All 2015 Content
Session
Technical Session VI: Ground Systems and Communications
Abstract
We present a novel algorithm for science planning for the Microwave Radiometer Technology Acceleration (MiRaTA) CubeSat mission that reasons about onboard resource limitations and automatically produces timelines for onboard activities with minimal human involvement. The Resource-Aware SmallSat Planner (RASP) attempts to maximize total science data acquisition time while also maximizing onboard energy and data storage margin. RASP was demonstrated over a representative 24 hour simulation of MiRaTA’s orbit with 19 timing and resource critical science acquisition opportunities. We showed that RASP successfully plans the science opportunities over varying planning horizon lengths, achieving at most 16 and at least 12 of the opportunities. Average onboard resource usage margins were examined, and ranged between 69.0 and 72.5% of the total range for data storage and 60.5 and 71.1% of the total range for energy storage. We examined the effect of ignoring resource storage margin, and found that energy margin dips as low as 42.3% and data margin as low as 33.6%. Finally, we found that RASP takes on the order of 10 seconds to create a feasible plan for the length of one orbit, suggesting that the algorithm is suitable for adaptation to a more computationally constrained onboard processor system.
Presentation
Automated Resource-Constrained Science Planning for the MiRaTA Mission
We present a novel algorithm for science planning for the Microwave Radiometer Technology Acceleration (MiRaTA) CubeSat mission that reasons about onboard resource limitations and automatically produces timelines for onboard activities with minimal human involvement. The Resource-Aware SmallSat Planner (RASP) attempts to maximize total science data acquisition time while also maximizing onboard energy and data storage margin. RASP was demonstrated over a representative 24 hour simulation of MiRaTA’s orbit with 19 timing and resource critical science acquisition opportunities. We showed that RASP successfully plans the science opportunities over varying planning horizon lengths, achieving at most 16 and at least 12 of the opportunities. Average onboard resource usage margins were examined, and ranged between 69.0 and 72.5% of the total range for data storage and 60.5 and 71.1% of the total range for energy storage. We examined the effect of ignoring resource storage margin, and found that energy margin dips as low as 42.3% and data margin as low as 33.6%. Finally, we found that RASP takes on the order of 10 seconds to create a feasible plan for the length of one orbit, suggesting that the algorithm is suitable for adaptation to a more computationally constrained onboard processor system.
