Could’ve been perception was altered due to watching one too many Jetsons’ episode or because the rapid advancement of technology at the time brought with it high expectations. Regardless, the reality of one of those things happening wasn’t bad!

Fast forward to the present as robotics and automation are yielding unprecedented results

Today, sans flying cars and house maid robots, businesses across the globe are being transformed through robotics and automation. New technologies are constantly emerging, bringing with them an increased ability for tasks to be completely fulfilled by robotic solutions.

The shift from human performed functions to partial or fully automated tasks has been greatly influenced by the pandemic workforce exodus. With labor shortages and new hire difficulties, it’s estimated that industrial robots are now operating within warehouse, logistics and manufacturing facilities worldwide, performing repetitive tasks, simplifying manual processes and overall, improving productivity, throughput and profit.

If there has been one positive takeaway from these tumultuous times, it’s that the implementation of robotics and automation solutions have proven to mitigate workforce issues, resolve challenging production demands and garner relatively quick .

The BOOM – A bubble or sustainable option for the future?

Robotics and automated solutions are here to stay. Capabilities continue to increase and costs as related to ROI are extremely palatable, making highly coveted aspects of business-like expansion, scalability and production optimization possible.

The advent of automated technologies that allow humans to seamlessly work alongside their robotic counterparts is also a huge factor. This coupled with the ability to redeploy valuable staff to other functions within a facility makes automation even more appealing and viable for many. Plus, businesses utilizing multiple robots, cobots, AMR’s and machine vision systems throughout their facility can integrate additional technological capabilities to tie everything together – making manual tracking, sorting and managing a thing of the past and more accurate.

What does this look like in applicable terms for many industrial applications?

Automated solutions come in a variety of forms. Most common for industries such as warehousing, manufacturing and logistics operations are: Robots, Cobots, AMR’s and Machine Vision Systems. When integrated to perform automated tasks, solutions can make production processes faster and more efficient. Plus, they significantly increase the potential for companies to save time and money on everything from labor to inspection and tracking.

Move it. Pick it. Pack it. Store it. Inspect it.

Common applications of automation and robotic solutions include: Assembly, Pick and Place, Material Handling and Transport, Palletizing/Depalletizing, Packaging and more.

Robots are ideal for repetitive tasks where payload and reach may exceed human capabilities and productivity. Robust, industrial robots are reliable, easy to operate and offer a great deal of flexibility in handling many application specific functions.

Cobots, provide an equal value as robots when it comes to executing automated tasks with the added benefit of being collaborative. These systems can work alongside humans safely, are often portable and easy to configure.

AMR’s or Autonomous Mobile Robots provide industry with the ability to eliminate wasted travel time, transporting materials throughout a facility, providing material handling and cross-docking capabilities, safely and efficiently. The ability to redeploy valuable personal to other functions has been a key decision-making factor for many companies incorporating AMR’s into their automation mix.

Machine Vision Systems are a key advantage in many environments. Providing more consistency than the human eye with up to a 99% accuracy rate, they eliminate the need for manual inspections, sorting, tracking and traceability functions.

Then, now and beyond.

Technology will continue to advance, and in many aspects so will the needs of business and industry along the way. As hiring remains a challenge, wages continue to escalate and inflation doesn’t seem to be slowing down anytime soon, robotic solutions remain a cost-effective option to combat these variables.

Easy to deploy, reliable, efficient and safe with not much down time, automation makes it possible to run shifts 24/7 at maximum speed and capacity. This translates into increased productivity, process optimization and long-term profitability.

Chat with an automation expert today to see what solutions are right for you – you may be surprised at how easy, seamless and effective they can be.

A cancerous cell could appear in a number of sizes and shapes, and its various forms are, in most cases, more different than they are similar. It’s effectively impossible to teach an inspection system to identify all possible anomalies without extensive programming, and even then, the possibility of false identification or rejection is high. In a situation like this, deep learning-based image analysis in unsupervised mode offers a highly accurate and efficient mode of inspection.

In our cell abnormality detection application, a training engineer uses sample images of possible cell abnormalities, like cancer, to teach the software to conceptualize and generalize the normal appearance of a cell or cell clusters. These slides are labeled as “good” examples of healthy cells and take into consideration normal healthy cell variants, like mitosis. Then, during runtime, any variations are flagged as anomalous and likely exemplifying cell damage. This application requires one further step.

Once a cell or cell cluster is flagged, the particular region of interest needs to be dynamically segmented in real-time for further review. The cell exhibits potential damage, after all, because its appearance strays from the norm, but it is not necessarily cancerous. These deviations could be caused by artifacts on the slide.

Normally, a human inspector—likely a pathologist—would have to review this subset of samples to make a firm diagnosis. But again, Cognex’s deep learning-based software can re-run its algorithm over the subset target zones—this time with retraining in supervised mode–to parse between “good” (tolerable, non-damaged) and “bad” (pathological, damaged) cells.

