Show simple item record

dc.contributor.authorDrayer, Gregorio E.en_US
dc.contributor.authorHoward, Ayanna M.en_US
dc.date.accessioned2013-07-18T20:31:07Z
dc.date.available2013-07-18T20:31:07Z
dc.date.issued2012-07
dc.identifier.citationG. 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.urihttp://hdl.handle.net/1853/48469
dc.description©2012 by the American Institute of Aeronautics and Astronautics, Inc.en_US
dc.descriptionPresented at the 42nd International Conference on Environmental Systems (ICES), 15-19 July 2012, San Diego, California.en_US
dc.descriptionDOI: 10.2514/6.2012-3408en_US
dc.description.abstractRegenerative 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.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectRegenerative life support systemsen_US
dc.subjectData fusionen_US
dc.subjectSituation observabilityen_US
dc.subjectFuzzy associative memoryen_US
dc.subjectAggregationen_US
dc.subjectOptimizationen_US
dc.titleHuman-Expert Data Aggregation for Situation-Based Automation of Regenerative Life Support Systemsen_US
dc.typeProceedingsen_US
dc.typePost-printen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Human-Automation Systems Laben_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Electrical and Computer Engineeringen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Center for Robotics and Intelligent Machinesen_US
dc.publisher.originalAmerican Institute of Aeronautics and Astronautics, Inc.en_US
dc.identifier.doi10.2514/6.2012-3408


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record