WHAT IS EMBEDDED VISION?

 

March 2019

  

In recent years, a miniaturization trend has been established in many areas of electronics. For example, ICs have become more and more integrated and circuit boards in the electrical industry have become smaller and more powerful. This has also made PCs, mobile phones and cameras more and more compact and also more powerful. This trend can also be observed in the world of vision technology.

A classic machine vision system consists of an industrial camera and a PC:

Both were significantly larger a few years ago. But within a short time, smaller and smaller-sized PCs became possible, and in the meantime, the industry saw the introduction of single-board computers (SPCs), i.e. computers that can be found on a single board. At the same time, the camera electronics became more compact and the cameras successively smaller. On the way to even higher integration, small cameras without housings are now offered, which can be easily integrated into compact systems.

Due to these two developments, the reduction in size of the PC and the camera, it is now possible to design highly compact camera vision systems for new applications. These systems are called embedded (vision) systems.

Design and use of an embedded vision system

An embedded vision system consists, for example, of a camera, so-called board level camera, which is connected to a processing board. Processing boards take over the tasks of the PC from the classic machine vision setup. As processing boards are much cheaper than classic industrial PCs, vision systems can become smaller and also more cost-effective. The interfaces for embedded vision systems are primarily USB, Basler’s BCON for MIPI or BCON for LVDS.

Embedded vision systems are used in a wide range of applications and devices, such as in medical technology, in vehicles, in industry and in consumer electronics. Embedded systems enable new products to be created and thereby create innovative possibilities in several areas.

Which Embedded Systems Are Available?

A so-called SoC (system on chip) lies at the heart of all embedded processing solutions. This is a single chip on which the CPU (which may be multiple units), graphics processors, controllers, other special processors (DSP, ISP) and other components are integrated.

Due to these efficient SoC components, embedded vision systems have become available in such a small size and at a low cost only recently.

As embedded systems, there are popular single-board computers (SBC), such as the Raspberry Pi® or DragonBoard®. These are mini-computers with the established interfaces (USB, Ethernet, HDMI, etc.) and a range of features similar to traditional PCs or laptops, although the CPUs are of course less powerful.

Embedded vision solutions can also be designed with a so-called SoM (system on module, also called computer on module or CoM). In principle, an SoM is a circuit board which contains the core elements of an embedded processing platform, such as the SoC, storage, power management, etc. An individual carrier board is required for the customization of the SoM to each application (e.g. with the appropriate interfaces). This is connected to the SoM via specific connectors and can be designed and manufactured relatively simply. The SoMs (or the entire system) are cost-effective on the one hand since they are available off-the-shelf, while on the other hand they can also be individually customized through the carrier board.

Completely individual processing boards in the form of a full custom design may also be a sensible choice for high quantities.

Characteristics of Embedded Vision Systems versus Standard Vision Systems

Most of the above-mentioned single board computers and SoMs do not include the x86 family processors common in standard PCs. Rather, the CPUs are often based on the ARM architecture.

The open-source Linux operating system is widely used as an operating system in the world of ARM processors. For Linux, there is a large number of open-source application programs, as well as numerous freely-available program libraries. Increasingly, however, x86-based single-board computers are also spreading. A consistently important criterion for the computer is the space available for the embedded system.

For the software developer, the program development for an embedded system is much more complex than for a standard PC. While the PC used in standard software development is also the main target platform (meaning the type of computer which the program is later intended to run on), this is different in the case of embedded software, where the target system generally can’t be used for the development due to its limited resources (CPU performance, storage). This is why the development of embedded software also uses a standard PC on which the program is coded and compiled with tools that may get very complex. The compiled program must then be copied to the embedded system and subsequently be debugged remotely.

When developing the software, it should be noted that the hardware concept of the embedded system is oriented to a specific application and thus differs significantly from the universally usable PC.

