Preference-Based Multi-Robot Planning for Nuclear Power Plant Online Monitoring and Diagnostics

摘要

AbstractCurrent preventative maintenance paradigms in nuclear power plants carry several costly risks and challenges associated with component downtime and the need for human data collection. Preventative maintenance may be enabled by an online monitoring system that accurately assesses component condition and identifies potential faults. We present an approach for autonomous online monitoring and multiagent planning for robotic data collection. Under the occurrence of a fault, we utilize a machine learning model to form an initial guess of its nature, which we then refine by selectively measuring certain variables to gain additional information via a situation-aware variable selection model. To generate a multi-robot plan to conduct these measurements, we develop a preference-based planning framework within a linear temporal logic–based planning approach that prioritizes collecting data from the most important features. Finally, we demonstrate our approach on a case study using a simulated nuclear power plant circulating water system, showing fault diagnostic performance as well as simulated robot data collection.Keywords: Multi-robot task planninglinear temporal logiconline monitoringfault diagnosisnuclear power plant operation NomenclatureA== alphabet of Boolean formulasA== agent transition systemaA== agent actionaG== team automaton actionaP== product automaton actioncA== agent action costcG== team automaton action costcP== product automaton action costcβ== team cost weight vectorcˆβ== augmented team cost vectorE== set of graph edgesea,r== decision variable for agent-action pairF== set of accepting final states in NFAF== nondeterministic finite automatonG== graphG== team automatonN== number of agentso== ordering weightP== product automatonq== NFA stateR== real numbersR== temporal logic releasesA== agent statesG== team automaton statesP== product automaton stateU== temporal logic untilV== set of graph vertices◯== temporal logic next∧== logical and∨== logical or♢== temporal logic eventually¬== logical not→== logical implication□== temporal logic alwaysGreekα== team preference cost weightβ== sequence in team automatonδ== NFA transition relationϵ== team makespan cost weightζ== switch transitionκˆ== team cost functionλ== labeling functionπ== atomic propositionσ== temporal sequenceϕ== linear temporal logic specificationψ== preference costAcknowledgmentsThe authors would like to thank the U.S. Department of Energy, Office of Nuclear Energy, Distinguished Early Career Program for funding this work, award number DE-NE0009306.Disclosure StatementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the Office of Nuclear Energy [DE-NE0009306].