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A Look at the Current Challenges of Robot Vision AI

A machine with a robot vision system can do a lot for your manufacturing facility. Machines with a 3D robot vision camera, for instance, can detect the location of a product or component, and determine its precise orientation. Additionally, the camera may provide workers with a range of viewing angles to aid in assembly inspection.

Despite the things that robot vision brings to the table, several factors may affect machine vision in the workplace, task configuration, and the environment. Taking note of these challenges will help you make proper adjustments to your robots and make your machines easier to use.

Here are a few of the challenges you can find in robot vision solutions:

Speed and Movement of an Object

Movement-related issues, such as blurring, could cause problems in a robot with a built-in vision system. Blurring, for instance, is the result of a product or component moving too fast on a conveyor.While an image sensor can capture photos over a short period, this electronic device may be unable to obtain the whole picture straightaway.

As much as possible, keep the movement of the objectsat a reasonable speed. Robot vision AI works best when it’s able to get a static and clear image.

The Positioning of the Camera

The improper placement of a camera could cause pixelation. Getting the positioning correct, therefore, is essential to avoid this problem.

When setting up your robot vision camera for the first time, get rid of any distracting objects or backgrounds between the viewing surface and the electronic device. Place the camera in an area with a clear view of the products or components, and good lighting. If you can, put the electronic device close to your intended objects without causing occlusion.

Covered Objects

A portion of a product or component that isn’t visible to the vision system may pose a problem. Your robot vision solution will have difficulty detecting objects when an obstruction is covering up or blocking off an area of a product. This type of situation is called occlusion.

Various factors can contribute to occlusion, including incorrect camera setup and foreign objects blocking the sensor’s line of sight. The good news is you can overcome this hurdle by matching the replica or known model of a product to the visible components of the object. The machine will then “fill the gap” by assuming that the blocked-off portion of the product is present.

Scaling

Differences in scale could play tricks on the human eye. This confusion or visual illusion may also occur in robot vision systems.

Let’s say you have two products with similar characteristics. One of these objects, however, is larger than the other. If you’re using a 2D vision system that determines the size of the product by calculating the distance of an object from the electronic device, the robot may come into the incorrect conclusion that the two products are the same in terms of size. When fixing this scaling hurdle, avoid configuring the system to recognize which object is smaller than the other.

Image Background

The background seen by a robot vision camera can affect how the electronic device detects an object. If a product, for instance, is sitting on a background with printed photos of that product, the robot vision system may have a hard time differentiating the real object from the background image.

The solution to this background challenge is easy. Simply opt for a blank background that provides you with an excellent contrast with the objects for detection. On top of that, remember to differentiate the brightness and the color of the background with the color and the brightness of the product.

Orientation and Position

Identifying the position and orientation of a known object is one of the most common functions of a machine with a robot vision camera. Detecting the location of a product or component is easy — provided that the whole object is visible within the camera image. If the orientation of the object changes, such as a component undergoing a range of 3D rotations, occlusion may occur. Detecting a component or product in this instance, therefore, may be difficult. If this is the case, keep the position of the object as consistent as possible.

Object Deformation or Articulation

Smart factoryDeformed shapes can be problematic for some robot vision systems. If your manufacturing plant is producing ball-shaped products, for instance, the camera can identify the object correctly by using a template-matching algorithm or detecting the circular outline.Unfortunately, the electronic device won’t be able to recognize a squished or flattened ball due to the drastic change in shape.

Apart from deformation, object articulation can make the recognition process more difficult for your robot vision AI.Take the shape of the human arm as an example. The shape changes when you bend the arm. Though the bones retain their shape, the outline itself undergoes deformation.

Keeping the shape of the object consistent, therefore, can help you avoid these problems.

Lighting

Poor lighting conditions can be disastrous for vision robots, as their imaging sensors aren’t sensitive like the human eye. A vision system will have trouble detecting or recognizing objects correctly with lousy lighting.

When overcoming this lighting challenge, you have a few options. First, you could adjust the lighting conditions in your facility, such as replacing defective bulbs, to allow the sensor to pick up objects properly. Second, you have the option to install dynamic lighting onto the vision robot. Third, you could introduce technologies that utilize other forms of light, such as lasers and infrared lighting.

Unrealistic Expectations

This particular challenge is less concerned about technical aspects and more with your approach to a robot vision system. Your manufacturing business will hit a roadblock if the workforce has unreasonable expectations on what a vision robot should do during production.

The solution here is simple: Make sure that your expectations are in line with the technology of your robot vision camera. This way, you avoid frustrations and get the most out of the technology. You can achieve this by educating your workers adequately about the robot vision system you plan to introduce to your manufacturing plant.

Do you need a device equipped with a state-of-the-art, built-in vision system? Techman Robot has solutions that can satisfy your requirements. Our TM Robot Series have a range payload and arm reach, equipped with a smart vision sysem that can overcome many visual tasks, such as barcode identification, image enhancement, and vision recognition.

Contact us today for a free consultation.