National Aeronautics and Space Administration
Small Business Innovation Research 2001 Program Solicitation

TOPIC H6 Human Exploration and Expeditions

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H6.01 Automated Rendezvous, Docking and Capture
H6.02 Robotics Assistance, Assembly, Maintenance, and Servicing

The goals of this topic include: working collaboratively with technology developments in Space Science (and other organizations) to enable future human exploration missions to effectively address -- and at a fundamental level -- the "grand" science challenges facing NASA, driving down the cost of human exploration missions and campaigns beyond Earth orbit, and sharing the experience of exploration with the public. In pursuing these goals, the objectives under this topic include: 1) Developing and validating the capability for human explorers to gain deep lunar and planetary sub-surface knowledge and access -- both remotely and through sampling -- ranging down to 1000s of meters. 2) Enabling safe and affordable human exploration of other planetary surfaces -- locally but over global distances involving traverses of up to 1000s of kilometers. 3) Integrating and validating the technologies needed to revolutionize public engagement in "virtual exploration" -- ranging from higher rate communications, to the creation of virtual reality simulations, to innovative human-machine interfaces. 4) Establishing a foundation for profitable commercial development of space applications of these technologies in the mid- to far-term.

H6.01 Automated Exploration Applications of Intelligent Systems
Lead Center: ARC
Participating Center(s): JSC, MSFC

NASA is planning to fill space with robotic explorers, carrying our intelligence and our curiosity outward in ways never before possible. To survive decades of operation, these remote agents need to be smart, adaptable, curious, wary, and self-reliant in harsh and unpredictable environments. NASA is soliciting research in automated reasoning for autonomous systems that will enable the design, construction and operation of a new generation of remote agents that perform progressively more exploration at much lower cost than traditional approaches. NASA also needs automated reasoning to improve its operations closer to home. For instance, software is needed for monitoring shuttle and space station systems and diagnosing faults when they occur or software agents for processing, classifying and archiving the mountains of data from Earth orbiting satellites. Specific areas of interest for automated reasoning include the following:

Agent Architectures

• Autonomy architectures that support plug and play of automated reasoning components
• Architectures for homogeneous and heterogeneous distributed systems of agents Capabilities related to Autonomous Performance
• Planning and scheduling systems that support planning concurrent with execution, plan optimization, resource management and/or distributed plan creation capabilities
• Model-based and statistical methods for monitoring, command confirmation, fault isolation, and diagnosis from sensor information
• Methods for robust recovery and repair
• Algorithms for real-time deduction and search
• Novel environment sensing or mapping capabilities
• Machine learning and adaptive control technologies
• Methods for precisely and dynamically adjusting the level of human control

Capabilities Related to Design

• Declarative specification of software and hardware behaviors, collaborative environments for large scale model building
• Methods for code synthesis and controller generation from declarative specifications
• Automated generation of test sequences from component models and analytic verification methods, including model checking and theorem proving
• Methods for modeling, code synthesis, simulation, testing and validation, as above, that operate from hybrid discrete/continuous models

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H6.02 Flight/Ground Operations and Crew Training
Lead Center: JSC
Participating Center(s): MSFC

Dramatic improvements will be needed in crew and ground operations performance and productivity as NASA develops new operational capabilities to support multiple, manned missions and long duration and long distance missions. Robotic, vehicle and support systems will be required to be more robust, autonomous and intelligent as well as more maintainable. These capabilities will allow operators to "buy time" by increasing system mean time between failures, predicting when intervention will be needed, managing degraded operations, and using functional redundancy. Advanced capabilities for information, data analysis, presentation, onboard planning, execution and fault management will be needed to assist the crew. Sophisticated training, maintenance support systems and a robust knowledge base will be needed onboard, and will need to be well integrated with increasingly advanced control and maintenance systems. Ground support operations will need to be redesigned to support the increasing autonomy of space systems and onboard crew. There will need to be advanced support for distributed and adjustable command responsibility and distributed and flexible training. Significantly, more productive and intuitive approaches are needed for updating, adapting, testing and certifying advanced distributed operations software and knowledge bases during missions. Specific areas of interest in the areas of crew training and in flight and ground operations include:

Crew Training and Maintenance Support Systems

• Flexible training and tutoring systems for mission operations support including distributed cooperative training, virtual reality training, intelligent computer-based training, and authoring tools
• Integration of training with advanced control and maintenance systems
• Tools to collect/capture and tailor design-time information for use in developing training materials
• Procedures or technology for evaluating effectiveness of innovative training methods
• Data Management, Data Analysis, and Presentation and Human Interaction
• Methods for selecting and summarizing vehicle systems and payload data relating to status and events to support crew and ground awareness, operational decision-making, and management by exception and opportunity rather than by continuous or scheduled monitoring
• Human interaction methods for collaboration, cooperation and supervision of intelligent semi-autonomous systems
• Goal-driven collaborative data analysis systems capable of adaptation and learning
• Simple systems for notification and coordination including natural language interfaces
• Immersive environments: real-time environments to enhance a human operator's ability to interact with large quantities of complex data, especially at distant locations
• Intelligent data analysis techniques: capabilities to interpret, explain, explore, and classify large quantities of heterogeneous data

Robust Planning, Operations, Fault Detection, and Recovery with Distributed Adjustable Command Responsibility

• Onboard planning, sequencing, monitoring, and re-planning of activities including systems and crew activities
• Flexible management of the actions of subsystems within the larger context of system flight rules and constraints
• Flexible and robust fault management approaches that use system models, "buy time" for human intervention and maintenance, and learn from human operators during and after the interventions
• Approaches to distributed and adjustable command responsibilities among systems, crew and ground
• Model-based continuous estimation of the likelihood of critical events, including human errors, to provide warnings of potential events and their consequences and to suggest appropriate countermeasures
• Integration of systems for fault management, maintenance and training
• Operations knowledge management and software updating
• Systems and processes for crew and ground operators to quickly and effectively define, update, test and certify operational knowledge and rule bases before and during missions designed for reuse in autonomous systems and in training
• Tools for incorporating and using engineering data and specifications (about equipment and its operating modes and failures and about operations procedures) into operations knowledge and model-based autonomous systems
• Tools and environments to support modification and validation of knowledge bases (models of activities, equipment and environment) in intelligent autonomous software by operators to capture methods and knowledge used by operators during interventions and to collaboratively adapt to unanticipated circumstances
• Simulation environments and tools for use in designing and testing intelligent semiautonomous systems
  

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