LG's robot to be used in facility diagnosis of POSCO's steel mill
LG Electronics’ AI-based self-driving robots can be widely used for facility diagnosis at hazardous worksites in the future, the company said Wednesday, after completing a two-day demonstration of unmanned facility management using its robot at POSCO’s Gwangyang steel plant. LG said the robot completed its facility management mission in the electrical room of the steel plant in Gwangyang, South Jeolla Province, on Monday and Tuesday.
This demonstration project was carried out as part of a business agreement between LG Electronics and POSCO Holdings. In May, the two companies signed a business agreement for the development of technologies in the fields of robots, AI and 5G networks, and conducted the first stage of a demonstration in July to control the driving of a semi-autonomous robot.
“We equipped the robot with cameras and light detection and ranging (LiDAR) sensors. The robot uses the information acquired from the sensors to recognize its surroundings more accurately,” LG said. “It has a high recognition rate even in low-light environments such as basements or places where safety fences are installed around equipment. If it detects abnormal temperatures in various equipment in the electrical room using the temperature information obtained from its thermal imaging camera, it will take a picture and send an alert to the control room.”
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.
Automation in metal fabrication continues to become more mobile
Think about an automated precision sheet metal operation, one with all the technological bells and whistles at every manufacturing step. A flexible manufacturing system brings sheet from a live-inventory tower system to a laser cutting bed. Blanks are cut, sorted, and stacked automatically with part removal automation, then brought to a robotic press brake with automated tool changes and a robot with an ultraflexible gripper that’s able to handle a range of workpieces.
As part of analyzing the process, think about the judgments and decisions the operation requires. Consider a mobile robot with an arm that moves blanks to a conveyor or other processes downstream. When presented with a pallet of four stacks, which stack does the robot take from first, when, and at what pace and sequence? Are integrated solutions needed to handle or prevent unexpected or rare events, like an air knife or other device to prevent double picking of blanks? Will the mobile robot need to navigate around different obstacles?
Advantech Partners with Leading Robotics Engine Platform MOV.AI to Accelerate Autonomous Mobile Robot Development
Advantech, a leading provider of industrial computing platforms, is pleased to announce its partnership with MOV.AI, a mobile robotics software solutions provider. The goal of this collaboration is to streamline the creation of autonomous mobile robots (AMRs) by providing robot manufacturers and integrators access to both cutting-edge industrial computing technology and a powerful software platform for building, deploying, and running intelligent robots.
Advantech’s rugged hardware can be integrated with MOV.AI’s Robotics Engine Platform™, making it easier to develop and deploy Autonomous Mobile Robots (AMRs) for businesses. This collaboration provides a solution to address the growing need for faster time-to-value, flexibility, improved efficiency, and higher productivity in manufacturing and logistics.
Predicting congestion in fleets of robots
Many Amazon fulfillment centers use mobile robots to move shelves, retrieve products, and deliver them to workers for sorting, reducing the need for employees to walk long distances. For simplicity and scalability, the path-planning algorithm those robots currently use focuses on individual agents and ignores interactions between multiple agents.
In a paper we presented at this year’s International Conference on Robotics and Automation (ICRA), we propose a deep-learning model that can predict congestion on the floor in real time. We tested the model’s predictions in simulations of two downstream applications: dynamic path planning in sortation centers, where our model improved throughout by 4.4%, and travel time estimation, where it improved the mean absolute percentage error by 30% to 40% relative to the current production methods.
Automotive works on its mojo
Top of the list here is reducing transportation costs. In fact, transportation is the largest single cost in the supply chain for automotive, says Matt Bush, vice president of engineering and innovation at KPI Solutions. The challenge, he says, is to increase the density of parts and components inside the trailer. But as Freeberg points out, LIB components can easily weigh out a truck faster than it can be cubed out. The other challenge is to maximize the return ratio of collapsed containers on their trip back to the manufacturing plant, wherever that might be, says Freeberg. The standard ratio today is 3:1, reducing the number of trucks needed to return sustainable containers by two for every three shipments.
