## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Sensor** & **DOFs** & **Control Type** & \
\begin{tabular}{c} **Scene** \\ **Adaptability** \\ \end{tabular} & **Intelligence** &
\begin{tabular}{c} **Disassembly** \\ **Quality** \\ \end{tabular} \\ \hline [PERSON] [17] & No & - & Manpower & Any scene & Low & Not considered \\ [PERSON] [18] & No & 6 & Robot teaching & Low & Low & Not considered \\ [PERSON] [19] & No & 6 & HRC & Low & Low & Not considered \\ [PERSON] [20] & Camera & 3 & Automation & 2D scene & High & Not considered \\ [PERSON] [22] & F/T sensor & 6 & HRC & Flat surfaces & High & Considered \\ This study & Camera & 6 & Automation & 3D scene & High & Considered \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between the previously reported methods and the proposed method. Figure 3: Scheme of the proposed screw disassembly system. the center of the image plane. This can help to improve the measurement accuracy of the screw pose. (2) For the screw-tool engagement, a calculation method is developed to determine the hexagonal screw pose rapidly and accurately based on image information. In this method, 3D locations of hexagonal vertexes can be obtained through an accurate description of screw edges. Compared with the machine-learning-based method [23], this method reduces the location uncertainty caused by sample training and meets the application requirements of ordinary bolts, which can help to increase the success rate of the screw-unfastening operation significantly. (3) An unfastening control method is proposed for the cooperated motion of an unfastening tool and a robot to ensure the disassembly quality. Additionally, variation curves of the torque under different motion speeds are analyzed based on sensing data of the unfastening tool, which provides a significant data foundation for the value selection of motion parameters. (4) A strategy based on vision feedback and methods of the imaging process and coordinated motion control of a robotic system is proposed. Compared with the methods based on force and torque information, it largely reduces the operating cost of a system, as well as the data size and processing complexity. From the main contributions of this work, the novelty lies in new methods proposed for screw-tool engagement and unfastening action. The new method for screw-tool engagement is composed of a camera pose adjustment method and a screw pose calculation method, which are proposed to improve the engagement success rate and reduce the system cost. The unfastening action is realized by the cooperated control of the axial motion of the robotic end effector and the rotational motion of the unfastening tool with proper speeds, which can be taken according to their influence analysis on the disassembly quality. Based on the above methods, an automatic disassembly strategy of hexagonal screws based on robot vision is finally obtained. Compared with the methods based on force and torque information, it largely reduces the system cost since there is no need for high-cost sensors, such as a six-axis wrist force sensor, as well as the data size and computing complexity. The rest of this article is organized as follows. Section 2 introduces a disassembly strategy based on robot vision, including four steps--camera pose adjustment, screw-tool engagement, hexagonal screw unfastening, and moving screw away from a workpiece--and describes the hexagon screw pose calculation based on the camera pose adjustment, as well as the geometry analysis. Section 3 presents the experiments conducted to verify the effectiveness of the proposed control strategy and discusses the experimental results in terms of the screw-tool engagement. Section 4 gives the main conclusions and future work directions.