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Understanding Robotic Vision Challenges

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Robotic vision can enhance a company’s automation structure. Unified robot solutions provide easy and fast robotic vision benefits. One does not require programming skills to achieve this. However, understanding vision can be an arduous task even though technology has advanced tremendously.

There are various factors that influence robotic vision in the workplace, task setup, and the environment. Below are various challenges one is likely to face in regard to robotic vision.


Whether you are a professional photographer or not, you understand the importance of lighting when it comes to capturing good photos.  Poor lighting can ruin your efforts. Remember, the human eye is more adaptable as compared to imaging sensors and with poor lighting, the latter cannot recognize objects.

One can conquer this lighting challenge in various ways. First, they can link the vision sensor to an active lighting, a concept often used by Universal Robots in their cob\/ot cameras. Secondly, one can utilize laser lighting, fixed lighting, or infrared lighting

Articulation or Deformation

A computer vision structure can easily recognize a ball and by utilizing a template comparable algorithm, one can recognize its circular framework. If the ball was crushed, the shape would definitely change and this means that utilizing the same strategy would no longer be viable.

This is referred to as deformation and it can cause challenges in various robotic vision approaches. Articulation defines the deformations that result from movable joints. For instance, every time you bend your arm at the elbow the shape changes.

While the distinctive links in this case the bones are not altered, the framework becomes deformed. Many algorithms utilize the framework of a shape and articulation creates challenges when it comes to object detection.

Orientation and Position

Among the prevailing robotic vision system functions is to recognize a known object’s orientation and position. This means that the challenges facing both orientation and position are often conquered in many integrated vision solutions.

Recognizing the position of an object is often a straightforward task provided the whole object can be perceived in the camera image. Many systems are sturdy to alterations in object orientation. Still, all orientations are not equal.

While detecting an object that is pivoted along an axis is simple, recognizing the same object after it has undergone several 3D spins is a difficult task.


Occlusion defines an object’s covered part. Occlusion is different from other challenges because one part is invisible. A vision system is not capable of recognizing hidden or missing objects.

Occlusion could occur due to various reasons such as bad positioning of the camera, robot parts, or other objects. This can be conquered by pairing the object’s visible parts with its recognizable model and imagining its presence.


The background of an object determines whether its detection process will be easy or difficult. For instance, if a sheet of paper has some printed images and a similar image is placed on top of the paper, the robotic vision frameworks may be incapable of detecting the real object.

The ideal background is usually blank and it provides an excellent contrast with the recognized figures. Its actual characteristics depend on the vision recognition algorithms utilized. Where an edge-detector is utilized background should be clear. The color and background brightness should vary from that of the object.


The human eye is susceptible to trickery by scale variations in many situations and this can also happen to robotic vision systems. Assume that you are in possession of two objects with almost identical features save for the fact that one is smaller than the other.

Now assume you are utilizing a 2D vision framework which apart from being fixed utilizes the object size to establish its distance to the robot. In the event the system has been programmed to detect the smaller images, it is likely to err by recording that the bigger image is closer to the camera. Pixel values are another challenge in this context. When the cobot camera is situated farther away, fewer pixels represent the object with minimal omissions.


Understanding robotic vision helps one exploit technology to the fullest. Utilize the available resources online to gain knowledge and become conversant with these systems which are increasingly becoming necessary for efficient business operations.

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