Life Sciences Optical Character Recognition

Many medical suppliers rely on automatic identification for traceability and to meet safety regulations. Human-readable alphanumeric characters can easily present as deformed to the camera of an automated inspection system if it is present on stretchable, moldable material like an IV bag. Specular glare and reflection can also confuse the system, obscuring and changing the code’s natural appearance.

Even without these visual variations, it can still be immensely time-intensive to teach a vision system to recognize different fonts, such as in the case of optical character verification (OCV), when the inspection system can’t anticipate what font style it will encounter. This is where a pre-trained, omni-font library can come in handy. A deep learning-based tool that it pretrained to recognize various fonts essentially works out-of-the-box; there is no upfront image-based training required, and the minimal training that does occur only happens on missed characters to refine the model’s logic.

Fast, easy implementation and limited application adjustments make deep learning-based OCR an obvious choice for applications involving deformed, skewed, and poorly etched characters or in verification applications when the camera is sure to encounter a wide range of unknown fonts.

Life Sciences Assembly Verification

Lab automation devices such as clinical analyzers and in-vitro diagnostic devices rely on machine vision to ensure that samples are perfectly inserted and aligned for optimal testing conditions. Diagnostic device manufacturers’ success relies on the accuracy of their machines’ measurements and results. Perhaps most importantly, they rely on accurate test set-ups and deck assemblage, which provide the device with precise data so that the tests are performed correctly and uniformly.

The correct assemblage of testing samples—blood, urine, or tissue—in what’s known as a pre-assembly verification is essential to reduce any potential errors which could threaten contamination, mix up or mislabel diagnoses, or slow down or break expensive equipment. During these inspections, the automated system must verify that there are no misaligned or absent test tubes, caps that haven’t been removed, or extraneous vessels loaded into the analyzer’s rack. Verifying that the equipment’s rack has been populated completely and correctly involves managing several factors: sample and reagent tubes and vessels vary by manufacturer in shape, size, and dimension, and it can be impossible for the machine to predict the position of samples on the deck.

With these unpredictable variations in test set-ups, it makes sense to use deep learning to perform assembly verification. Cognex deep learning-based software can learn the varying appearance of different samples and reagents, as well as their unpredictable and varying locations, based on a set of training images.

The tool generalizes the distinguishing features of the samples and reagents based on their size, shape, and surface features and learns their normal appearance, as well as their general location on the deck’s racks or microplates. In this way, deep learning is able to automate and solve a previously hard-to-program application in a quick, highly accurate, and easy-to-deploy manner.

Life Sciences Classification

Ascertaining the quality of a blood sample still requires a significant amount of human judgment. This is because a properly prepared sample which has been centrifuged and indexed needs to receive individual scores for turbidity and plasma color. Based on how the samples are loaded into the analyzer machine, their appearances can vary and blood can appear relatively more or less separated. This affects indexing.

For example, a sample with more clearly stratified plasma, buffy coat, and red blood cells would be rated more highly than one with less distinct phases. But in a highly automated lab environment which relies on good workflows, this approach is not ideal. Thankfully, deep learning-based image analysis can mimic human intelligence and assess the quality of a centrifuged sample’s separation. But the quality management process involves one further step: classification.

Only those samples with a passing grade will be allowed for testing. This makes it imperative for the inspection system to be able to generalize and conceptualize the appearance of “good” (i.e, well separated) red blood cell phases. It does this based on factors like plasma color, turbidity, and buffy coat volume, which are all criteria used in sample processing.

Deep learning is the only automation tool able to intelligently classify, sort, and grade multiple objects within a single image. In this case, Cognex Deep Learning is able to sort multiple classes within a single vial of blood to identify and pass only those samples which meet testing criteria.

As the latest automation solution for complex life sciences applications, Cognex’s deep learning-based tools are conveniently available as both off-the-shelf and OEM systems to be designed directly into lab automation devices. With highly reliable results and low demand on additional infrastructures like CPUs or embedded PCs, Cognex’s deep learning-based software is a natural addition to the life science industry’s arsenal of machine vision inspection tools.

: Author Mike Poe

This is the final part of our four-part series examining typical logistics processes within in a distribution center (DC). In our last discussion, we explored opportunities to improve operational efficiency and increase throughput for sortation. Today we will focus on outbound logistics.

Increased consumer demand for online shopping has created more pressure on retail distribution and e-commerce fulfillment facilities to deliver products to the right place and on-time to customers and stores, at optimal cost. Outbound orders require more traceability and efficiency of outbound shipping processes to optimize costs and achieve these goals. Reliable and highly accurate machine vision and image-based barcode reading solutions are critical to logistics operations to meet these requirements. UsingĚýĚýfrom the these solutions, companies can fine tune their outbound operations to identify problems before they become larger and more expensive. Here are a few examples of typical challenges within the outbound logistics operations and how Cognex solutions help achieve efficiency and cost optimization goals as well as improve traceability.

