Through the Slingshot 1 mission, Aerospace Corp. has exemplified the efficacy of open standards and nonproprietary interfaces in simplifying the process of satellite integration and operation. This success was evident after over a year of on-orbit functioning, as affirmed by David Hinkley, an operator for Aerospace Slingshot payloads, who stated that Slingshot has achieved significant accomplishments.
The Slingshot 1 project featured 19 distinct payloads, each developed independently and seamlessly integrated within a few weeks before being launched in July 2022. The launch utilized a Virgin Orbit LauncherOne rocket for a 12-unit cubesat.
This efficient integration was facilitated by Handle, a modular plug-and-play interface. Handle enables payloads to access power from the satellite bus and establish communication not only with the host satellite but also with other payloads. This connectivity operates on a peer-to-peer network, allowing every payload to establish communication pathways. Alexander Utter, Slingshot’s lead for command and data handling, and the principal investigator for Slingshot payload SatCat5, elucidated the nature of this system.
As Slingshot embarks on its extended mission phase, Aerospace Corp. is leveraging the plug-and-play design of Slingshot’s architecture for supplementary missions and encouraging satellite operators to contemplate its adoption.
While the Slingshot standard has not yet received official endorsement from international standards bodies, Alexander Utter expressed confidence in its progress beyond the current technological norm.
After slightly over a year in space, the Slingshot payloads persist in showcasing self-sufficiency, robotic capabilities, and onboard processing. Furthermore, the satellite is outfitted with a GPS transponder, a hydrogen peroxide thruster, and a laser communications downlink.
The universal interface of Slingshot has enabled payloads to effectively share resources.
To illustrate, consider Vertigo, a modular attitude control system designed to orient Slingshot toward specific ground targets. This system accesses processing capabilities via Slingshot’s local area network. Consequently, Vertigo doesn’t require an independent high-capacity processor of its own.
Another innovative payload integrated into Slingshot focuses on machine learning applied to rendezvous and proximity operations. Within this setup, a camera placed on Slingshot observes a miniature cubesat replica affixed to an expandable panel on the satellite’s exterior. By observing this miniaturized satellite across various lighting conditions, orientations, and backgrounds, valuable training data is gathered for machine learning algorithms.