Embracing the Future: Industrial 4.0 and Vision-Guided Robotics!
In the era of Industry 4.0, autonomous robots play an increasingly important role in factories, but they require visual feedback to navigate the workspace, avoid obstacles, collaborate with humans, identify and locate working parts, and improve positioning accuracy. Different vision techniques, such as photogrammetry, stereo vision, structured light, time of flight, and laser triangulation, are widely used in industry for inspection, quality control, and now for robot guidance. The choice of which vision system to use depends highly on the parts that need to be located or measured. Therefore, this paper presents a comparative review of different machine vision techniques for robot guidance. The study analyzes the accuracy, range and weight of sensors, safety, processing time, and environmental influences. This provides background information for researchers and developers in their future work[1].
Particularly highlighted is the advantage of industrial 3D vision guidance technology. In industrial scenarios, industrial robots need to achieve high absolute accuracy, and conventional robots are limited in achieving this level of precision. To improve accuracy, optical calibration methods, such as laser tracker systems, photogrammetry, or vision systems with multiple high-resolution cameras, are used to detect the spatial position of the tool tip and correct robot motion. Additionally, actual positions of working parts may slightly differ from what the robot expects. Thus, 3D vision guidance technology enables successful navigation of mobile robots by providing detailed information on the immediate environment, giving developers the opportunity to create sophisticated navigation software.
The development of industrial robotic arms is closely related to the application of machine vision technology. Therefore, this paper compares and studies different machine vision techniques and their applications for robot guidance. The suitability of each vision technique depends on its final application, as requirements differ in terms of accuracy, measurement range and sensor weight, safety for human workers, data acquisition and processing time, environmental conditions, integration with other systems (mainly the robot), and budget. Challenges are found in scenarios with textureless surfaces for the correspondence problem, lighting conditions causing brightness issues, camera viewpoint occlusions, undetermined moving objects, etc. Other related comparisons are also analyzed in this work.
The research provides background information for the field of industrial robotic arm vision guidance and serves as a reference for future research and development.
