Dr. Stephen K. Robinson
University of California, Davis
Habitats Optimized for Missions of Exploration (HOME)
To safely execute the human-exploration missions currently being proposed by NASA, the design of deep-space habitats requires a fundamentally disruptive approach – one that relies not only on traditional subsystem reliability engineering and probabilistic risk analysis, but on emergent technologies in autonomous systems, failure-tolerant design, human/automation teaming, dense sensor populations, data science, machine learning, robotic maintenance, and on-board manufacturing. Accordingly, the vision for the proposed HOME STRI for Deep Space Habitat Design is to synthesize the ideas and backgrounds of a uniquely experienced, diverse, and operationally pragmatic research team with initially-low TRL innovative technologies to provide a 5-year flow of research deliverables that comprise a new paradigm for resilient, autonomous, and self-maintained deep-space habitats for human explorers. The scope of the HOME STRI’s research will be driven by two primary operational requirements of NASA’s deep-space habitats:
- Keep humans alive while they are resident, and
- Keep the vehicle alive while they are not.
A highly autonomous deep-space habitat for human crews requires three major classes of control: autonomy, robotics, and humans – the interactions and interdependencies between these three domains comprise the research landscape for the HOME Space Technology Research Institute. The goal of the STRI is to develop validated technology to help NASA ensure safe human habitability during crewed deep-space missions, and to maintain the habitat to support future crews even while uninhabited.
The proposed STRI brings together a deeply experienced and broadly multi-disciplinary team:
University of California, Davis (lead institution) – spacecraft design, human/automation performance, robotics, machine-learning for control, smart power systems, human/system integration, failure response
University of Colorado, Boulder – habitat functional decomposition, human life support, space human/system integration with automation, space simulations/analogs, tech evaluation
Carnegie Mellon University – smart infrastructure, sensor system design, facility management, AI reasoning for multi-robot systems, human-centered robotics, predictive analytics, human activity forecasting, additive manufacturing
Georgia Institute of Technology – sensors, data-driven predictive analytics, machine learning, asset management, knowledge based robotics, collaborative autonomy, human-robot interaction, robot tasking
Howard University – Architecture, habitat volume distribution, human habitability
University of Southern California – Spacecraft Design for Crew Safety Texas A&M – On-Board Manufacturing, Metallic additive manufacturing
Blue Origin – aerospace manufacturer and spaceflight services developer
Sierra Nevada Corporation – aerospace manufacturer and NASA contractor for both deep-space habitats and ISS resupply services
United Technologies Aerospace Systems – aerospace R&D company with expertise in life-support systems for spacecraft and spacesuits
We propose to extend existing SOA technology through team-based research in the following hierarchy of research areas, each of which will have a family of interdependent objectives:
A. Smart Habitat Intervention
- Decision-making tools/algorithms for maintenance/repair options: predictive analytics, autonomous models for logistical decisions
- Response actions: intervention spectrum (autonomous, robotic, remote, onboard crew), onboard manufacturing of parts and sensors, de-rate system to lower capacity, robotic repair/module swap (internal vs external requirements)
B. Smart Architecture and Analysis
- Systems engineering approach for mission and system functional requirements
- Spacecraft habitat concept (for context): will work from two Design Reference Missions – a Gateway-class vehicle in microgravity, and a surface hab in partial gravity
- Subsystem definition: especially robust ECLSS systems
- Software/algorithms for: system state estimation, sensor data reliability, diagnostics, state dynamic modeling, and prognostics
- Development of multi-system simulators in which to integrate and test algorithms, analytics, and decision-optimization software innovations, and to serve as a testbed for the development of metrics for system performance and sensitivities
C. Smart Context and Situation Awareness
- Human involvement: humans as neuro-plastic sensors, servos, effectors, data aggregators, robotic operators, teammates, innovators, communicators, reconfiguration agents, error-inducers, repair agents
- Measure, model, predict, and modify human behavior while interacting with the system autonomy. Develop Virtual/Augmented Reality applications for crew interaction/training
- Human health and performance: medical and psych, sustaining, injuries, interventions, emergency care, on-board training, teaming, trust, evaluation