However, the boundary between embedded and desktop computer systems is sometimes difficult to define. Just think of the popular Raspberry Pi, which on the one hand has many features of an embedded system (ARM-based, single-board construction), but on the other hand can cope with very different tasks and, with the connection of a monitor, mouse and keyboard, is therefore a universal computer.

What Are the Benefits of Embedded Vision Systems?

In some cases, much depends on how the embedded vision system is designed. An SBC (single-board computer) is often a good choice, as this is a standard product. It is a small compact computer that is easy to use. This solution is also useful for developers who have had little to do with embedded vision.

On the other hand, however, the single-board computer is a system which contains unused components and thus generally does not allow the leanest system configuration. For that reason, this approach is not very economical in terms of manufacturing costs and is more suitable for small unit numbers, where the development costs must be kept low while the manufacturing costs are of secondary importance.

The leanest setup is obtained with a full-custom design, a system that is highly optimized for individual applications. But this involves high integration costs and the associated high development expenditures. This solution is therefore suitable for large unit numbers.

An approach with a conventionally available system on module (SoM) and an appropriately customized carrier board presents a compromise between an SBC and a full-custom design (also see above: “Which embedded systems are available? “). The manufacturing costs are not as optimized as in a full custom design (after all, a setup with a carrier board plus a more or less generic SoM is a bit more complex), but at least the hardware development costs are lower, since the significant part of the hardware development is already completed with the SoM. This is why a module-based approach is a very good choice for medium-level unit numbers in which the manufacturing and development costs must be well-balanced.

The benefits of embedded vision systems at a glance:

• Leaner system design
• Light weight
• Cost-effective, because there is no unnecessary hardware
• Lower manufacturing costs
• Low energy consumption
• Small footprint

Also Read: HOW HIGH-SPEED CAMERAS CAN BE USED TO STUDY HUMAN MOTION SEQUENCES

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BITFLOW PREDICTS VISION-GUIDED ROBOTICS TO BECOME MAJOR DISRUPTIVE FORCE IN GLOBAL MANUFACTURING

JANUARY, 2019  ARTICLE

As the plant floor has become more digitally connected, the relationship between robots and machine vision has merged into a single, seamless platform, setting the stage for a new generation of more responsive vision-driven robotic systems. BitFlow, Inc., a global innovator in frame grabbers used in industrial imaging, predicts vision-guided robots will be one of the most disruptive forces in all areas of manufacturing over the next decade.

"Since the 1960s robots have contributed to automation processes, yet they've done so largely blind," said Donal Waide, Director of Sales for BitFlow, Inc. "Vision-equiped robots are different. Now, just like a human worker, robots can see a specific part to validate whether it is being placed correctly in a pick and place application, for example. Cost savings will be realized since less hard fixturing is required and the robot is more flexible in its ability to locate a variety of different parts with the same hardware."

HOW ROBOTIC VISION WORKS

Using a combination of camera, cables, frame grabber and software, a vision system will identify a part, its orientation and its relationship to the robot. Next, this data is fed to the robot and motion begins, such as pick and place, assembly, screw driving or welding tasks. The vision system will also capture information that would be otherwise very difficult to obtain, including small cosmetic details that let the robot know whether or not the part is acceptable. Error-proofing reduces expensive quality issues with products. Self-maintenance is another benefit. In the event that alignment of a tool is off because of damage or wear, vision can compensate by performing machine offset adjustment checks on a periodic basis while the robot is running.

DUAL MARKET GROWTH

In should come as no surprise that the machine vision and robotic markets are moving in tandem. According to the Association for Advancing Automation (A3), robot sales in North America last year surpassed all previous records. Customers purchased 34,904 total units, representing $1.896 billion in total sales. Meanwhile total machine vision transactions in North America increased 14.8%, to $2.262 billion. The automotive industry accounts for appoximately 50% of total sales.

THE ROLE OF FRAME GRABBERS

Innovations in how vision-guided robots perceive and respond to their environments are exactly what manufacturers are looking for as they develop automation systems to improve quality, productivity and cost efficiencies. These types of advancements rely on frame grabbers being paired with high-resolution cameras to digitize analog video, thus converting the data to a form that can be processed by software.

