What is Robotic Vision? | 5+ Important Applications

Robotic Vision

The subject of discussion: Robotic Vision/Robot Vision and its features

Robot Vision | Robot Vision System

What do you mean by Robot Vision? | Robot Vision System Definition

The method of processing, characterizing, and decoding data from photographs leads to vision-based robot arm guidance, dynamic inspection, and enhanced identification and component position capability, called Robot Vision or Robotic Vision. The robot is programmed through an algorithm, and a camera, either fixed on the robot or in a fixed location, captures picture of each workpiece with which it can communicate.

The function of robotic vision was created in the 1980s and 1990s. Engineers devised a method for teaching a robot to see. The piece is rejected if it does not complement the formula, and the robot can not deal with it. This are most commonly applied in material handling and selection applications in packing industry , pick and placing, deburring, grinding, and other industrial process.

Vision Guided Robotic Systems | Vision and Robotics

Robotic vision is one of the most recent advancements in robotics and automation. In essence, robotic vision is a sophisticated technology that aids a robot, usually, an autonomous robot, in better-recognizing items, navigating, finding objects, inspecting, and handling parts or pieces before performing an application.

Vision Algorithms for Mobile Robotics

Robotic vision typically employs various sophisticated algorithms, tuning, and temperature sensing sensors, many of which have differing degrees of sophistication and implementation. Robotic perception is continually evolving and progressing in smoother ways, just as technology increasingly advances in complexity.

This cutting-edge but simplistic technology will reduce operating costs and provide a simple solution for all forms of automation and robotic needs. When equipped with robotic vision technology, robots working side by side will not collide. Human employees would be safer as well, so the robots will be able to “sense” any workers that are in the way.

The robotic vision mechanism consists of two basic steps:

Imaging:

Scanning or “reading” is done by the robot using its vision technology. This basically scans 2D objects such as lines and barcodes and 3D and X-ray imaging for inspection purposes.

Image Processing:

The robot “thinks about” the entity or image after it has been detected. This processing includes identification of image’s edge, detects the existence of an interruption, pixel counting,  manipulation of objects as per requirements, pattern recognition, and processing it as per its program.

Architecture of Robotic Vision System

Every Robotic Vision System works under the following six-step architecture:

  • Sensing – Process that yields a visual image.
  • Pre-processing – Noise reduction, enhancement of details.
  • Segmentation – Partitioning of an image into an object of interest.
  • Description – Computation of features to differentiate objects.
  • Recognition – Process to identify objects.
  • Interpretation – Assigning meaning to a group of objects.

Robotic Vision System Block Diagram

robotic vision
Image Credits: RoboticsBible

Robot Vision Applications

Robots are static and limited to executing pre-determined pathways in highly regulated settings without a vision system. A robotic vision system’s fundamental goal is to allow for slight variations from pre-programmed paths while keeping output going.

Robots may account for variables in their work environment if they have a sound vision system. Parts don’t have to be shown in the same order. And when conducting in-process inspection operations, the robot may ensure it is performing the mission correctly. When industrial robots are fitted with sophisticated vision systems, they become even more dynamic. The primary motivation towards application of robotic vision systems is flexibility.

Robots with robotic vision can perform a variety of activities, including:

  • Taking measurements
  • Scanners and reading barcodes
  • Inspection of engine parts
  • Inspection of the packaging
  • Assessment of the consistency of the wood
  • Examination of the surface
  • Orientation of modules and parts is directed and verified
  • Defect Inspection

Computer Vision in Robotics and Industrial Applications

Computer vision is an interdisciplinary research discipline that studies how computers can interpret artificial images or videos at a high level. From an engineering standpoint, it aims to comprehend and simplify functions that the human visual system can.

Methods for collecting, encoding, interpreting, and interpreting visual images and the retrieval of high-dimensional data from the physical world to obtain numerical or symbolic knowledge, such as in the form of decisions, are both examples of computer vision tasks.

