# A System for Robotic Extraction of Fasteners
## 1 Introduction  
There is a growing need for more intelligent and adaptive industrial robot capabilities within robotic work cells in scenarios regarding servicing, managing EOL components, and reclaiming e-waste materials in a non-destructive manner. A vast amount of preliminary research into this field focuses on feasibility assessments of product disassembly for component re-purposing or recovery by analyzing product waste streams and the complexity of high-value component assemblies [1, 2]. Other areas of focus are various non-destructive or semi-destructive initiatives for reclaiming materials from EOL electric vehicle waste streams [1, 3, 4]. Some methods proposed include variations in human-machine collaboration for EOL battery recycling where humans can handle non-trivial tasks while collaborating robot platforms can manage high-volume repetitive taskings like removing screws and bolts [4, 5]. Other areas of automated disassembly focus on consumer products like LCD screens [6, 7], which, given the lack of predetermined knowledge of fastener position, still rely on semi-destructive methods like using angle grinders and power drills to separate components. Mechanical fastener extraction constitutes approximately 40% of all disassembly operations [8]. Several researchers have addressed the automated disassembly of fasteners. [PERSON] and [PERSON] [9] developed a system to remove screws from the backs of laptops automatically using non-AI vision algorithms. [PERSON] et al. [8] presented a device for automated hexagonal-headed threaded fastener removal that used a spiral search strategy to overcome uncertainties in the location of the fasteners. [PERSON] et al. [10] introduced a method thatutilized images of a screw to assist in aligning the screw-tool interaction, with torque monitoring employed throughout the screw removal process. With recent advances in vision-based AI in the classification and accurate identification of mechanical components using varying region-based convolutional neural networks (R-CNNs) and DCNN methods, many researchers [11; 12; 13; 14; 15; 16; 17; 18; 19] have reported on the use of vision-based AI in screw detection. This makes it possible to integrate this technology with robotic systems for applications such as reclaiming e-waste components. This task is traditionally prohibitively expensive with human operators and needs to be more efficient when relying on existing automated methods. While there is a significant interest in the use of AI methods for screw detection, only a few studies [20; 21] have addressed the development of AI-based robotic screw disassembly systems. This paper focuses on developing a robotic system capable of extracting cross-recessed mechanical fasteners through an application programming interface (API) layer. This system integrates a standalone DCNN vision system, explicitly trained to detect cross-recessed screws, with robotic simulation software designed for industrial robot applications. The system was tested on cross-recessed screw (CRS) targets with a 0.5-inch (12.7 mm) diameter at camera ranges of 280-500 mm. It employs a stereoscopic camera system with an RGB (red, green, blue) imager. The primary contribution of this work lies in the integration of a DCNN vision-based system, simulation software, and robotic framework for screw removal, as well as the development of a two-stage imaging process for screw disassembly. Unlike previous work [20; 21], which focused on the disassembly of hexagonal-headed screws, this research explicitly targets CRS disassembly. Figure 1 illustrates the input images used to train this study's DCNN implemented for fastener detection. The remainder of this paper is organized as follows. Section 2 of this work focuses on methodology, including introducing the simulation software, and how other software was integrated to provide detection and fastener removal capabilities.