Title:
Human-Expert Data Aggregation for Situation-Based Automation of Regenerative Life Support Systems

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dc.contributor.author Drayer, Gregorio E. en_US
dc.contributor.author Howard, Ayanna M. en_US
dc.contributor.corporatename Georgia Institute of Technology. Human-Automation Systems Lab en_US
dc.contributor.corporatename Georgia Institute of Technology. School of Electrical and Computer Engineering en_US
dc.contributor.corporatename Georgia Institute of Technology. Center for Robotics and Intelligent Machines en_US
dc.date.accessioned 2013-07-18T20:31:07Z
dc.date.available 2013-07-18T20:31:07Z
dc.date.issued 2012-07
dc.description ©2012 by the American Institute of Aeronautics and Astronautics, Inc. en_US
dc.description Presented at the 42nd International Conference on Environmental Systems (ICES), 15-19 July 2012, San Diego, California. en_US
dc.description DOI: 10.2514/6.2012-3408 en_US
dc.description.abstract Regenerative life support systems (RLSS) introduce novel challenges for the development of automation systems given the emerging behaviors that result from incremental system closure. Switching control paradigms offer the ability to manage such uncertainty by allowing flexibility into the control path, enabling for autonomy modes that depend on the situation of the system. Previous research proposed a granular approach that combines sensor information to define operation conditions and act upon them. It makes use of fuzzy associative memories (FAM) to define the pairs (Situation, Controller) that assign control actions to each situation. The FAM are composed granules that represent situations in which the autonomous system may operate. One of the challenges of this approach is the combinatorial explosion that arises for large numbers of sensors. Human-system interaction offers a solution to this problem and, for such purpose, this paper elaborates on the aggregation of human-expert data to obtain the granular structure of the FAM. The aggregation process consists of an optimization process based on particle swarms. The result is a three dimensional array with parameters that define n-dimensional non-interactive granules. Two alternatives are presented in this paper: (1) a four-dimensional optimization algorithm to obtain normal fuzzy sets, and (2) a five-dimensional alternative that results in subnormal fuzzy sets. The results were obtained with simulations of an aquatic habitat that serves as a small-scale model of a RLSS. The discussion elaborates on which of the two alternatives may be better suited for applications in situation assessment and automation. en_US
dc.identifier.citation G. Drayer, A. Howard, “Human-Expert Data Aggregation for Situation-Based Automation of Regenerative Life Support Systems,” 42nd International Conference on Environmental Systems (ICES), 15-19 July 2012, San Diego, California. en_US
dc.identifier.doi 10.2514/6.2012-3408
dc.identifier.uri http://hdl.handle.net/1853/48469
dc.language.iso en_US en_US
dc.publisher Georgia Institute of Technology en_US
dc.publisher.original American Institute of Aeronautics and Astronautics, Inc. en_US
dc.subject Regenerative life support systems en_US
dc.subject Data fusion en_US
dc.subject Situation observability en_US
dc.subject Fuzzy associative memory en_US
dc.subject Aggregation en_US
dc.subject Optimization en_US
dc.title Human-Expert Data Aggregation for Situation-Based Automation of Regenerative Life Support Systems en_US
dc.type Text
dc.type.genre Proceedings
dc.type.genre Post-print
dspace.entity.type Publication
local.contributor.author Howard, Ayanna M.
local.contributor.corporatename School of Civil and Environmental Engineering
local.contributor.corporatename Institute for Robotics and Intelligent Machines (IRIM)
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relation.isOrgUnitOfPublication 88639fad-d3ae-4867-9e7a-7c9e6d2ecc7c
relation.isOrgUnitOfPublication 66259949-abfd-45c2-9dcc-5a6f2c013bcf
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