A vision-guided robot (VGR) system is a robot fitted with one or more cameras used as sensors to provide a secondary feedback signal to the robot controller for a more accurate movement to a variable target position. VGR enables robots to be adaptable and more easily implemented, while reducing the cost and complexity of fixed tooling previously associated with the design and set up of robotic cells, including material handling, automated assembly, agricultural applications, and life sciences. Vision-guided robotics can be economically advantageous in countries with high manufacturing overheads and skilled labor costs by reducing manual intervention, improving safety, increasing quality, and raising productivity rates. The expansion of vision-guided robotic systems is part of the broader growth within the machine vision market, which is expected to grow to $17.72 billion by 2028. This growth can be attributed to the increasing demand for automation and precision, as well as the broad adoption of smart technologies across industries.
Overview
A machine vision system comprises a camera and microprocessor or computer, with associated software. This is a broad definition that can be used to cover many different types of systems which aim to solve a large variety of tasks. For example, vision systems can be used for quality control to check dimensions, angles, colour, surface structure, or for the recognition of an object as used in VGR systems. A camera can be anything from a standard compact camera system with an integrated vision processor to more complex laser sensors and high-resolution and high-speed cameras. Combinations of several cameras to build up 3D images of an object are also available. In one example of VGR used for industrial manufacturing, the vision system (camera and software) determines the position of randomly fed products onto a recycling conveyor. The vision system provides the exact location coordinates of the components to the robot, which are spread out randomly beneath the camera's field of view, enabling the robot arm(s) to position the attached end effector (gripper) to the selected component and pick from the conveyor belt. The conveyor may stop under the camera to allow the position of the part to be determined, or if the cycle time is sufficient, it is possible to pick a component without stopping the conveyor using a control scheme that tracks the moving component through the vision software, typically by fitting an encoder to the conveyor, and using this feedback signal to update and synchronize the vision and motion control loops.
Limitations There are always difficulties in integrated vision systems to match the camera with the set expectations of the system. In most cases, this is caused by a lack of knowledge on behalf of the integrator or machine builder. Many vision systems can be applied successfully to virtually any production activity, as long as the user knows exactly how to set up system parameters. This setup, however, requires a large amount of knowledge by the integrator, and the number of possibilities can make the solution complex. Lighting in industrial environments can be another major downfall of many vision systems.
Overcoming lighting constraints with 3D vision An advantage of 3D vision technology is its independence from lighting conditions. Unlike 2D systems that rely on specific lighting for accurate imaging, 3D vision systems can perform reliably under a variety of lighting scenarios. This is because 3D imaging typically involves capturing spatial information less sensitive to contrast and shadows than 2D systems. In recent years, start-ups have started to appear, offering softwares simplifying the programming and integration of these 3D systems, in order to make them more accessible for industries. By leveraging 3D vision technologies, robots can navigate and perform tasks in environments with dynamic or uncontrolled lighting, which significantly expands their applications in real-world settings.
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