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AI

NVIDIA Isaac Sim on Omniverse available in Open Beta

June 21, 2021 By WTWH Editor Leave a Comment

The new Isaac simulation engine creates good photorealistic environments, and streamlines synthetic data generation and domain randomization to build ground-truth datasets to train robots in applications from logistics and warehouses to factories of the future.

Omniverse is the underlying foundation for NVIDIA’s simulators, including the Isaac platform — which now includes several new features. Discover the next level in simulation capabilities for robots with NVIDIA Isaac Sim open beta, available now.

Built on the NVIDIA Omniverse platform, Isaac Sim is a robotics simulation application and synthetic data generation tool. It allows roboticists to train and test their robots more efficiently by providing a realistic simulation of the robot interacting with compelling environments that can expand coverage beyond what is possible in the real world.

This release of Isaac Sim also adds improved multi-camera support and sensor capabilities, and a PTC OnShape CAD importer to make it easier to bring in 3D assets. These new features will expand the breadth of robots and environments that can be successfully modeled and deployed in every aspect: from design and development of the physical robot, then training the robot, to deploying in a “digital twin” in which the robot is simulated and tested in an accurate and photorealistic virtual environment.

Summary of key new features:
–Multi-Camera Support
–Fisheye Camera with Synthetic Data
–ROS2 Support
–PTC OnShape Importer
–Improved Sensor Support
Ultrasonic Sensor
Force Sensor
Custom Lidar Patterns
–Downloadable from NVIDIA Omniverse Launcher

Isaac Sim sending multi-camera sensor data to Rviz (ROS Visualization Tool)
Controlling the Dofbot Manipulation Robot in Isaac Sim

Isaac Sim enables more robotics simulation
Developers have long seen the benefits of having a powerful simulation environment for testing and training robots. But all too often, the simulators have had shortcomings that limited their adoption. Isaac Sim addresses these drawbacks with the benefits described below.

Realistic Simulation

In order to deliver realistic robotics simulations, Isaac Sim leverages the Omniverse platform’s powerful technologies including advanced GPU-enabled physics simulation with PhysX 5, photorealism with real-time ray and path tracing, and Material Definition Language (MDL) support for physically based rendering.

Modular, Breadth of Applications

Isaac Sim is built to address many of the most common robotics use cases including manipulation, autonomous navigation, and synthetic data generation for training data. Its modular design allows users to easily customize and extend the toolset to accommodate many applications and environments.

Seamless Connectivity and Interoperability

With NVIDIA Omniverse, Isaac Sim benefits from Omniverse Nucleus and Omniverse Connectors, enabling collaborative building, sharing, and importing of environments and robot models in Universal Scene Description (USD). Easily connect the robot’s brain to a virtual world through Isaac SDK and ROS/ROS2 interface, fully-featured Python scripting, plugins for importing robot and environment models.

Synthetic data generation in Isaac Sim bootstraps machine learning

Synthetic Data Generation is an important tool that is increasingly used to train the perception models found in today’s robots. Getting real-world, properly labeled data is a time consuming and costly endeavor. But in the case of robotics, some of the required training data could be too difficult or dangerous to collect in the real world. This is especially true of robots that must operate in close proximity to humans.

Isaac Sim has built-in support for a variety of sensor types that are important in training perception models. These sensors include RGB, depth, bounding boxes, and segmentation.

Ground Truth Synthetic Data with Glass Objects

In the open beta, we have the ability to output synthetic data in the KITTI format. This data can then be used directly with the NVIDIA Transfer Learning Toolkit to enhance model performance with use case-specific data.

Domain Randomization

Domain Randomization varies the parameters that define a simulated scene, such as the lighting, color and texture of materials in the scene. One of the main objectives of domain randomization is to enhance the training of machine learning (ML) models by exposing the neural network to a wide variety of domain parameters in simulation. This will help the model to generalize well when it encounters real world scenarios. In effect, this technique helps teach models what to ignore.

Domain Randomization of a Factory Scene

Isaac Sim supports the randomization of many different attributes that help define a given scene. With these capabilities, the ML engineers can ensure that the synthetic dataset contains sufficient diversity to drive robust model performance.

NVIDIA Isaac Sim

Filed Under: AI, Company News Tagged With: NVIDIA

Siemens delivers Artificial Intelligence-powered CAD sketching technology

June 16, 2020 By Leslie Langnau Leave a Comment

Siemens Digital Industries Software announced a new solution for capturing concepts in 2D. The NX Sketch software tool enhances sketching in CAD. By changing the underlying technology, users are  able to sketch without pre-defining parameters, design intent, and relationships. Using Artificial Intelligence (AI) to infer relationships on the fly, users can move away from a paper hand sketch and truly create concept designs within NX software. This technology offers flexibility in concept design sketching, and makes it easy to work with imported data, allowing rapid design iteration on legacy data, and to work with tens of thousands of curves within a single sketch. With these latest enhancements to NX, Siemens’ Xcelerator portfolio continues to bring together advanced technology, even within the core of modeling techniques, helping remove the traditional barriers users have experienced to dramatically improve productivity.