Optimize shipping costs and enhance revenue recovery

Shipping costs are a large piece of any logistics facility’s operating expenses. To optimize shipping costs, facilities must have the capability to audit the charges they receive for shipping and understand if they line up with expectations based on item sizes. Today, many operations around loading trucks are commonly performed manually. As more products begin to “ship in own container” traditional measuring equipment struggles to provide accurate dimensions on the variety of products which causes extra money to be spent on additional truckloads.

Using a 3D and 2D smart camera, such as theĚý, helps organizations more accurately estimate and lower shipping costs by capturing and providing accurate dimensional and volumetric packaging information to optimize shipping costs and optimize the number of packages per truck. Using 2D image data, the 3D-A1000 helps recover revenue by providing proof of shipment for traceability against damaged or missing claims.

Build pallets faster and increase overall truck loading efficiency

Distribution facilities are always seeking more efficient ways to load and unload trucks as the faster they can get trucks in and out of a facility, the higher their throughput. In many cases, packages are manually loaded into trucks and stacked, one item at a time. To meet efficiency requirements, facilities are trending to building pallets prior to outbound shipment as it saves time and money as well as protects product during transport.

Part of the palletization process involves scanning each item before it is placed on the pallet. Traditionally, laser-based hand scanners are used for this purpose. However, these scanners have difficulty reading damaged or smudged codes, which means operators must take the time to print out a new label for the package which adds time and money to the process. In addition, using a hand scanner requires a free hand, which reduces overall efficiency.

Using image-based overhead barcode reading solutions, such as theĚý, offer a hands-free solution to read codes quickly, efficiently, and accurately. The overhead scanning configuration means operators can read and build pallets faster than if using handheld or ring scanners. In addition, this approach ensures distribution or fulfillment facilities ship the correct items, improves inventory accuracy, eliminates costly returns, and improves overall package traceability.

Load outbound trucks faster while increasing traceability and reducing manual handling

Retail distributors and fulfillment facilities are seeking ways to improve traceability from the inbound dock door to the outbound dock door. To achieve “zero loss” of goods as they travel into and through a facility, management teams are becoming increasingly focused on quality of operations and asset management (minimizing loss) so verifying what goes on the outbound truck is more important than ever. In many organizations, operational methods rely on ship sorter accuracy to ensure the right products are loaded on trucks.

However, this approach does not always meet loss objectives. Packages get lost, stolen, or get put on the wrong ship lane due to incorrect sorting due to poor code quality and the scanning system’s inability to read the codes. It is not uncommon for distribution centers to lose several dozen packages per day and tens of thousands over a year. In addition to increased internal costs, customer expectation and brand reputation are negatively impacted when products are not delivered on time or at all.

Many companies use hand scanners as a basic point of traceability at the dock door. This can cause unwanted delays as operators need to take extra time to pick up a scanner, scan a box, put the scanner down, and load the box onto the truck. So, what are your options?

There are a few ways you can improve traceability at the outbound dock door. Choosing the right solution depends on several factors including how many operators your facility requires as part of the outbound logistics process as well as throughput requirements. Operations teams require high accuracy and fast performance from their code reading processes, including the ability to read damaged, smudged, or torn codes. Logistics teams are seeking ways to continually improve the quality of their flow-through operations by using analytics that leverage no-read data and images of unread codes to identify and solve problems earlier in the process, before they become larger and more costly.

Ěý– Presentation Scanning at the Dock Door

Using image-based overhead presentation scanning solutions, such as theĚý, offer a hands-free solution to reading codes quickly, efficiently, and accurately. The overhead scanning configuration means operators can read and load more boxes per minute than if using handheld or ring scanners. In addition, using this application at the dock door ensures distribution or fulfillment facilities ship the correct items, improves inventory accuracy, eliminates costly returns, and improves overall package traceability.

– Outbound Scanning Tunnels

High-volume distribution facilities require fast throughput, higher levels of automation with less operator intervention, and increased traceability. One way to lower the cost of operations is to useĚý, featuring image-based barcode technology such as the Cognex DataMan 470 fixed-mount barcode readers. Using image-based reading technology, you get complete, high-speed barcode reading coverage regardless of the position or condition of the codes. In addition, Cognex barcode scanning tunnels use advanced algorithms andĚýĚýto accurately read codes at extreme angles (up to 85 degrees) to enable packages and parcels to be placed closer together to increase throughput.

As you can see through these application examples, there are plenty of opportunities to optimize areas of your outbound logistics functions for efficiency and throughput. Browse our library of logistics applications and logistics barcode reading systems and tunnel solutions to learn how Cognex image-based barcode readers and machine vision solutions help improve efficiency and traceability, increase throughput, and optimize processes.

In case you would like to read other blogs in this series:

Part 1:Ěý

Part 2:Ěý

Part 3: