## 4 Conclusions and Future Works  
_(1) Conclusions:_ This work proposes an automated disassembly strategy based on robot vision for a hexagonal screw with arbitrary loose states. The robotic system is composed of a 6-DOF robot, a monocular camera, and a servo unfastening tool. In the proposed strategy, advanced methods are employed to complete tasks of the camera pose adjustment, screw-tool engagement, and screw unfastening, which increase system adaptation and reduce the hardware and software costs. Finally, real experiments are performed to verify the effectiveness of the proposed strategy. Based on the experimental results, the following conclusions can be drawn:  
(1) The screw-tool engagement is achieved using only a vision system, which can significantly reduce the system cost. However, achieving a successful engagement requires high certain measurement accuracy of the vision system. Through the experiment, the largest positioning error on the plane where the screw is located is calculated as 0.856 mm, which is less than the preset gap between the screw head and tool sleeve. This result indicates that the measurement accuracy is high enough to ensure successful screw disassembly. (2) The unfastening operations of the three tested screws are successfully completed through the cooperation motion of the manipulator and unfastening tool. The experimental results show that the proposed strategy can be used to perform the screw disassembly work effectively and reliably. (3) Variations in the torque values of the three screws under different settings are similar, which indicates the unfastening process at the micro level. Results show that the torque and its standard deviation increase with the rotating speed, which demonstrates that the motion speed of the unfastening work can be determined considering both the actual requirements of the working efficiency and the unfastening quality. Moreover, the variation curve of the torque can provide a data foundation for the value selection of the motion parameter. _(2) Limitations and future works_: This work mainly aims to develop a whole control strategy based on a vision system for screw disassembly, including the camera pose adjustment strategy for high-quality images of a screw, the screw-tool engagement strategy, and the unfastening strategy with properly set motion parameters. However, there exist cases with a poor contract between the target screw and the background. In these types of cases, the effectiveness of the proposed strategy will be reduced since the accuracy and robustness of the screw edge detection can be significantly affected by a poor contract. In this work, a screw is recognized based on the geometric features of a hexagonal shape of a screw head, which can be totally distinguished from other markings surrounding the screw but cannot effectively solve the above-mentioned problem. To overcome this problem, the machining learning-based algorithms can be used to improve the robustness of the screw edge detection under a poor contract, which could be costly during the training but may not increase the running cost during the screw disassembly. In future work, the machining learning-based algorithms will be introduced to improve the proposed strategy further, and more complex cases with high reliability will be discussed. Figure 24: The standard deviation of the torque of three screws under different settings. **Author Contributions:** Methodology, formal analysis, validation, resources, writing--original draft, J.C.; funding acquisition, writing--review and editing, Z.L. ; supervision, writing--review and editing, funding acquisition, J.X. ; validation, formal analysis, C.Y. ; validation, formal analysis, H.C.; data processing, funding acquisition, Q.C. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was supported by the National Natural Science Foundation of China (No. 51975019), Scientific Research Project of Beijing Educational Committee (No.