# Camera Calibration in High-Speed Robotic Assembly Operations
## 1 Introduction  
Over the last several decades, robotics grew continuously and positioned itself as one of the most dynamic fields in engineering. Today, different areas in robotics are more and more integrated together into the same applications. Industrial robots, once reprogrammable machines that were only capable of performing repetitive tasks again and again, are now able to use a large variety of sensors to collect data from the working environment and can make decisions based on the information received. The sensors used in industrial robotics include (but are not limited to) force and torque sensors, LIDAR sensors, ultrasonic sensors, infrared sensors, laser sensors, capacitive sensors, temperature sensors, gyroscope, accelerometers, and cameras. The growing level of sensor integration allowed a greater level of interaction between the industrial robots and the nearby equipment, as well as program adaptability and better responses to changes and various events in the working environment. This, in turn, increased the flexibility of industrial robotic systems [1]. Benefiting from growing levels of sensor integration, industrial robots gained better capabilities and were increasingly integrated into automated lines for palletizing, sorting operations, or automating simple, repetitive tasks [2]. Actually, this direction of evolution in industrial robotics represents a response to the requirements of IoT and Industry 4.0, concepts that dictate the general trend in today's industrial landscape. Smart robots that are capable of operating in uncertain environments (that is, environments that are not deterministic) are required in many modern tasks [3]. Being the backbone of this approach in robotics, sensors are present in virtually every industrial robotic application. They are essential for interaction with the working environment, for M2M (machine-to-machine) communication, and for monitoring the internal state of the robotic system. One of the most important areas in the field of industrial robotics, collaborative applications, benefits from the extensive use of sensors. Sensors are also the base of the transition towards smart manufacturing and Cyber-Physical Systems (CPS) as solution to full development of smart factories. Being the main elements for data acquisition, sensors allow collecting information and gaining knowledge, which, in turn, represent the building blocks for training artificial intelligence systems and big data analysis [4]. One of the main issues in uncertain working environments is linked to unknown part position, orientation, dimensions, and shape. In an industrial application, creating a deterministic setup for the workpiece often requires fixtures, guiding elements, or other pieces of equipment that increase the costs and complexity. Furthermore, working with known part dimensions and shape imposes limitations on application flexibility. Being able to work with parts that have random positions and orientations, while allowing for large variations in dimensions and shape, greatly improves flexibility and productivity, and represents an important step towards smart manufacturing. Robotic vision systems are among the solutions that address these issues. These systems include cameras that, together with specific software (ifm vision assistant version 2.6.24), process digital image data in order to provide information to the manufacturing system. Besides object recognition, robotic vision systems have multiple uses, such as defectoscopy, object tracking, pattern recognition, augmented reality, and optical character recognition (OCR), and can be integrated with AI and machine learning [5; 6]. Taking these aspects into account, from the robotic task point of view, the vision system has the potential to increase productivity, the flexibility of the application, and the collaborative level, as well as the accuracy and repeatability of the robot.