BitFlow has responded to the demands of the robotics industry by introducing frame grabbers based on the CoaXPress (CXP) machine vision standard, currently the fastest and most powerful interface on the market. In robotics applications, the five to seven meters restriction of a USB cable connection is insufficient. BitFlow CXP frame grabbers allow up to 100 meters between the frame grabber and the camera, without any loss in quality. To minimize cabling costs and complexity, BitFlow frame grabbers require only a single piece of coax to transmit high-speed data, as well as to supply power and send control signals.

BitFlow's latest model, the Aon-CXP frame grabber, is engineered for simplified integration into a robotics system. Although small, the Aon-CXP receives 6.25 Gb/S worth of data over its single link, almost twice the real world data rate of the USB3 Vision standard and significantly quicker than the latest GigE Vision data rates. The Aon-CXP is designed for use with a new series of single-link CXP cameras that are smaller, less expensive and cooler running than previous models, making them ideal for robotics.

Also Read: AN INTRODUCTION TO MACHINE VISION SYSTEMS

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AN INTRODUCTION TO MACHINE VISION SYSTEMS

Machine vision is the incorporation of computer vision into industrial manufacturing processes, although it does differ substantially from computer vision. In general, computer vision revolves around image processing. Machine vision, on the other hand, uses digital input and output to manipulate mechanical components. Devices that depend on machine vision are often found at work in product inspection, where they often use digital cameras or other forms of automated vision to perform tasks traditionally performed by a human operator. However, the way machine vision systems ‘see’ is quite different from human vision.

THE COMPONENTS OF A MACHINE VISION SYSTEM CAN VARY, BUT THERE ARE SEVERAL COMMON FACTORS FOUND IN MOST. THESE ELEMENTS INCLUDE:

• - Digital or analog cameras for acquiring images


• - A means of digitizing images, such as a camera interface


• - A processor

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When these three components are combined into one device, it’s known as a smart camera. A machine vision system can consist of a smart camera with the following add-ons:

• - Input and output hardware


• - Lenses


• - Light sources, such as LED illuminators or halogen lamps


• - An image processing program


• - A sensor to detect and trigger image acquisition


• - Actuators to sort defective parts

HOW MACHINE VISION SYSTEMS WORK

Although each of these components serves its own individual function and can be found in many other systems, when working together they each have a distinct role in a machine vision system.

To understand how a machine vision system works, it may be helpful to envision it performing a typical function, such as product inspection. First, the sensor detects if a product is present. If there is indeed a product passing by the sensor, the sensor will trigger a camera to capture the image, and a light source to highlight key features. Next, a digitizing device called a framegrabber takes the camera’s image and translates it into digital output, which is then stored in computer memory so it can be manipulated and processed by software.

In order to process an image, computer software must perform several tasks. First, the image is reduced in gradation to a simple black and white format. Next, the image is analyzed by system software to identify defects and proper components based on predetermined criteria. After the image has been analyzed, the product will either pass or fail inspection based on the machine vision system’s findings.

GENERAL APPLICATIONS

Beyond product inspection, machine vision systems have numerous other applications. Systems that depend on visual stock control and management, such as barcode reading, counting, and store interfaces, often use machine vision systems. Large-scale industrial product runs also employ machine vision systems to assess the products at various stages in the process and also work with automated robotic arms. Even the food and beverage industry uses machine vision systems to monitor quality. In the medical field, machine vision systems are applied in medical imaging as well as in examination procedures.

Also Read: A LOOK AT THE PROGRESSION OF MACHINE VISION TECHNOLOGY OVER THE LAST THREE YEARS

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A LOOK AT THE PROGRESSION OF MACHINE VISION TECHNOLOGY OVER THE LAST THREE YEARS

Machine vision represents a diverse and growing global market, one that can be difficult to keep up with, in terms of the latest technology, standards, and product developments, as they become available from hundreds of different organizations around the world. 

