DARLA

Demonstration of Artificial Reasoning, Learning, and Analysis 1

DARLA-01 is a 3U-CubeSat mission and integrated ground network to demonstrate key technologies for on-board science event detection and response. Using neural networks, the in-orbit spacecraft will be trained to identify interesting science events and respond to them.

For this mission, “interesting” science will be defined as unexpected objects detected by the imaging system (meteors, auroras) and radiofrequency (RF) events detected by the software-defined radio. Responses will include notifying the ground network, and by changing on-board science operations plans to collect more information about the event.

Mission Management

Program Manager: Melanie Reym

Chief Engineer: Nathan Brubaker

Spacecraft Configuration

-Form Factor: Single 3U CubeSat

-Attitude Stabilization: Passive magnetic stabilization

-Communications: UHF radio system

Flight & Ground Software

-Programming Language: Python-based flight and ground software

-Architecture: Asynchronous, network-based communication framework

-Update Capability: In-flight payload and flight software updates

Autonomy & Fault Management

-RF Event Detection and Reaction

-On-board Fault Detection and Response

Mission Capabilities

-Supports internal and external experimental payloads

-Enables in-orbit experimentation and rapid iteration

-Designed for flexible mission reconfiguration after launch

Development Status

-Current Status: Fully integrated

-Software: Fully functional flight software

-Final Integration: Fall 2024

Launch & Program Support

-Launch Opportunity: Sponsored by NASA’s CubeSat Launch Initiative (CSLI)

Senior engineering student Michael Dompke prepping DARLA for space launch.

Junior and sophomore students working with new engineering precursors in the new Space Systems Research Lab.

DARLA project manager Melanie Reym performs a practice assembly run.

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