different for the fastening and unfastening actions. [PERSON] [15] proposed a detachable tool consisting of a modified drill with a movable needle,  
Figure 1: Three ways of performing the screw fastening and unfastening tasks. (**a**) Manual operation using a wrench or screwdriver. (**b**) Automatic operation using a Cartesian robot. (**c**) Automatic operation using an articulated robot. Figure 2: Screws in a complex environment. (**a**) Screws under different loose states located on the same surface. (**b**) Screws located on different surfaces. which can be used to grab the top of fasteners. [PERSON] [16] designed an unfastening tool that can be fixed on the end of a robot and applied to fasteners of the same shape. There are four main methods for screw unfastening, including human operation, artificial teaching control, human-robot cooperation, and sensor-based intelligent control methods. For instance, [PERSON] [17] proposed a human dismantling platform, which can assist a computer in learning the dismantling process of an LCD display rear cover, including tool locations and the key process. [PERSON] [18] completed the repeated disassembly automation operation of motor bolts by robots using the artificial control-based method. [PERSON] [19] built a hybrid human-robot cooperation unit to complete the automatic disassembly work for the Diaphragm coupling structure. [PERSON] [20] and [PERSON] [21] developed a robotic system based on a Kinect sensor, which has three motion axes, and applied it to the notebook disassembly. Experimental results [20] have shown that the success rate of the assembly work guided by this system can reach 96.5 % (251/260). Actually, robots with three motion axes have been widely used in the automated assembly and disassembly tasks in the field of electronic assembly. However, their application is limited for workpieces with high requirements for size and shape requirements, as well as for that with screws located on multiple planes. [PERSON] [22] proposed a spiral method for a robot to search for screws and adjust the tool-screw engagement based on sensing data of a force or torque sensor, as well as completing the screw unfastening operation through human-robot cooperation (HRC). Still, the working efficiency and success rate of this method cannot meet practical requirements for key components in the field of aerospace and rail transit. In summary, the existing methods have been limited in screw removal in complex 3D environments. To address this limitation, this work presents a low-cost and high-adaptability disassembly strategy based on robot vision for a robot with six degrees of freedom (DOFs) equipped with a special screw tightening tool at its end effector, as shown in Figure 3. The comparison of representative recently-proposed methods and the strategy proposed in this study is given in Table 1.  
The main contributions of this work can be summarized as follows:  
(1) A camera pose adjustment method is proposed for obtaining high-quality images of a target screw. In this method, the camera orientation is adjusted based on the geometric feature analysis results of the characteristic circle, aiming to make the image plane parallel to the screw head. Meanwhile, the camera position is adjusted based on the rough location of a screw relative to the image center, aiming to make the screw located approximately in