Preliminary results of the Autonomous Sciencecraft Experiment

The Autonomous Sciencecraft Experiment (ASE) operates onboard the Earth Orbiter 1 mission in 2004. The ASE software uses onboard continuous planning, robust task and goal-based execution, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting. In This work we discuss how these AI technologies are synergistically integrated in multi-layer control architecture to enable a virtual spacecraft science agent. We also present the preliminary results from flight validation of this experiment. This software demonstrates the potential for space missions to use onboard decision-making to detect, analyze, and respond to science events, and to downlink only the highest value science data. As a result, ground-based mission planning and analysis functions were simplified, thus reducing operations cost.