In this case, understanding refers to converting visual representations (retinal input) into world descriptions that make sense to thought processes and evoke effective action. The untying of symbolic knowledge from image data utilizing specific multi domain models built with the help of geometry, physics, statistics, and learning theory known as image comprehension.

The philosophy behind artificial systems that derive knowledge from images is the subject of computer vision, a scientific discipline. Video loops, different camera views, multi-dimensional data from a 3D printer, or medical scanning data are also examples of image data and computer vision is a field of science that aims to apply its ideas and models to the development of it.

Application of Computer Vision in Robotics

Industrial machine vision devices, for example, that inspect bottles speeding past on a manufacturing line, to artificial intelligence research and machines or robotics that can comprehend the world around them are examples of applications. Computer vision is a broad term that refers to the fundamental technology of digital image processing, which is used in various applications.

Computer vision devices may be used for a variety of purposes, including:

  • Automatic inspection in manufacturing applications
  • Using a species recognition device to assist humans with identification activities
  • Controlling processes with the precision of an automotive robot
  • Detecting activities to conduct video monitoring or count the number of participants
  • Interaction between computers and humans
  • Medical image processing or topographical modelling
  • Navigation of a self-driving car or a mobile robot
  • Organizing files, such as indexing image and image sequence databases

Robotic Vision vs Computer Vision

Robot Vision or Robotic Vision is closely linked to Machine Vision. They have a lot in common when it comes to Computer Vision. Computer Vision might be considered their “father” if we talked about a family tree. However, to comprehend where they all blend into the universe, we must first add the “grandparent” – Signal Processing.

Signal processing entails cleaning up electronic signals, extracting information, preparing them for display, or converting it. Something, in any sense, maybe a warning. Images are essentially a two-dimensional (or more) signal.

Robot Vision in Digital Image Processing

While Computer Vision and Image Processing are cousins, their goals are very different. Image conversion methods are mainly used to increase the accuracy of an image, transform it to a different format (such as a histogram), or modify it in some other way in preparation for further processing. On the other hand, computer vision is more concerned with removing detail from images to make sense.

So far, it has been so straightforward. When we add Pattern Recognition, or more broadly, Machine Learning, things tend to get a bit more complicated to the family tree. This family division is focused on identifying trends in data, which is critical for many of Robot Vision’s more advanced functions. As a result, Machine Learning, like Signal Processing, is another parent in Computer Vision.

All improve as we get to Machine Vision. This is because Machine Vision is more concerned with basic implementations than with techniques. Machine vision is the use of vision in the manufacturing industry for automated monitoring, process controlling, and robotic guidance purpose. Machine Vision is an engineering domain, while the rest of the “family” are science domains.

Finally, we come to Robotic Vision or Robot Vision, which combines all of the previous words’ strategies and Robot vision and Machine Vision are also used interchangeably as per requirements. Furthermore, Robot Vision is not solely a technical area. It is a discipline with its own collection of study areas. Unlike pure Computer Vision science, Robot Vision methods and algorithms must integrate elements of robotics, such as kinematics, reference frame calibration, and the robot’s capacity to influence the environment physically.

What is Machine Vision in Robotics?

Machine Vision system in Robotics

Machine vision (MV) refers to the technologies and techniques that are used in manufacturing to provide imaging-based automated inspection and interpretation for applications ( i.e., automatic inspecting, process controlling, and robotic guidance etc. ) and this encompasses with wide range of technology, starts from software and hardware, interconnected processes, behaviour, practises, and experience.

To escape annoyance, dissatisfaction, and heartbreak for the user and unpleasant surprises for the application developer, each one must be carefully considered. The four primary stages are:

  • Image Acquisition
  • Information extraction from the image
  • Information Analysis
  • Result communication

As a branch of systems engineering, machine vision is separate from machine vision, which is a branch of computer science. It tries to combine existing innovations in novel ways to adapt them to real-world problems. The concept is most commonly used for these functions in industrial automations, protection and safety purpose and self driven car to vehicle guidance applications.