If you are looking for an example of how fast the market moves, and how quickly trends and new technologies emerge, our Innovators Awards provides a good reference point. In 2015, we launched our first annual Innovators Awards program, which celebrates the disparate and innovative technologies, products, and systems found in the machine vision and imaging market. In comparing the products that received distinction in 2015 to this past year’s crop of honorees, it does not take long to draw some obvious conclusions. First, let’s start with the most noticeable, which was with the cameras that received awards. 

In 2015, five companies received awards for cameras. These cameras performed various functions and offered disparate capabilities, including pixel shifting, SWIR sensitivity, multi-line CMOS time delay integration, high-speed operation, and high dynamic range operation. In 2018, 13 companies received awards for their cameras, but the capabilities and features of these cameras look much different.

CAMERAS THAT RECEIVED AWARDS IN 2018 OFFERED THE FOLLOWING FEATURES:

Polarization, 25GigE interface, 8K line scan, scientific CMOS sensor, USB 3.1 interface, fiber interface, embedded VisualApplets software, 3-CMOS prism design, and subminiature design. Like in 2015, a few companies were also honored for high-speed cameras, but overall, it is evident that most of the 2018 camera honorees are offering much different products than those from our inaugural year.

There are two other main categories that stick out, in terms of 2018 vs. 2015, the first of which is software products. In 2015, two companies received awards for their software—one for a deep learning software product and another for a machine learning-based quality control software. In 2018, eight companies received awards for software.

THESE SOFTWARE PRODUCTS OFFERED THE FOLLOWING FEATURES OR CAPABILITIES:

Deep learning (three honorees), data management, GigE Vision simulation, neural network software for autonomous vehicles, machine learning-based desktop software for autonomous vehicle vision system optimization, and a USB3 to 10GigE software converter. 

Lastly, the category of embedded vision looked much different in 2018 than it did in 2015. In the embedded vision category—which I am combining with smart cameras due to overlap—there were two companies that received awards in 2015, both of which were for smart cameras that offered various capabilities. This year, however, there were 12 companies that were honored for their embedded vision innovations, for products that offered features including: embedded software running on Raspberry Pi, computer vision and deep learning hardware and software platform, embedded vision development kits, embedded computers, 3D bead inspection, as well as various smart cameras. 

Throughout the other categories, there was equal or similar number of honorees from both years, but there were several interesting technologies or applications that products that popped up in 2018 offered. This includes a lens for virtual reality/augmented reality applications, a mobile hyperspectral camera, a 3D color camera, and various lighting products that targeted multispectral and hyperspectral imaging applications. 

This is all to say that, when looking back to 2015 to today, machine vision technology has grown quite a bit. With the rapid pace of advancements, the growing needs of customers and end users, the miniaturization and smaller costs of components, and so on; it is exciting to think about what machine vision products in 2021 might look like.

Also Read: FIVE MYTHS ABOUT ROBOTIC VISION SYSTEMS.

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FIVE MYTHS ABOUT ROBOTIC VISION SYSTEMS

Vision systems for robotic manufacturing applications have significantly evolved over the last decade. While the vision systems of old were unreliable, clunky and expensive, today’s systems are anything but. Proper vision systems can make the difference between an efficient robotic system and one that is not working optimally.

HERE ARE 5 MYTHS AND TRUTHS ABOUT VISION SYSTEMS.

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MYTH #1: VISION SYSTEM ARE COMPLICATED

In actuality, modern vision systems are very simple to install and use. Most of the algorithms and communications are built in, so it can be very easy and quick to make adjustments without the help of a trained engineer. New users are often surprised just how easy it is to use and maintain their vision systems.

