Utility-driven Scheduling for a Europa Lander Mission Concept

A proposed Europa Lander Mission presents unique challenges. The concept lander would land with a fixed amount of energy to accomplish its objectives of excavating, sampling, and analyzing samples in search of astrobiology signatures (and downlinking results). The mission concept must be robust despite uncertainties about remaining energy, time/energy to excavate and sample, sample analysis results, and data volume to downlink. Additionally, the lander would be out of contact with the Earth for over 42 out of every 84 hours due to the Europan orbit around Jupiter. Finally, intense radiation from Jupiter will eventually degrade lander operations at an unpredictable time.

We describe an autonomy prototype based on the Multimission Executive – Europa Lander (Mexec-EL) to address these challenges. Mexec-EL uses a flexible execution strategy in concert with replanning and plan optimization to dynamically adjust the mission in response to execution feedback such as from excavation, sampling, analysis, data volume, and energy estimation. We discuss our plan optimization and utility-based approach in the context of Europa Lander Mission concept use cases including excavation variation, changing sample location utility estimates, and energy variations. We also discuss our approach and contrast it to a more complete decision-theoretic approach and other related work.

Bio
Dr. Steve Chien is a Technical Fellow, Senior Research Scientist, and (Co-) Head of the Artificial Intelligence Group, at the Jet Propulsion Laboratory, at the California Institute of Technology. He has played a key role in the deployment of AI software to a wide range of missions including Autonomous Sciencecraft on EO-1, Earth Observing Sensorweb, and the M2020 Perseverance Rover.