NASA SBIR 2017 Solicitation

FORM B - PROPOSAL SUMMARY


PROPOSAL NUMBER: 171 H6.03-8666
SUBTOPIC TITLE: Spacecraft Autonomous Agent Cognitive Architectures for Human Exploration
PROPOSAL TITLE: A Cognitive Architecture Using Reinforcement Learning to Enable Autonomous Spacecraft Operations

SMALL BUSINESS CONCERN (Firm Name, Mail Address, City/State/Zip, Phone)
Onu Technology, Inc.
7280 Blue Hill Dr., Suite 2
San Jose, CA 95129 - 3624
(408) 714-9253

PRINCIPAL INVESTIGATOR/PROJECT MANAGER (Name, E-mail, Mail Address, City/State/Zip, Phone)
Dr. Volkmar Frinken
volkmar@onutechnology.com
7280 Blue Hill Dr., Suite 2
San Jose, CA 95129 - 3624
(669) 231-9200

CORPORATE/BUSINESS OFFICIAL (Name, E-mail, Mail Address, City/State/Zip, Phone)
Dr. Guha Jayachandran
guha@onutechnology.com
7280 Blue Hill Dr., Suite 2
San Jose, CA 95129 - 3624
(408) 714-9253

Estimated Technology Readiness Level (TRL) at beginning and end of contract:
Begin: 1
End: 3

Technology Available (TAV) Subtopics
Spacecraft Autonomous Agent Cognitive Architectures for Human Exploration is a Technology Available (TAV) subtopic that includes NASA Intellectual Property (IP). Do you plan to use the NASA IP under the award?
No

TECHNICAL ABSTRACT (Limit 2000 characters, approximately 200 words)
We propose an architecture to enable the modular development and deployment of autonomous intelligent agents in support of spacecraft operations. This architecture supports both training and application of artificial intelligence models. It particularly enables the use of deep reinforcement learning for each module independently and jointly. Deep reinforcement learning is a technique that enables the automated learning of plans of action and has recently successfully been used, for example, to learn strategies for games like Go. Our proposed architecture provides a "utility" layer for generalized learning and a provides for independent functional modules that can be added, modified, or removed easily. It also accounts for intensive multicore computational needs. Lastly, it allows for desired behavior to be learned independently or in the context of the broader system. In Phase I, we will deliver a preliminary cognitive architecture, a feasibility study, a prototype of an autonomous agent, and a detailed plan to develop a comprehensive cognitive architecture feasibility study.

POTENTIAL NASA COMMERCIAL APPLICATIONS (Limit 1500 characters, approximately 150 words)
The proposed technology can be deployed as a backup in current spacecraft and can play a foundational role in upcoming deep-space missions, which will require higher levels of autonomy than current missions. The system proposed can autonomously manage many spacecraft operations, including systems health, crew health, maintenance, consumable management, payload management, food production, and recycling.

POTENTIAL NON-NASA COMMERCIAL APPLICATIONS (Limit 1500 characters, approximately 150 words)
The proposed technology can be applied in any setting that would benefit from robust, autonomous management, including airplane piloting, autonomous or semi-autonomous trucking, decision support, and medicine.

TECHNOLOGY TAXONOMY MAPPING (NASA's technology taxonomy has been developed by the SBIR-STTR program to disseminate awareness of proposed and awarded R/R&D in the agency. It is a listing of over 100 technologies, sorted into broad categories, of interest to NASA.)
Algorithms/Control Software & Systems (see also Autonomous Systems)
Autonomous Control (see also Control & Monitoring)
Intelligence
Perception/Vision
Simulation & Modeling
Spacecraft Instrumentation & Astrionics (see also Communications; Control & Monitoring; Information Systems)

Form Generated on 04-19-17 12:59