Analysis has shown that in an average day or workflow, around 10% of a typical user’s day is spent sketching. In addition, within current design environments most concept sketching is happening outside of the CAD software due to the level of rules and relationships that must be decided on and built into the sketch by the user up front. Often designers in concept design stage do not necessarily know what the final product may be, which requires a sketching environment that is flexible and can evolve with the design. NX offers the flexibility of 2D paper concept design within the 3D CAD environment, as the first in the industry to eliminate upfront constraints on the design. Instead of defining and being limited by constraints such as size or relationships, NX can recognize tangents and other design relationships to adjust on the fly.

“Sketching is at the heart of CAD and is critical to capturing the intent of the digital twin,” said Bob Haubrock, Senior Vice President, Product Engineering Software at Siemens Digital Industries Software. “Even though this is an essential part of the process, sketching hasn’t changed much in the last 40 years. Using technology and innovations from multiple past acquisitions, Siemens is able to take a fresh look at this crucial design step and modernize it in a way that will help our customers achieve significant gains in productivity and innovation.”

Siemens Digital Industries Software
www.sw.siemens.com
www.plm.automation.siemens.com/global/en/products/mechanical-design/2d-and-3d-cad-modeling.html

 

Filed Under: AI, News, Siemens Digital Industries Software Tagged With: Siemensdigitalindustriessoftware

CAD and AI: making design better, faster, and easier

September 3, 2019 By Leslie Langnau Leave a Comment

AI has the potential to allow engineers to design products faster than before while meeting design specifications, sometimes in new and unique ways.

Jean Thilmany, Senior Editor

Artificial intelligence could be said to be the new hot buzzword, as it seems to be making inroads into all types of software.

“We don’t see a lot of AI yet in a CAD environment, but it’s coming,” says Andreas Vlahinos, chief technology officer Advanced Engineering Solutions, a research and design firm in Castle Rock, Colo.

AI is a broad field focused on using computers to do things that require human-level intelligence.

But how CAD will make future use of AI is still up for debate, he adds.

While some CAD makers are delving into AI functionality, the marriage of AI and design software is in the early stages, says Jon Hirschtick, chief executive officer of Onshape, which makes cloud-based CAD software.   “AI has great potential, but so far no one has illustrated how it will unfold,” he says.

AI doesn’t have a one-size-fits-all definition within any industry yet, says said Gian Paolo Bassi, chief executive officer at Dassault Systèmes.  “Today, there’s a huge debate about what AI is. People say AI is machine learning, or they say it’s related to the neural network or to neuroscience. Definitions vary.”

The machine learning that AI depends on is actually already present to a certain degree in the CAD systems that include topology optimization and generative design capabilities. “The primary functions of these features within CAD is to automate the analytical steps of design, Vlahinos says. The computer generates designs from an engineer’s preliminary directions.”

The key focus of AI in CAD right now is design optimization achieved through the creation of more intelligent designs which are lighter, stronger and more economical. And, in some cases, more artistic, continues Vlahinos.

Typically, designers create their design step by step, analyzing certain junctions to get critical feedback about performance. They tweak the design if it doesn’t meet performance needs or customer specifications. The incorporation of AI, as it stands now, allows the designers to skip these time-consuming steps allowing the task to get over quickly and effectively.

Last year, for example, Autodesk released generative design to subscribers of its Fusion 360 Ultimate product development software. The design concept allows engineers to define design parameters such as material, size, weight, strength, manufacturing methods, and cost constraints–before they begin to design. Then, using artificial-intelligence-based algorithms, the software presents an array of design options that meet the predetermined criteria, says Ravi Akella, who headed the product management team for Autodesk’s generative manufacturing solutions before moving last year to become director of product development at Roblox.  The feature focuses on helping designers define the problem they’re trying to solve, he says.

“The software asks the user preliminary questions. ‘What sorts of materials would you consider for your design? Where does it connect with other things as part of an assembly? What are the loads? What are the pieces of geometry?’” Akella says.

After a short period of time, the software then presents designers and engineers with an array of design options that best meet their requirements. Designers choose the best design. Or, if none of the options meet their needs, they can begin the generative process again, this time offering slightly different inputs.

Like other big-name CAD makers, SolidWorks also includes topology optimization capabilities within its CAD software.

“We expect the computing platform to anticipate your design goals,” says Bassi.

