Image provided courtesy of Torch Technologies, Inc.

From defense to healthcare training: building VR on a common framework

By Sébastien Lozé |
September 10, 2020
Training on new military and defense systems can carry significant costs, especially if trainees must learn on the actual item. This is where VR comes in to save time and money. Torch Technologies, Inc., a Department of Defense (DoD) contractor for over 17 years, sees VR with Unreal Engine as the future of training on these complex systems.

Three and a half years ago Torch Technologies formed their in-house Advanced Viz Lab (AVL) to find better ways to provide virtual solutions across their DoD client base, one of which is a major DoD weapons program. The AVL initially developed demonstrations of VR-based training systems in Unreal Engine, which quickly caught on with clients. 

“The cost of major military systems is measured in billions of dollars—with a ‘B’,” says Darryl Trousdale, Senior Manager of Torch Technologies’ Advanced Visualization Lab. “To build a traditional training device for any part of that system costs tens of millions of dollars. So when you can build that virtually for a fraction of those costs, you get a lot of attention.”
Image provided courtesy of Torch Technologies, Inc.
AVL has grown into a robust gaming lab with over 40 artists, designers, and developers, and has shipped their training products all over the globe. 

Building a core with Unreal Engine

All Torch Technologies’ training solutions are built on a common core—the backend system, database, learning management system, and so on—with Unreal Engine at the center. “My team and myself, we're focused on the engineering aspects of Unreal Engine,” says Jeff Morgan, Senior Systems Engineer at Torch Technologies, and the software director for the AVL. “The idea is to have a core that works for all our customers. That core element is what we use to build the multiple different training devices that we're pursuing.”

Such an approach means that each simulation does not need to be built from the ground up in Unreal Engine. Instead, AVL can quickly bolt on the appropriate Blueprint scripts, import the required 3D models, and build out a training solution, all in a fraction of the time it would take to build it from scratch.
Image provided courtesy of Torch Technologies, Inc.
“Our products are very fast-paced, very quick-turn solutions anchored around Unreal Engine,” explains Trousdale. “We've been very, very happy with that. And it's leading to lots of other customers that want similar products.”

Proving the platform with medical simulation

The AVL team recently had a chance to test their Unreal Engine core on a product a world away from missile systems: Certified Registered Nurse Anesthetist (CRNA) training.
Image provided courtesy of Torch Technologies, Inc.
Trousdale’s next-door neighbor is Peter Stallo, a longtime healthcare professional with a bent toward programming and animation. In fact, 21 years ago, Stallo’s graduate thesis was on virtual reality and healthcare, years before the technology was available to support it.

In the meantime, Stallo started an online educational company called Prodigy Anesthesia. About a year ago, Stallo downloaded Unreal Engine to see if he could quickly put together a functional model for training. Two weeks later, he had a basic mockup of an operating room. “I showed it to Darryl,” Stallo says, “and that's where the relationship between the two companies started.”
Image provided courtesy of Torch Technologies, Inc.
Using Torch Technologies’ core technology, the AVL project became Simvana, a VR training simulator that immerses anesthesia providers in complex and difficult situations.

“Our developers were able to take his initial prototype work and build up a more thorough framework with it, using the technology that we've developed for our military trainer,” says Alex Engelmann, R&D Lead in the AVL at Torch Technologies. 

The development and release of Simvana proved to Torch Technologies that they had a backbone framework that could be used to build virtually any type of training. “We made a hyper-jump to anesthesia,” says Trousdale. “That's far outside the DoD world. But developing Simvana proved the point: whatever sophisticated system you can bring to me, as long as you can bring a subject matter expert with it, we can build it and operate it in 3D space.”
Image provided courtesy of Torch Technologies, Inc.

From Blueprint to C++ with Unreal Engine

While the Torch Technologies AVL team had several choices when starting their first venture into VR training and simulation, they chose Unreal Engine for its visual fidelity and easy programmability.

“Firstly, it didn’t take much effort to get the visual fidelity up and running so quickly, which was a big plus for us in the early days,” says Engelmann. “And then with Blueprint, we were able to prototype ideas and iterate on things quickly, to see what works and see what doesn't. That was the slam-dunk feature for us.”

After using mostly Blueprint, Unreal Engine’s visual scripting system, for development, the team recently started moving core processes to C++ to keep simulation speed high in the face of the massive datasets required by some of their customers. But because they had structured their Blueprints to use modular classes, the AVL team found that transferring them to C++ was fairly easy. 

“We kept things organized, so it’s pretty much a direct port.” says Engelmann. “And in some cases, we were able to use the nativization feature and let Unreal handle it by itself, with minimal work on our end.” He adds that the team still keeps certain processes in Blueprint, “just to keep things flexible.”
Image provided courtesy of Torch Technologies, Inc.
Morgan adds that ADL’s long-term vision is to have a core that is built in C++, with a series of services that integrate with external operational simulations. “There would be a standalone simulation built to meet the customer's needs, and we would integrate that through a service layer into our Unreal projects,” he says. “That's the direction we're pursuing.”

Morgan cites Epic’s commitment to building out the Unreal Engine platform as an inspiration for AVL’s engagement and investment in becoming experts in the software. “As things like Pixel Streaming and other technologies get integrated into that platform, we have the option to push our products into these areas,” he says. “Because of Unreal Engine, we can take our products into new VR and AR technologies much more rapidly than we otherwise could.”

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