As Bush of KPI explains, it’s a continuing battle for automakers to manage the flow and relative state of assembly completion of parts and components lineside, where space is at a premium. For instance, a key question continues to be: Is it better to send kits of parts to the line or stage all inventory there for on-the-spot assembly? “The kitting process takes space but reduces the number of steps people must take along the line,” adds Bush.
Locus Robotics Launches the Industry's First Data Science-Driven Warehouse Automation Platform
Locus Robotics, the leader in autonomous mobile robots (AMRs) for fulfillment warehouses, announces LocusONE, the industry’s first data science-driven warehouse automation platform to enable seamless operation and management of large quantities of multiple AMR form factors as a single, coordinated fleet in all sizes of warehouses. LocusONE uses proprietary data science to support the full breadth of material movement needs found in today’s fulfillment and distribution warehouses.
When Will the Mobile Robot Vendor Base Consolidate?
Despite numerous acquisitions the mobile robot market is in fact not consolidating. More vendors emerge each year and more industrial companies are launching AMRs. The combined market share of the top 10 and top 20 leading vendors barely changed between 2018 and 2020 and indeed dropped in 2021. Over the past six years of researching this industry, we consistently identify new players (both start-ups and existing companies from adjacent markets that now offer AMRs).
Many of the AMR start-ups from yester-year are now generating significant revenues (>$20m) having successfully expanded on pilots conducted in previous years. US-based Locus Robotics became the industry’s first “unicorn” being valued at over $1bn following its $150m fund-raising round close to two years ago. Chinese rival, Geek+ has long been rumored to be planning its IPO (perhaps when industry and macro conditions improve), further highlighting how far these once-start-ups have come.
MiR250 Fleet at Denso
Mauser Improves Throughput by 600% with OTTO
Ford Operates 3D Printers Autonomously
At Ford’s Advanced Manufacturing Center here, Javier is tasked with operating the 3D printers completely on his own. He is always on time, very precise in his movements, and he works most of the day. He never takes a lunch break or a coffee break—he doesn’t even ask for a paycheck. Javier is an autonomous mobile robot from KUKA, and he’s integral to the company’s development of an industry-first process to operate 3D printers with little or no human intervention.
Typically, different pieces of equipment from various suppliers are unable to interact because they do not run the same communication interface. Ford developed an application interface program that allows different pieces of equipment to speak the same language and send constant feedback to each other. For example, the Carbon 3D printer tells the KUKA autonomous mobile robot when the printed product will be finished, then the robot lets the printer know it has arrived and is ready to pick up parts. This innovative communication is what makes the whole process possible.
Wireless Charging Enables Industry 4.0 Implementation with Mobile Robots
Modern wireless charging systems with increased efficiency and cost-optimized components have proven to be a game changer in a factory setup for a number of reasons. First, they improve productivity and reduce manufacturing costs in a variety of ways. They enable continuous operation with opportunity charging (i.e., using idle time to charge), and reduce investment since robots can be multipurposed for different operations. They also reduce human intervention because the charging process can be automated, as well as maintenance costs since connector and cables, etc., can be eliminated resulting in a completely contactless solution. Second, these charging systems increase safety and security. They remove the risk of sparks caused by connectors and short circuits due to contamination or moisture inside them.
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.
Yokogawa and Mitsubishi Heavy Industries to Undertake AI-enabled Robot System Project for the Nippon Foundation - DeepStar Joint Research & Development Program
The aim of this project is to develop an automatic inspection system that utilizes robots to identify and predict hazards in offshore facilities. The use of a wide variety of robots to enable unmanned operations and thereby reduce the risk of performing inspections on offshore platforms has long been considered; however, the centralized coordination of individual robots is complex as it requires the management of multiple systems and the data that they acquire. Yokogawa has already been engaged in the research and development of a robot service platform that centralizes the management of multiple robots and seamlessly links them with existing control systems. Leveraging the findings of this R&D, this project will build a communications infrastructure and robot system that is well suited for the environment found on offshore platforms, and utilize an AI application to convert for use in offshore platform operations the image and sound data acquired by robots.
