Vision-Guided Robots’ Role in Smart Factory Automation

Industry 3.0 replaced the human workforce doing repetitive tasks with robots that perform the same tasks with utmost accuracy without getting tired at any point in time. This advancement increased the output of factories by folds, helping them to make huge profits by reaping the benefits of economies of scale. But consumers today demand more customized goods and services that best suit their needs. To cater to changing consumer behavior, many industries have switched to smart factories, which are a complete ecosystem of interconnected assembly lines and monitoring systems that continuously monitor and adapt based on changing needs to maintain high efficiency and output and facilitate customization. Industry 4.0 is the age of smart factories, which are a digitized form of conventional manufacturing units wherein the toughest of jobs are replaced by intelligent robots that are completely aware of their surroundings. Vision-guided robots are an integral part of such smart factories.

Conventional robots can easily perform repetitive and pre-defined tasks for which they have been built and programmed. But they fail to perform tasks where there are dynamic changes in environmental conditions; i.e., these robots are effectively blind and require a controller to operate in such conditions. To automate tasks that are dynamic in nature, machine vision along with robotics is used to build vision-guided robots. Vision-guided robotics is a collective term for the technologies needed to recognize, process, and handle items based on visual data. These robots find a wide range of applications in quality assurance, assembly lines, enforcement, and inventory management.

Market dynamics of vision-guided robots.

Market dynamics of vision-guided robots (Source:

Machine vision for visually guided robots

Machine vision is the ability of machines to “see” and make rapid decisions based on their surroundings. It uses inputs from cameras and different sensors to effectively analyze its surroundings and make decisions. The technique when combined with robotics makes the robots smart enough to make accurate decisions based on dynamic environmental conditions. Machine vision supports smart factories by improving their performance through stock improvement, defective component detection, and product quality improvement. There are three major steps involved in the working of a vision-guided robot:

Working steps of a vision-guided robot.

Working steps of a vision-guided robot (Source:

  • Data capturing. The first step is to capture the surrounding variables through one or multiple cameras and sensors. The hardware used to capture the data can range anywhere from a compact smart camera with an integrated vision processor to complex laser and infrared sensors and high-resolution, high-speed cameras depending upon the complexity and the accuracy requirement.
  • The collected imagery is processed along with data collected by different sensors. The other sensors could include a LiDAR sensor or a line follower that helps in the navigation of the robot through spaces, or it also includes inputs from sensors that are specific to the process being implemented. Decisions are then made based on the data collected by sensors as well as the cameras.
  • Based on the inferences drawn from the data, desirable steps are carried out by the robot based on the task to be completed.

Machine vision makes these robots highly adaptable to their surroundings and is necessary for industries with shorter production runs and regular changes to a product. For example, traditional automation done in factories has limited flexibility and is generally used for larger batch sizes, but if a new product or method is introduced, large changes need to be done to satisfy the needs.

Vision-guided robots, on the other hand, can easily adapt with small changes to efficiently meet new needs. Using their vision system and control software, it is possible for them to handle different types of components. Parts with varied shapes can be fed in any random orientation to the system and be picked and placed without any mechanical alterations to the machine, resulting in quick changeover times. Other features of vision-guided robots include the following:

  • Most of the functions are software-controlled, which makes switching between processes easier and faster without mechanical adjustments.
  • They have higher residual values compared with process-specific robots when the process is changed.
  • There are shorter lead times and payback periods.
  • They have higher efficiency, reliability and flexibility.

Vision-guided robots also use 3D time-of-flight sensors along with standalone cameras to get 3D measurements of nearby objects without physically moving the camera. This helps to effectively locate parts in a space-constrained environment with higher accuracy and speed.

These advanced machine-vision systems employed in robots have to process a large amount of data in a short period of time. Processors in the system use hardware accelerators like FPGAs and ASICs, which gives these robots the capability to handle thousands of SKUs on a production line or any other resource-exhaustive application. Technologies like edge sensors and private 5G play a major role in facilitating real-time data transfer and data processing for mission-critical applications.

Future of vision-guided robots

The future of vision-guided robots for smart factories is extremely promising. This technology has the potential to revolutionize manufacturing processes by increasing efficiency, safety and accuracy in industrial operations. As artificial intelligence continues to develop at a rapid pace, it will be increasingly capable of anticipating and responding to changing conditions on production floors with greater speed than ever before—ensuring that tasks are completed more quickly and accurately than human operators alone can achieve. Furthermore, advances in computer vision have enabled these robots to identify objects within their environment precisely so they can navigate autonomously or perform complex assembly sequences with minimal oversight from an operator.

Manufacturers leveraging this technology also benefit financially, as machines operating with precision use fewer raw materials while reducing scrap waste significantly compared with manual labor-based methods—resulting in cost savings over time as well as improved customer satisfaction through higher-quality customized products delivered faster. In conclusion, the utilization of vision-guided robotics presents many opportunities for those looking to make their factory smarter and more efficient now and into the future. One just needs access to capital investments needed when deploying such advanced technologies today.

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