Swarm Intelligence: a New C2 Paradigm with an Application to The

Swarm Intelligence: a New C2 Paradigm with an Application to The

SwarmSwarm Intelligence:Intelligence: aa NewNew C2C2 ParadigmParadigm withwith anan ApplicationApplication toto thethe ControlControl ofof SwarmsSwarms ofof UAVsUAVs PaoloPaolo GaudianoGaudiano IcosystemIcosystem CorporationCorporation Cambridge,Cambridge, MA,MA, USAUSA www.icosystem.comwww.icosystem.com OverviewOverview • UAVs: Definition and Examples • Complex Systems and Swarm Intelligence • Agent-Based Modeling • ABM for the control of UAV Swarms • Conclusions and future work 2 UAV:UAV: DefinitionDefinition A powered, aerial vehicle that does not carry a human operator, uses aerodynamic forces to provide vehicle lift, can fly autonomously or be piloted remotely, can be expendable or recoverable, and can carry a lethal or non-lethal payload. Source: DoD UAV Roadmap 2002 3 ManyMany TypesTypes ofof UAVUAV 4 ControllingControlling MultipleMultiple UAVsUAVs Problem Statement: • Current UAVs require at least one operator per UAV • Technological advances make multi-UAV missions a near-term reality Need control strategies that allow one operator to monitor/control multiple UAVs 5 UAVUAV SwarmsSwarms asas ComplexComplex SystemsSystems A system is complex when: 1. It consists of a large number of elements 2. Significant interactions exist between elements 3. System exhibits emergent behavior: cannot predict system behavior from analysis of individual elements Traditional “reductionist” approaches cannot cope with complex systems 6 TheThe IcosystemIcosystem GameGame 7 TheThe IcosystemIcosystem GameGame 8 TheThe BadBad NewsNews • Cannot predict emergent behavior from individual rules, even for such a “simple” complex system • Individual participants are unaware of overall system behavior • Small changes in rules lead to dramatically different emergent behaviors 9 TheThe GoodGood NewsNews • It is possible to manipulate the behavior of a complex system by changing the rules that control individual elements • We have developed a methodology to predict emergent behavior in complex systems using bottom-up simulation Agent-Based Modeling! 10 SampleSample ComplexComplex SystemsSystems 11 ControllingControlling EmergentEmergent BehaviorBehavior • How can we control emergence? • How do we define individual behaviors and interactions to produce desired emergent patterns? “Here is where we think the problem is... 12 AgentAgent--basedbased modelingmodeling • Shift viewpoint from system (centralized) to individual elements (de- centralized) • Each agent follows local rules • Behavior depends on interactions with other agents • Overall system behavior emerges from local interactions 13 Example:Example: FlowFlow SimulationsSimulations • Traditional approach: mathematical description at macroscopic level. • Example: fire diffusion in airplane cabin 14 LimitationsLimitations ofof TraditionalTraditional ApproachesApproaches • Previous simulation requires extensive computation • Any modification (e.g., number of seats, load, initial conditions) requires new computation Compare to agent-based approach 15 AgentAgent--basedbased FlowFlow SimulationsSimulations • The Game • Boids • Traffic 16 SwarmSwarm ControlControl ofof UAVsUAVs Supported by Air Force Research Labs SBIR • Create Agent-Based Model of UAV swarm • Test various swarm control strategies for two mission types: • Search (area coverage) • Search, track and hit targets (SEAD) • Measure performance systematically under various scenarios and conditions 17 TheThe UAVUAV AgentAgent--BasedBased ModelModel • Rectangular search area • 3-D motion: thrust, pitch, yaw control • GPS for localization • Probabilistic ground/target sensor • Circular collision sensor • Pheromone emitter & probabilistic sensor • Communications (noisy) to central control • Stationary or moving targets 18 Simulation:Simulation: AreaArea Coverage/SearchCoverage/Search ParameterParameter SettingsSettings