Multi-agent and Optimization Reasoner for Space Exploitation (MORSE)

Future space missions will rely heavily on automated multi-agent cyber-physical-human teams to perform a number of tasks, such as robotic servicing, habitat maintenance, health management, etc.nbsp;In order for these multi-agents to become a reality, trust in them and uncertainty quantification will need to be factored into decision making policies by the system participants.nbsp;Even if one assumes perfect information the problem is NP-Hard, thus timely optimal solutions are unachievable. The central objective of the proposed technology is to provide near optimal mission planning for autonomous multi-agents with uncertain and possibly untrustworthy data sources using a hybrid deep reinforcement learning-optimization approach.nbsp;A new architecture will be developed under this optimization approach that will allow for consideration of uncertainty and trustworthiness in planning decisions.nbsp;A use-case that incorporates realistic uncertainties will be employed to provide metrics on the proposed approach.nbsp;The use-case involves multiple satellites working in coordination to provide vital information to ground agents, each with a task of resupplying outlying bases.nbsp;Past results by the investigators provide a basis-for-optimism that the proposed approach is viable.nbsp;The Phase I effort will focus on extensive simulation studies and analyses. nbsp;This will build a foundation to develop benchmark testing at the onset of Phase II, with the end of this work being a fully functional demonstration unit.