But, Vlahinos adds, the AI in those systems is used for simulation rather than for design. It’s by continual simulation that the designs are found. The tools allow the engineer to skip all the step-by-step analyzing. The human is still involved in the process and must validate the simulation the CAD system returns.

“The generative process could get you plus or minus 15 percent of the real answer but with 2 percent of the effort,” he says. “So, you know how to make the heat exchanger this way or that way – you’ve isolated the design alternatives and you can find them right away and validate.”

Even though the tools function through simulation, “You get amazing design insights and design innovation so you can see how something can be done,” he says.

But Vlahinos cautions against relying fully on the current AI-enabled CAD optimization tools.

“They are not simulation replacement,” he says. “Don’t let the vendors oversell them. They are design guidance, like a spell checker for you design concept. But they do give you more amazing results.”

Still, by helping engineers more quickly meet their prescribed design specifications, AI also frees up time engineers can spend focusing on other aspects of the piece—like its inherent shape or its artistic merits.

And – with proper validation in place — these tools can help ensure parts will meet manufacturing specifications and allow for much quicker design than traditional methods. It also can create unorthodox, sometimes never-before-seen-shapes that can be manufactured through 3D printing.

 The engineer as artist

Design for manufacturability, as it’s called, is of course an important—some might say bedrock—necessity. AI techniques have a role to play in other aspects of design. And some of its uses may not have been conceived of yet, as CAD makers focus on these first AI implementations, Vlahinos says.

“Right now, it’s ‘Please tell me what the optimal shape is to achieve my engineering goal,” Vlahinos says. “We could see AI answering other questions in the future.”

Though his career has focused on rapid product development — Vlahinos recognizes that AI could help engineers design products faster than before — at the same time it offers engineering company customers new and unique ways to meet needs they may not even know they have.

For instance, broaden the view beyond the focus on manufacturability and AI can also lend artistic value to an engineered piece or product, he says.

“We’ve never properly valued the artistry of the design. But we could,” he says.

A product’s design artistry is, of course, subjective, so putting a monetary value to that number — as opposed to function — has always been elusive. Likewise, the capability to add never-before-seen geometries that create swirls and whorls in new and unexpected ways to pieces can bring a great deal of satisfaction, or headaches, for designers, depending on their liking to bring creativity to their engineering work.

With proper validation in place — AI tools can help ensure parts will meet manufacturing specifications and allow for quicker design than traditional methods. It also can create unorthodox, sometimes never-before-seen-shapes that can be manufactured through 3D printing.

If CAD can evolve, in the not-too-distant future, everyday objects like your blender, electric toothbrush or even the engine within your automobile, will take the shape of nothing you’ve ever seen before, said Hod Lipson, a mechanical engineering professor Columbia University and director of the school’s Creative Machines Lab. He is a roboticist who works in the areas of AI and digital manufacturing.

Most 3D printers take their printing instructions from 3D CAD files. Because the 3D printer receives its instructions from CAD files, the printers are limited in the shapes that those CAD systems generate, Lipson says.  CAD software only allows for designers to work with recognized geometries: circles and ovals, squares and rectangles, and so on, he says.

That’s changing as topology optimization and generative design capabilities make their way into design tools, Vlahinos adds.

So the day of the twisted blender may be upon us sooner than we think.

Beyond simulation

Feature and character recognition, which have been part of AI for many years, are part of the SolidWorks system. In fact, they’re so standard that many users may not recognize the AI component of those features—until, for instance, they begin to type a misspelled word they use frequently and see that word corrected automatically, Bassi says.

And AI has a role in CAM as well. For instance, SolidWorks CAM automatically generates a part’s manufacturing toolpath after design. CAM software uses the CAD models to generate the toolpaths that drive computer numerically controlled manufacturing machines. Engineers and designers who use CAM can evaluate designs earlier in the design process to ensure that they can be manufactured, Bassi says.

AI has a role in CAM as well as CAD. For instance, SolidWorks CAM automatically generates a part’s manufacturing toolpath after design. CAM software uses the CAD models to generate the toolpaths that drive computer numerically controlled manufacturing machines. Such a features helps engineers evaluate designs earlier in the design process to ensure that they can be manufactured.

“The toolpath captures design strategies and recognizes features and types of materials, so you can have a CAM solution that’s almost completely automated,” Bassi says. AI drives the way the toolpath is automatically created.

“You can create a toolpath in a couple of clicks. You don’t need a lot of details for intelligent manufacturing,” Bassi said.

One thing is certain, Vlahinos says. AI will never take the human engineer or designer out of the equation.

Even intelligent machines need guidance. That means engineers will always be vital to the design process, he adds. A human will always be needed to view shapes and designs in the same way other humans will. To translate a part’s use, — its form, and its function — with an eye toward other human users.

Filed Under: AI, Software

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