GE Healthcare Achieves Lean Efficiency With Autonomous Mobile Robots
Germain likes the OTTOs because they get material where it needs to be, when it needs to be there. This is essential, as the AMRs must deliver parts for more than 2,000 equipment repairs per week. Material flow efficiency is improved, notes Germait, because the robots enable pull-type supply chain management, where material movement is based on actual demand. However, material handling is not standardized because the facility receives different-size parts every day, and the parts often need to be delivered to different repair cells.
Hundreds of technicians work in the West Milwaukee facility, in repair cells that are 40 percent smaller since implementing AMRs. Downsizing the cells has also enabled GEH to increase its productive floor space by 66 percent and greatly improve throughput per square foot.
How AMRs change the safety equation
Soon, manufacturers and buyers wanted clear safety standards for AMRs from organizations like A3. They asked, “What guidance can you provide through a standard to help us understand how we can assess the safety of these devices?” Wise was on the committee that created Mobile Robot Standard R15.08-1-2020, the new Mobile Industrial Safety Standard. In April, Fetch announced full conformance with the standard.
As far as safety standards, Universal Robots follows the ISO standard that came out in 2011 (ISO 10218-1). This ISO standard is Part 1 of ANSI/RIA R15.06. She noted that European companies, like Universal Robots, tend to have higher requirements for safety, given the requirements of the European Directives.
Computer-on-Modules For Autonomous Intralogistics Vehicles
At Transpharm Logistik, however, the promotional products change frequently and come in different shapes, sizes and weights. Staff therefore have to pick them individually per recipient. Nevertheless, Transpharm Supply Chain Analyst Martin Zwiebel was tasked to optimize the pick and delivery process further. “Staff were using heavy, bulky carts to pick promotional products,” recounts Zwiebel. Equipped with tablets and supported in some cases by pick-by-light systems, they gathered the individual items from across the entire warehouse and then wheeled the cart with the complete pick to the packing department, where the promotional products were made ready for dispatch. “When looking for a faster and easier solution, it became apparent that a driverless transport system promised significant advantages,” the analyst continued. So, what was needed was an affordable robotic trolley that could autonomously find its way to the next storage bay following a predefined optimized route, and that would prove a constant and helpful companion to staff.
The New Isaac AMR Platform (Full Version)
Where Four-Legged Robot Dogs Are Finding Work
Sensor fusion gets robots roving around factories
Adam explained that most manufacturing processes are organized around fixed conveyors and robotic systems. To vary the specifications of the end product, human operators are typically needed to move product pieces from one assembly process to another. ‘Increasing flexibility requires more people to handle the work pieces and push them around, but this human intervention does not add much value,’ he said.
For that reason large manufacturing companies are keen to deploy mobile robots to transport inventory and product pieces around the factory floor. These autonomous mobile robots (AMRs) are designed to move and operate by themselves, which means that they must be able to perceive their surroundings and react to them. Visual information is crucial to aid navigation and avoid collisions, as well as to enable the robot to perform simple functions such as selecting and picking up the objects that need to be moved.
Fleet of MiR robots at Mirgor in Argentina
Hyundai Motor Group x Boston Dynamics Factory Safety Service Robot
AGV and AMR: What is the Actual Difference?
In logistics centers and production halls, there are always a lot of pallets, crates, mesh boxes, racks and numerous other objects that must be transported. This task can be accomplished by forklifts with human operators behind the steering wheel. Increasingly, driverless transport systems (DTS) are being used to move goods autonomously from A to B.
These driverless transport vehicles include Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs). Although they both accomplish the same tasks, these abbreviations should not be used synonymously: the two vehicle types are different and each of them has specific characteristics.
The A in AGV stands for Automated, while the A in AMR stands for Autonomous: a small difference with major significance. As the name suggests, AMRs operate autonomously, for instance by evading obstacles that suddenly block their path. On the other hand, AGVs travel on fixed routes and can only accomplish pre-defined tasks by following automated instructions. In contrast, AMRs make their own decisions when a situation requires.