LOGAN, Utah – Through the Slingshot 1 mission, the Aerospace Corp. has demonstrated how open standards and nonproprietary interfaces can help streamline satellite integration and operation.

After more than a year of on-orbit operations, Slingshot “has been a huge success,” David Hinkley, Aerospace Slingshot payload operator, told SpaceNews.

The 19 Slingshot payloads were developed independently and integrated in a couple of weeks prior to launch in July 2022 of the 12-unit cubesat on a Virgin Orbit LauncherOne rocket.

Speedy integration was possible thanks to Handle, a modular plug-and-play interface that allows payloads to draw power from the satellite bus and to communicate with the satellite and other payloads.

“It’s a peer-to-peer network where every payload can communicate,” said Alexander Utter, Slingshot command and data handling lead and principal investigator for Slingshot payload SatCat5.

Extended Operations

As Slingshot begins extended operations, Aerospace is using Slingshot’s plug-and-play architecture for additional missions and inviting satellite operators to consider adopting it.

The Slingshot standard has not been endorsed by international standards organizations. Still, “we think it’s an advancement on the current state of the art,” Utter said. .

After a little more than a year in orbit, Slingshot payloads continue to demonstrate autonomy, robotics and onboard processing. In addition, the satellite is equipped with a GPS transponder, a hydrogen peroxide thruster and a laser communications downlink.

Sharing Resources

Slingshot’s common interface has allowed payloads to share resources.

For example, Vertigo, a modular attitude control system that helps Slingshot point at ground targets, is accessing processing through Slingshot’s local area network. As a result, Vertigo does not need its own highly capable processor.

Another novel payload on Slingshot focuses on machine learning for rendezvous and proximity operations. A camera onboard Slingshot observes a tiny cubesat replica attached to a deployable panel on the outside of the satellite. Observing the miniature satellite in various lighting conditions, orientations and against different backgrounds provides training data for machine learning algorithms.

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