What are the four basic types of Machine Vision System?

To satisfy the demands of your individual vision applications, you must choose the correct vision system. The basic machinne vision system are

  • 1D Vision System
  • 2D Vision System
  • Line Scan or Area Scan system
  • 3D Vision System

1D Vision Systems

Instead of staring at the whole image at once, 1D vision analyses a digital signal one line at a time, such as comparing the variance between the most current set of ten obtained lines and an older group. This method is widely used to identify and classify defects in continuous-process materials.( i.e., paper, clothing, metals, plastics, sheet or roll products)

2D Vision Systems | 2D Robot Vision

Area scans, which include taking 2D snapshots in different resolutions, are performed by the majority of inspection cameras. Line scan is a form of 2D machine vision that creates a 2D image line by line.

Line Scan or Area Scans

Line scan systems have distinct benefits over field scan systems in many applications. Inspecting circular or cylindrical sections, for example, can necessitate the use of multiple area scan cameras to cover the entire component surface and revolving the portion in front of a single line scancamera, on the other hand, unwraps the image and catches the whole surface.

Where the camera would peer through rollers on a conveyor to see the bottom of a section, line scan systems fit more conveniently into narrow spaces; in general, line scan cameras have a significantly higher resolution than conventional cameras. Since line scan systems rely on moving parts to create a picture, they’re ideal for constantly moving goods.

3D Vision Systems | 3D Robot Vision Systems

Many cameras or one or more laser displacement sensors are commonly used in 3D computer vision systems. In robotic guidance systems, multi-camera 3D vision offers aspect orientation knowledge to the robot. Multiple cameras are installed at various positions, and “triangulation” on an objective point in 3-D space is used in these systems.

Types of Vision Sensors used in Robotics

Robotic Vision sensor applications are multi-component devices with a lot of moving parts. There are constant advancements in this area. Smart cameras, with its frequent application in vehicle recognition systems, will be the most common vision sensors for many. On the other hand, vision sensors are commonly used in industry to track operations and ensure product safety.

There are two kinds of robotic vision sensors, each of which can be modified for various purposes:

  • Orthographic projection-type: The rectangular field of view of orthographic projection-type robotic vision sensors is the most common. They’re ideal for infrared sensors with short-range or laser range finders.
  • Perspective projection-type: The field of view of robotic vision sensors that use perspective projection has a trapezoidal shape. They’re ideal for sensors that are used in cameras.

Robotic Arm Computer Vision

Robotic Arm; Image Credits : seekpng

Universal Robot Vision System

Image Credits: Stemmer Imaging

Stereo Vision Robot

Image Credits: Boredom Projects

Welding Robot Vision System

Robot Welding; Image Credits: Robots.com

The Computerized vision algorithms and vision sensors, such as laser based range-finder and photo-metric cameras ( with silicon based multi-channel array detector of UV, visible and near-infra light popularly known as CCD array), are utilized in vision based navigation or optical navigation system to extract the visual features necessary for localization purposes. However, there are a variety of vision based navigation and localization techniques available, with the critical components of each technique being:

  • Representation of the environment.
  • Sensing model.
  • Localization algorithm.

The vision based navigation has been categorized into two types:

  • Indoor Navigation.
  • Outdoor Navigation.

About Esha Chakraborty

I have a background in Aerospace Engineering, currently working towards the application of Robotics in the Defense and the Space Science Industry. I am a continuous learner and my passion for creative arts keeps me inclined towards designing novel engineering concepts.
With robots substituting almost all human actions in the future, I like to bring to my readers the foundational aspects of the subject in an easy yet informative manner. I also like to keep updated with the advancements in the aerospace industry simultaneously.

Connect with me with LinkedIn - http://linkedin.com/in/eshachakraborty93

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