How to Train Autonomous Mobile Robots to Detect Warehouse Pallet Jacks Using Synthetic Data
This use case will again take a data-centric approach by manipulating the data, as opposed to changing the model parameters to fit the data. The process begins by generating synthetic data using NVIDIA Omniverse Replicator in NVIDIA Isaac Sim. Next, train the model with synthetic data in NVIDIA TAO Toolkit. Finally, visualize the model’s performance on real data, and modify the parameters to generate better synthetic data to reach the desired level of performance.
For this first batch of synthetic data, the team used the LOCO dataset, which is a scene understanding dataset for logistics covering the problem of detecting logistics-specific objects to visualize the real-world model performance.
Amazon Introducing Warehouse Overhaul With Robotics to Speed Deliveries
Amazon is introducing an array of new artificial intelligence and robotics capabilities into its warehouse operations that will reduce delivery times and help identify inventory more quickly. The revamp will change the way Amazon moves products through its fulfillment centers with new AI-equipped sortation machines and robotic arms. It is also set to alter how many of the company’s vast army of workers do their jobs. Amazon says its new robotics system, named Sequoia after the giant trees native to California’s Sierra Nevada region, is designed for both speed and safety. Humans are meant to work alongside new machines in a way that should reduce injuries, the company says.
Amazon said it would also start to test a bipedal robot named Digit in its operations. Digit, which is designed by Agility Robotics, can move, grasp and handle items, and will initially be used by the company to pick up and move empty tote containers.
📦 Inside Walmart’s Warehouse of the Future
“What this technology does for us is increases capacity, increases the accuracy of our loads, increases the speed of the supply chain and lowers cost,” said David Guggina, executive vice president of supply chain for Walmart. It is “also completely reshaping the way that our associates work within the distribution center.”
Kyle Silberger transferred from unloading trucks manually to what Walmart calls an “automated cell operator” about a year ago. “It’s easier physically and harder mentally,” said the 30-year-old who has worked at the warehouse for nine years. “It’s sort of autopilot on the loading dock,” he said, describing his former role.
🦾 Amazon’s New Robots Are Rolling Out an Automation Revolution
Proteus is part of an army of smarter robots currently rolling into Amazon’s already heavily automated fulfillment centers. Some of these machines, such as Proteus, will work among humans. And many of them take on tasks previously done by people. A robot called Sparrow, introduced in November 2022, can pick individual products from storage cubbies and place them into larger plastic bins—a step towards human-like dexterity, a holy grail of robotics and a bottleneck in the automation of a lot of manual work. Amazon also last year invested in a startup that makes humanoid robots capable of carrying boxes around.
Amazon’s latest robots could bring about a company-wide—and industry-wide—shift in the balance between automation and people. When Amazon first rolled out large numbers of robots, after acquiring startup Kiva Systems and its shelf-carrying robots in 2012, the company redesigned its fulfillment centers and distribution network, speeding up deliveries and capturing even more business. The ecommerce firm may now be on the cusp of a similar shift, with the new robots already starting to reshape fulfillment centers and how its employees work. Certain jobs will be eliminated while new ones will emerge—just as long as its business continues growing. And competitors, as always, will be forced to adapt or perish.
Packaging Automation: Making the Financial Case
The more you scrutinize the processes and all affected points in the supply chain and their associated value, the greater the possibility of a final ROC meeting corporate goals. It’s also essential to understand that every CFO or controller will have a firm grasp on the cost of money to the business as well as specific time targets for the realization of return. Many of our clients have realization targets of around two years. This is a much shorter timeframe than 40 years ago, which was closer to five years.
As you begin the documentation process, be aware that you should specify downstream equipment to easily recover from an upstream accumulation release. Typically, designers require that downstream equipment outproduce the immediate upstream process by 10-20%. Furthermore, as you start to block layouts for new equipment, it may require an ongoing line balancing by determining and coordinating the takt time of each corresponding step, ensuring that each phase in the process is more or less in sync with the next.
Amazon Turns to AI to Weed Out Damaged Goods
The AI checks items during the picking and packing process. Goods are picked for individual orders and placed into bins that move through an imaging station, where they are checked to confirm the right products have been selected. That imaging station will now also evaluate whether any items are damaged. If something is broken, the bin will move to a worker who will take a closer look. If everything looks fine, the order will be moved along to be packed and shipped to the customer.
