Current ground robots are largely employed via tele-operation and provide their operators with useful tools to extend reach, improve sensing, and avoid dangers. To move from robots that are useful as tools to truly synergistic human-robot teaming, however, will require not only greater technical capabilities among robots, but also a better understanding of the ways in which the principles of teamwork can be applied from exclusively human teams to mixed teams of humans and robots. In this respect, a core characteristic that enables successful human teams to coordinate shared tasks is their ability to create, maintain, and act on a shared understanding of the world and the roles of the team and its members in it. The team performance literature clearly points towards two important cornerstones for shared understanding of team members: mental models and situation awareness. These constructs have been investigated as products of teams as well; amongst teams, they are shared mental models and shared situation awareness. Consequently, we are studying how these two constructs can be measured and instantiated in human-robot teams. In this paper, we report results from three related efforts that are investigating process and performance outcomes for human robot teams. Our investigations include: (a) how human mental models of tasks and teams change whether a teammate is human, a service animal, or an advanced automated system; (b) how computer modeling can lead to mental models being instantiated and used in robots; (c) how we can simulate the interactions between human and future robotic teammates on the basis of changes in shared mental models and situation assessment.
Decentralizedhybridenergysystemsarepromisinglong-lastingsolutionstosupportsocio-economicdevelopmentincompliancewithenvironmentalconcerns.Traditionally,microgridplanninghasmainlyfocusedoneconomicsonly,sometimeswithreliabilityorenvironmentalconsiderations,andtheprojectcostshavebeenestimatedbyapproximatingthemulti-yearoperationofthesystemwithasingle-yearapproach,thusneglectinglong-termphenomena.Weproposeamulti-objectivemulti-yearmethodtoplanmicrogridsintheGlobalSouth,accountingforsocio-economic(NetPresentCost,jobcreation),security(publiclightingcoverage)andenvironmentalimpacts(carbonemissions,landuse);theentiremulti-yearlifespanoftheprojectisconsidered,includingdemandgrowthandassetsdegradation.Theadvancedversionoftheaugmentedε-constraintalgorithm,denotedasA-AUGMECON2,ishereproposedtoefficientlysolvethemulti-objectivemodel,byusinganovelpruningalgorithmthatavoidssolvingredundantoptimizations.ThemethodisappliedtoanisolatedcommunityinUganda.Theapproachsuccessfullyquantifiesthetrade-offbetweenlocallong-termimpacts,supportingpolicymakersandlocaldevelopersindesigningeffectivepoliciesandactions.Inparticular,ourresultssuggestthattheenvironmentaltargetscanbealignedwiththeprojecteconomics,andthatthefinancialimpactofpubliclightingislimited,whichencouragesitsimplementationinelectrificationprojects.Conversely,optimallanduseandjobcreationleadtohigheconomicandenvironmentalcosts,highlightingtheneedforatrade-offforpolicyandbusinessdecisionmakers.Moreover,thenovelA-AUGMECON2algorithmenablesreducingby48%thecomputationalrequirementsofthestandardAUGMECON2,extendingtheapplicationofmulti-objectivemethodologiestomorecomplexproblems.
Electricvehicle(EV)batteriescanprovideextendedvaluebeyondEVserviceiftheyarerepurposedfora“secondlife”inelectricitygridapplications.However,becausebatteriesfromdifferentEVmakesandmodelsvarysignificantlybysize,shape,chemistry,andthermalmanagement,thereisuncertaintyregardingtheirrelativeperformanceinsecond-lifeapplications.ThisexperimentalstudyevaluatessevendifferentEVbatteriesintheiroriginalmodulesand/orpacks,featuringfouruniquepositiveactivematerials,twonegativeactivematerials,threecellformats,andfourthermalmanagementdesigns.Eachbatteryissubjectedtodeep-dischargecyclingat4?h,2?h,and1?hconstant-powerratestoemulateperformanceinelectricitygridenergyarbitrage.Testresultsareevaluatedbasedonsixbatteryperformancemetricsinthreekeyperformancecategories,includingtwoenergymetrics(usableenergycapacityandcharge–dischargeenergyefficiency),onevolumemetric(energydensity),andthreethermalmetrics(averagetemperaturerise,peaktemperaturerise,andcycletime).Significantdifferencesinperformancearisefromthevarietyofchemistriesandthermalmanagementsystemstested,dominatinganyinfluencefrombatterystateofhealth.ChevroletVoltandEnerDelbatteries(bothfromhybridEVsusingNMCchemistry)givethebestusableenergycapacity(≥94%)andenergyefficiency(≥97%),whileTeslaModelSbatteries(fromlong-rangeEVsusingNCAchemistry)givethelowestusableenergycapacity(≥84%)andenergyefficiency(≥89%).However,theModelSbatteriesgiveroughlydoubletheenergydensity(halfthephysicalfootprint)oftheVoltandEnerDelbatteries.TheVoltbatteryexperiencesnomorethan2?°Cofwarmingevenduringa1?hdischarge,thankstoitsactive(forced)liquidthermalmanagementandhighenergyefficiency.ThiscontrastswiththeLeafandLishenbatteries,whichusepassive(naturalconvection)thermalmanagementandconsequently
This paper presents an overview of a newly developed Coupled Layer Architecture for Robotic Autonomy (CLARAty), which is designed for improving the modularity of system software while more tightly coupling the interaction of autonomy and controls. First, we frame the problem by briefly reviewing previous work in the field and describing the impediments and constraints that been encountered. Then we describe why a fresh approach to the topic is warranted, and introduce our new two-tiered design as an evolutionary modification of the conventional three-level robotics architecture. The new design features a tight coupling of the planner and executive in one Decision Layer, which interacts with a separate Functional Layer at all levels of system granularity. The Functional Layer is an object-oriented software hierarchy that provides basic capabilities of system operation, resource prediction, state estimation, and status reporting. The Decision Layer utilizes these capabilities of the Functional Layer to achieve goals by expanding, ordering, initiating and terminating activities. Both declarative and procedural planning methods are used in this process. Current efforts are targeted at implementing an initial version of this architecture on our research Mars rover platforms, Rocky 7 and 8. In addition, we are working with the NASA robotics and autonomy communities to expand the scope and participation in this architecture, moving toward a flight implementation in the 2007 time-frame.
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