MYTH #2: VISION SYSTEMS ARE NOT RELIABLE

If a vision system is properly applied, it will be highly robust, repeatable and reliable. Today’s vision system components are very robust, even in harsh environments. They are built to operate in rugged applications. Unlike a human, a vision system will see accurately every time. It never gets tired, takes a break or goes home for the evening.

MYTH #3: ALL VISION SYSTEMS ARE THE SAME

There is no truly out-of-the-box solution for vision systems. Each application is unique, and many factors of need to be considered. Anyone who tells you there’s a plug-and-play option for your operations is not selling you a solution that’s properly engineered for your needs. Customized vision systems are the only ones that will work efficiently and reliably.

MYTH #4: VISION SYSTEMS ARE ALWAYS THE BEST SOLUTION

While vision systems are helpful in many robotic applications, there are some jobs in which vision may not be the answer. For example, operations that have drastic changes from part to part moving quickly on a single line may not benefit from a vision system because more discriminating inspection may be necessary. In addition, a vision system helps provide tight tolerances, so applications with loose tolerances may be just fine with sensors and not need to be upgraded to a vision system.

MYTH #5: VISION SYSTEMS ARE TOO EXPENSIVE

Just 10 years ago, typical vision systems cost an average of $30,000. Today, that same system may cost only $5,000 to $15,000. The evolution of vision technologies have brought down the cost considerably. In fact, many companies can see an ROI relatively quickly because a vision system requires fewer special fixtures and conveyors, decreases downtime for fixture changeout, and increases operations overall.

An efficient manufacturer must get products in and out of a cell quickly and reliably. Vision systems paired with robotic operations can put an operation at a competitive advantage by providing opportunities to make more and streamline the process for optimum profitablilty.

Also read: MACHINE VISION TRENDS TO WATCH IN 2018 AND BEYOND

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MACHINE VISION TRENDS TO WATCH IN 2018 AND BEYOND

MACHINE VISION TECHNOLOGY has found its way into applications inside and outside of factory settings, riding a wave of progress in automation technology and growing into a sizable global industry. Quite a bit of future technology will depend on machine vision, and the market will grow accordingly.

In 2017, according to a recent report, the global machine vision market was valued at $7.91 billion1. By 2023, the global market is expected to reach $12.29 billion – a compound annual growth rate (CAGR) of 7.61%. This robust growth is caused by a number of broader economic factors.

WHAT’S DRIVING LONG-TERM GROWTH IN MACHINE VISION?

The main drivers of growth in the machine vision market are the need for quality inspection and automation inside factories, growing demand for AI and IoT integrated systems that depend on machine vision, increasing adoption of Industrial 4.0 technology that uses vision to improve the productivity of robotic automation, and government initiatives to support smart factories across the globe.

Machine vision software will be one of the fastest growing segments between 2017 and 2023. The main reason for this is the expected increase in integration of AI into industrial machine vision software to enable deep learning in robotics technology.

PC-based INDUSTRIAL MACHINE VISION PRODUCTS, the oldest form of industrial machine vision, will retain a large portion of machine vision market share because of their ease of use and processing power.

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WHAT TRENDS ARE WORTH WATCHING NOW?

While there are several main factors in the expected long-term growth of the global machine vision market, there are a few trends to keep an eye on now that are changing the way machine vision technology is deployed.

•  Industrial Internet of Things (IIoT): while AI and IoT technology are long-term drivers of growth, the IIoT is connecting production technology with information technology in today’s factories to increase productivity. The IIoT depends on heavily on machine vision to collect the information it needs.


•  Non-Industrial Applications: driverless cars, autonomous farm equipment, drone applications, intelligent traffic systems, guided surgery and other non-industrial uses of machine vision are rapidly growing in popularity, and often call for different functionality in machine vision than industrial applications. These non-industrial uses of machine vision are being deployed today and could be an important part of machine vision growth.


•  Ease of Use: Machine vision systems can often be complex from the user’s perspective. As mentioned above, PC-based machine vision systems will remain popular, despite their age, because of their ease of use. The desire for ease of use may drive further standardization in machine vision products, which could make them even easier to deploy inside and outside of factory settings.

