# Design and Validation of a Camera-Based Safety System for Fenceless Robotic Work Cells
## 2 Background  
In this section, safety standards in work cells using robot arms, shortcomings of existing safety systems, comparison of known sensor-based and camera-based safety systems, and some proposed solutions are summarized. Safety standards applied in robotic work cells have been an active research topic in the past few decades. Considering work cells using robot arms, the most critical factor is ensuring work and worker safety [10]. Some studies combine various design approaches for the hardware and software of safety-related systems [11; 12]. ISO 12100: 2010 Machine Safety--General Principles for Design--Risk Assessment and Risk Reduction Standard [13] describes a typical workflow that focuses on the safety of the work cell. This workflow can be listed as explaining the system, identifying the hazards, and determining the emerging risks and risk reduction strategies. In addition to the documentation of the system and the associated risks, it is also necessary to validate the risk reduction strategies. A study by [PERSON] et al. was conducted within the framework of these standards [14], which provides guidance for the design and implementation of a robotic safety system. There are many commercially developed safety systems for work cells using industrial robot arms. These can be classified into sensor- and camera-based systems. Sensor-based ones are mostly preferred in simpler and controlled industrial environments because of disruptive factors such as industrial ambient noise that affects measurements' sensitivity and accuracy. These effects are also valid for camera-based safety systems. Developing camera-based safety systems with software technology has a considerable advantage of supporting flexibility in production. Consequently, various camera image-based safety systems are available in the industry [15]. Considering that camera technology is developing day by day, it is predicted that it will be a more practical and widely used choice in the future, as compared with sensor-based safety systems [16]. In the past few decades, IP cameras have been used to ensure safety in robotic work cells in which monitoring systems are based on the human body's posture and position [17]. Another approach is tracking the position and speed of robot arms' end effectors [18]. There are also commercial systems that visually track undesirable objects in work cells [19]. Knowing these systems and with developments in camera technology, various systems using 2-D cameras have been proposed [20]. Some commercial systems have been developed by companies such as Kuka, Reis, ABB, Fanuc, and Pilz. The one by Pilz is a safety system called SafetyYE, in which a 3-D camera is used [3]. This system works with the principle of slowing down and stopping the robot arm when there is an undesired entry into the scanned volume of the work cell in 3-D. However, using one camera, it is believed that the robot arm may create a blind spot depending on the working angles. These blind spots affect system reliability. In addition, because additional 3-D cameras increase the cost, it is a necessity to introduce a more cost-effective and high-reliability system. Machine vision (MV) is an imaging-based application whose technology is used in the field of industrial automation [21]. In these systems, where software and hardware work in an integrated manner, images can be used in areas such as guidance, control, and safety of robot arms. While the input and output are both images in image processing, in MV technology, the output is an information or command corresponding to the image input. MV automatically converts the information it extracts from the image into a command. Thus, a camera-based safety system, using either 2-D or 3-D cameras and no artificial-intelligence-based image characterization is considered within the framework of MV. Consequently, there have not been any studies to find the most appropriate number of cameras to cover the blind spots in work cells.