Amazon so far has implemented the AI at two fulfillment centers and plans to roll out the system at 10 more sites in North America and Europe. The company has found the AI is three times as effective at identifying damage as a warehouse worker, said Christoph Schwerdtfeger, a software development manager at Amazon.
Comau’s MATE-XT wearable exoskeleton supports ergonomic well-being at John Deere’s parts distribution center in Brazil
Comau has equipped John Deere with multiple MATE-XT wearable exoskeletons to help sustain worker well-being, alleviate physical stress and reduce the ergonomic risk within its parts logistics operations. MATE-XT accurately replicates all movements of the shoulder, helping employees perform their jobs comfortably by reducing muscle fatigue without limiting mobility or adding bulk. Its ergonomic design can be easily adjusted to fit different people with different body types – changing the length of the shoulder straps and the required level of assistance based on the worker or the job at hand is quickly achieved in a few simple steps. Working closely with John Deere to implement the exoskeleton within its daily operations, Comau provided a hands-on training course held at John Deere’s 75,000m2 parts distribution center in Campinas, in the state of São Paulo.
Even when working with small and lightweight objects, the apparently minimal effort of repeated manual movements can take a toll on the body. To help John Deere quantify the benefits of using MATE-XT, Comau performed an electromyographic analysis of the ergonomic risk factor. MATE-XT kept the muscle at a rest stage for 98.5% of the activity time (compared to only 2.4% of the time without MATE-XT).
Warehouse Software Automation to Robotic Automation: Choosing a Scalable Solution that Matches Your Pace
In the past, the logistics chain was much more straightforward; warehouses delivered the bulk products on pallets to stores, and then consumers would travel to stores to select and purchase the items. Today, eCommerce fulfillment workers need to access thousands of SKUs that are ordered in random quantities and combinations and at random times. At the same time, warehouses struggle to attract workers in the current labor shortage.
Robotic automation in e-commerce fulfillment centers improves efficiency, productivity, and profitability while reducing labor costs in the warehouse. Traditionally, the most significant barrier to entry for warehouse automation was the cost, along with the necessary changes in infrastructure to accommodate it. Many robotic solutions require significant upfront capital investments.
How a universal model is helping one generation of Amazon robots train the next
In short, building a dataset big enough to train a demanding machine learning model requires time and resources, with no guarantee that the novel robotic process you are working toward will prove successful. This became a recurring issue for Amazon Robotics AI. So this year, work began in earnest to address the data scarcity problem. The solution: a “universal model” able to generalize to virtually any package segmentation task.
To develop the model, Meeker and her colleagues first used publicly available datasets to give their model basic classification skills — being able to distinguish boxes or packages from other things, for example. Next, they honed the model, teaching it to distinguish between many types of packaging in warehouse settings — from plastic bags to padded mailers to cardboard boxes of varying appearance — using a trove of training data compiled by the Robin program and half a dozen other Amazon teams over the last few years. This dataset comprised almost half a million annotated images.
The universal model now includes images of unpackaged items, too, allowing it to perform segmentation across a greater diversity of warehouse processes. Initiatives such as multimodal identification, which aims to visually identify items without needing to see a barcode, and the automated damage detection program are accruing product-specific data that could be fed into the universal model, as well as images taken on the fulfillment center floor by the autonomous robots that carry crates of products.
Walgreens Turns to Prescription-Filling Robots to Free Up Pharmacists
Walgreens Boots Alliance Inc. is turning to robots to ease workloads at drugstores as it grapples with a nationwide shortage of pharmacists and pharmacist technicians.
The nation’s second-largest pharmacy chain is setting up a network of automated, centralized drug-filling centers that could fill a city block. Rows of yellow robotic arms bend and rotate as they sort and bottle multicolored pills, sending them down conveyor belts. The company says the setup cuts pharmacist workloads by at least 25% and will save Walgreens more than $1 billion a year.
The ultimate goal: give pharmacists more time to provide medical services such as vaccinations, patient outreach and prescribing of some medications. Those services are a relatively new and growing revenue stream for drugstores, which are increasingly able to bill insurers for some clinical services.