The machine vision market is poised for long-term growth. The IIoT, growing non-industrial applications and ease of use are all helping buoy today’s machine vision market, but there are several other factors effecting long-term market expansion. 

With market growth comes innovation. There’s EXCITING THINGS ON THE HORIZON FOR MACHINE VISION AND VISION TECHNOLOGY.

Also read: LENS SEES CLEARLY EVEN THROUGH SHOCK AND VIBRATION

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LENS SEES CLEARLY EVEN THROUGH SHOCK AND VIBRATION

Many applications demand more from a lens than the performance delivered by STANDARD IMAGING LENSES, which introduces the need for ruggedized imaging lenses. A new imaging choice is the Stability Ruggedized imaging lens.

JESSICA GEHLHAR, VISION SOLUTIONS ENGINEER, AND CORY BOONE, OPTICAL ENGINEER, EDMUND OPTICS

Historically, Industrial and Ingress Protection Ruggedized imaging lenses have addressed many environmental and application challenges. But as imaging systems increase the number of moving elements and as products move through inspection systems faster for higher throughput, these movements require greater calibration and image performance. As applications like factory automation, measurement, robotics and autonomous vehicles continue to expand and develop, the need for Stability Ruggedized imaging lenses will increase along with the progression of the industry.

Each of these applications presents environmental challenges–such as shock, vibration, and contamination–to imaging systems. Unlike lab and observatory setups, which tend to have relatively controlled environments, manufacturing facilities can be rife with environmental operating difficulties.

To address these challenges, ruggedized imaging lenses have a number of features and benefits. But to determine the best ruggedized lens for an application, first let’s clearly define the various ruggedization techniques.

STANDARD IMAGING

A STANDARD IMAGING LENS can be insufficient in some applications and environments due to the large number of moving parts within the lens assembly, such as the doubled threaded focus adjustment, the multi-leaf iris diaphragms, and their corresponding thumb screws. For example, the thin overlapped iris leaves are especially susceptible to high shock and vibration, which can cause them to easily spring out of place and be damaged. By replacing the iris leaves with a fixed aperture, the survivability of the lens can be greatly improved.

Another component of the lens that may come loose during shock and vibration are the thumbscrews. Although they may not completely fall off, they can be loosened enough that the focus changes, potentially degrading the image quality. On a machine vision inspection system, faulty image quality can increase the potential to reject passing units or pass failing units. Debris and contaminants in the area can compound these effects.

RUGGEDIZATION

Historically, there have been two primary ruggedization techniques to address these environmental difficulties – Industrial Ruggedization and Ingress Protection Ruggedization.

IN AN INDUSTRIAL RUGGEDIZED IMAGING LENS, MANY OF THE MOVING PARTS OF A STANDARD IMAGING LENS ARE ELIMINATED:

• the multi-leaf iris is replaced with a fixed aperture stop
• the focus adjustment is replaced with a simple single thread
• the thumb screws are replaced with set screws.

Industrial Ruggedization prevents many of the unintentional movements and focus shifts described above, and therefore maintains ideal image quality. Industrial Ruggedized imaging lenses can also prevent a user from accidentally changing the focus and iris settings.

In an Ingress Protection Ruggedized imaging lens, the lens assembly is either fully enclosed or sealed with O-rings (or RTV silicone) to withstand environmental contaminants. IP66 or IP67 environmental ratings are the most well known standards for particulate and water resistance.

Enclosures and seals can be especially critical for lenses used to inspect food quality. The lenses must withstand direct exposure to liquids and humidity in such wash down applications.

Many manufacturing, processing, and packaging applications are in unfavorable environments where dust, debris, dirt, adhesives or fluids are commonplace. Ingress Protection Ruggedized imaging lenses are designed to withstand these harsh environments.

