| Summary: | Recently, the need to produce from soft materials or components in extra-large sizes has appeared, requiring special solutions that are affordable using industrial robots. Industrial robots are suitable for such tasks due to their flexibility, accuracy, and consistency in machining operations. However, robot implementation faces some limitations, such as a huge variety of materials and tools, low adaptability to environmental changes, flexibility issues, a complicated tool path preparation process, and challenges in quality control. Industrial robotics applications include cutting, milling, drilling, and grinding procedures on various materials, including metal, plastics, and wood. Advanced robotics technologies involve the latest advances in robotics, including integrating sophisticated control systems, sensors, data fusion techniques, and machine learning algorithms. These innovations enable robots to adapt better and interact with their environment, ultimately increasing their accuracy. The main focus of this study is to cover the most common industrial robotic machining processes and to identify how specific advanced technologies can improve their performance. In most of the studied literature, the primary research objective across all operations is to enhance the stiffness of the robotic arm’s structure. Some publications propose approaches for planning the robot’s posture or tool orientation. In contrast, others focus on optimizing machining parameters through the utilization of advanced control and computation, including machine learning methods with the integration of collected sensor data.
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