Amazon Shows Off Impressive New Warehouse Robots
Proteus is our first fully autonomous mobile robot. Historically, it’s been difficult to safely incorporate robotics in the same physical space as people. We believe Proteus will change that while remaining smart, safe, and collaborative.
Proteus autonomously moves through our facilities using advanced safety, perception, and navigation technology developed by Amazon. The robot was built to be automatically directed to perform its work and move around employees—meaning it has no need to be confined to restricted areas. It can operate in a manner that augments simple, safe interaction between technology and people—opening up a broader range of possible uses to help our employees—such as the lifting and movement of GoCarts, the nonautomated, wheeled transports used to move packages through our facilities.
Fabric Micro-Fulfillment Center in Dallas, Texas
Amazon Robotics Builds Digital Twins of Warehouses with NVIDIA Omniverse and Isaac Sim
Automation: Why software is the star
As fulfillment centers and warehouses become more highly automated facilities with multiple types of automation, software’s role looms larger. Issues like coordinating multiple systems around cut-off times and service levels, as well as knowing when and how to scale automated systems to accommodate peaks in demand, are two leading reasons why.
One way a warehouse execution system (WES) coordinates the allocation of work across automated systems is with smart order release, which instead of the big “waves” of work, releases work to systems in smaller chunks with the current status and capacity of multiple zones of automation in mind. This order release function can be thought of as the starting point for orchestration, with WES’s ties to lower-level control systems alerting of any unexpected events, or bottlenecks, that might be developing, with some software offering “load balancing” features to help adjust to the present reality on the floor.
With robotics solutions, software plays at multiple levels. Autonomous mobile robot (AMR) vendors, for example, don’t just make robots, they also offer fleet manager software, performance monitoring and analytics. Some vendors are also expanding into broader orchestration with functions like pack-out lines. Of course, artificial intelligence (AI) is in many robotics solutions, so the system can continuously learn over time when it comes to issues like path optimization, or how to best grasp and manipulate items.
Ocado showcases 3D printing innovation
Ocado has unveiled a new approach to building the robots in its fulfilment centres, which it hopes will dramatically improve efficiency and reduce operating costs. The company has developed a 600 Series bot, which it said can be built cheaper and is lighter than the current 500 Series bot. According to Steiner, the 600 Series grocery fulfilment bot “changes everything”. Ocado designed the 600 Series using topology optimisation, similar to the technique used in the aerospace sector to make aircraft parts strong but light. It then used additive manufacturing, in partnership with HP, to make 3D prints of the parts required to build the 600 Series.
A remote village, a world-changing invention and the epic legal fight that followed
The twisted tale of the battle between Norway’s AutoStore and the UK’s Ocado for robotic grocery picking supremacy.
How warehouse automation robotics transformed the supply chain
This technology reduces the cost per vehicle and helped teams redesign the factory to be more efficient. “Navigating vehicles around a manufacturing facility is costly, challenging and prone to human error,” said Jerone Floor, vice president of products and solutions at Seoul Robotics. AI-enabled warehouse orchestration engines can also improve coordination between robots and humans. Experts like Vecna Robotics’ Cherewka believe this has become increasingly important when facing new global supply chain challenges and growing consumer demands.
Medicine piece-picking robot for Hitachi Transport System
In Amazon’s Flagship Fulfillment Center, the Machines Run the Show
More than the physical robots, the stars of Amazon’s facilities are the algorithms—sets of computer instructions designed to solve specific problems. Software determines how many items a facility can handle, where each product is supposed to go, how many people are required for the night shift during the holiday rush, and which truck is best positioned to get a stick of deodorant to a customer on time. “We rely on the software to help us make the right decisions,” says Shobe, BFI4’s general manager.
When managers wanted to figure out how many people they needed at each station to keep up with customer orders, they once used Excel and their gut. Then, starting in about 2014, the company flew spreadsheet jockeys from warehouses around the country to Seattle and put them in a conference room with software engineers, who distilled their work and automated it. The resulting AutoFlow program was clunky at first, spitting out recommendations to put half an employee at one station and half an employee at another, recalls David Glick, a former Amazon logistics executive who supervised initial development of the software. Eventually the system learned that humans can’t be split in half.