STABILITY RUGGEDIZATION

Some of today’s more demanding applications in factory automation, measurement, robotics, and autonomous vehicles levy additional requirements on imaging systems beyond those of Industrial Ruggedization and Ingress Protection Ruggedization. In such situations, there’s another type of ruggedization: Stability Ruggedization. The EDMUND OPTICS TECHSPEC COMPACT RUGGEDIZED (CR) SERIES FIXED FOCAL LENGTH LENSES are an example of Stability Ruggedized imaging lenses.

While Ingress Protection Ruggedization prevents contamination and Industrial Ruggedization eliminates moving parts, Stability Ruggedization maintains (or stabilizes) optical pointing and positioning even after heavy shock, vibration and temperature change. In a Stability Ruggedized imaging lens, the individual lens elements are glued into place to prevent them from moving within the housing. 

In an optical system, lens elements sit within the inner bore of the barrel. The space between the outer diameter of the lens and inner diameter of the barrel is typically less than 50 microns; decenters of even tens of microns are enough to significantly affect the pointing of the lens.

When using a Stability Ruggedized lens, if an object point falls on the exact center pixel, it will always fall there even if the lens has been heavily vibrated; therefore pixel shift is reduced and the image is stabilized.

Even in clean inspection environments with well-controlled robotic movements, there are challenges. Conveyer lines and robotic systems move at higher speeds and handle heavier products than ever before. Vibration comes from operation speeds and weight of the objects, or by the lines and systems located next to equipment. These high intensity environments create tough shock and vibration requirements for imaging systems, making the lens performance more critical.

Additionally, users have higher resolution and image quality expectations. As camera pixel sizes become smaller, even slight misalignments in imaging systems become apparent. Pointing and alignment changes that were once unnoticeable with a camera having 5.6 micron pixels may be very obvious with a camera having 1.4 micron pixels either over time or after a strong shock.

Stability Ruggedization is important in applications where the field of view must be calibrated, such as measurement equipment, 3D stereo vision, lenses used for robotic sensing, and lenses used for tracking object locations. These applications often require the pointing to be stabilized to values much smaller than a single pixel.

In 3D stereo vision, two imaging lenses are used to image a pattern that’s been projected onto a 3D object. The two images are then compared to extract the 3D information about the object, but to do this the angle of the two lenses and field of view must be well calibrated.

Once calibrated, any shift in the pixel mapping will offset the information in the 3D model, affecting the measurements. Many times these systems experience heavy shock and temperature shifts during shipping or relocation. If the system were to require recalibration for each relocation, it would be costly to send a technician onsite, whereas setting up and calibrating the system once would keep costs down.

Another similar application is distortion calibration. Information is not lost in an imaging system that has distortion – the information is simply moved. Distortion can be mapped or calibrated from your imaging systems to remove it. If pixel shift occurs, the distortion mapped is now incorrect; shifting the distortion calibration map can move your values affecting your accuracy.

Another recent challenge machine vision imaging systems face is an increase in traditional robotic imaging systems crossing over into autonomous systems, along with many embedded vision solutions beyond the relatively stationary conveyer line and robotic setups.

Vision enabled robots rely on imaging systems to know where they are in space. As robots move to perform their task, the constant movement can cause pixel shift, affecting their ability to know where they are and their accuracy. Machine vision is also expanding beyond the final pack-out robots on a line. After products are packaged and boxed up, vision-guided robots can transport and load them on and off of trucks – even the trucks themselves may be autonomous vehicles, guided by many sensors including vision.

Back in the factory and warehouses, there are more autonomous robots, driving or flying around to move products or inspect storage locations. Large distribution centers often carry a variety of goods, inspection and handling systems to handle a variety of weights, sizes, and packaging materials. As industrial machine vision systems make their way outside of typical applications and onto autonomous vehicles, once ‘controlled’ environments, no matter how challenging, are no longer controlled in many ways.

Also read: HOW DANFOSS IXA AND EDMUND OPTICS ARE CREATING A CLEANER ENVIRONMENT

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