PanelPanel 33-D-D ViewView AreaArea PanelPanel CoverageCoverage GridGrid 19 NavigationNavigation StrategiesStrategies • Baseline: fly straight until border is detected, turn to stay within search area • Random: inject small “jitter” in heading • Repulsion: avoid UAVs within radius r • Pheromone: avoid areas already covered (by self or others) • Global Search: favor navigation toward unexplored sectors (Strategies can be combined arbitrarily) 20 SampleSample CoverageCoverage PatternsPatterns Repulsion (r=60) Pheromone 21 SystematicSystematic EvaluationEvaluation Goal: Understand impact of strategies, parameter choices and scenarios: • 2000x2000 area, single UAV entry point • 1000-sec simulation • Swarm size (1-10, 10-110) • Navigation strategies (individual & combo) Metrics: • Area coverage • Swarm coverage efficiency • Per-UAV coverage efficiency 22 BaselineBaseline StrategyStrategy Per-UAVPer-UAVEfficiencyEfficiency EfficiencyEfficiency ofof SwarmSwarm ofof SwarmSwarm StrategiesStrategies StrategiesStrategies 10.0%10.0% Total coverage increases 70.0%70.0% with swarm size... 9.0%9.0% 60.0%60.0% 50.0%50.0%8.0%8.0% 40.0%40.0% 7.0%7.0% BaseBase 30.0%30.0% Coverage Coverage 6.0%6.0% 20.0%20.0% ...but per-UAV coverage Coverage per UAV Coverage per UAV 5.0%5.0% efficiency decreases with 10.0%10.0% swarm size. 4.0%0.0%4.0%0.0% 12468101246810 UAVsUAVs 23 RandomRandom NoiseNoise StrategyStrategy Per-UAVPer-UAVEfficiencyEfficiency Efficiency Efficiency of of Swarm Swarm of of Swarm Swarm Strategies Strategies Strategies Strategies EffectEffect ofof randomrandom jitterjitter 10.0%10.0% 70.0%70.0% isis largelylargely independentindependent 9.0%9.0% 60.0%60.0% ofof swarmswarm sizesize 50.0%50.0%8.0%8.0% 40.0% BaseBase 40.0%7.0% BaseBase 7.0% RandRand 30.0% RandRand Coverage 30.0% Coverage 6.0%6.0% 20.0%20.0% Coverage per UAV Coverage per UAV 5.0%5.0% 10.0%10.0% 4.0% 0.0%0.0%4.0% 12468101246810 UAVsUAVsUAVsUAVs 24 PheromonePheromone StrategyStrategy • Inspired by insect behavior • Example of stigmergy (communication through the environment) • Each UAV lays “pheromone” • Each UAV can sense local pheromone trace • Navigation favors uncovered areas (Urea Strategy?) 25 PheromonePheromone StrategyStrategy ResultsResults Per-UAVPer-UAVEfficiencyEfficiency Efficiency Efficiency of ofPheromonePheromone Swarm Swarm of of Swarm Swarm Strategies Strategies Strategies Strategies strategystrategy isis moremore effectiveeffective forfor 10.0%10.0% larger swarms 70.0%70.0% larger swarms 9.0% 60.0%60.0%9.0% 50.0%50.0%8.0%8.0% 40.0% Base 40.0%7.0% Base 7.0% Pher 30.0% Pher Coverage 30.0% Coverage 6.0%6.0% 20.0%20.0% Coverage per UAV Coverage per UAV 5.0%5.0% 10.0%10.0% 0.0%4.0%0.0%4.0% 12468101246810 UAVsUAVs 26 CombiningCombining StrategiesStrategies EfficiencyEfficiency of of Swarm Swarm Strategies Strategies 80.0%80.0% 70.0%70.0% 60.0% 60.0% BasBas e e Rand 50.0%50.0% Rand GlobGlob 40.0%40.0% PherPher 30.0% Coverage 30.0% P+G Coverage P+G 20.0%20.0% PGJPGJ 10.0%10.0% 0.0%0.0% 12468101246810 UAVsUAVs EvenEven aa relativelyrelatively simple,simple, decentralizeddecentralizedstrategystrategy cancan yieldyield significantsignificant improvementimprovement inin swarmswarm efficiency!efficiency! 27 ExtendingExtending toto LargeLarge SwarmsSwarms SwarmSwarm coverage coverage efficiency efficiency 35.0%35.0% 30.0%30.0% 25.0%25.0% 20.0%20.0% 15.0%15.0% Coverage Coverage 10.0%10.0% 5.0%5.0% 0.0%0.0% 1010 30 30 50 50 70 70 90 90 110 110 UAVsUAVs 28 AdditionalAdditional Results:Results: SEADSEAD • Allow targets to move randomly over search area • Extend UAV behavior to track targets • Modify simulator to carry out search and suppress missions • Apply evolutionary computing to identify robust strategies, parameters 29 ExtendedExtended SimulatorSimulator DemoDemo 30 SampleSample SEADSEAD ResultsResults CumulativeCumulative Hit Hit Probability Probability 100% 120%100%120% 90%90% 100%100% 80%80% 70% 80%70% 80% 4 UAVs 60%60% 4 Targets4 UAVs 48 Targets UAVs 50%60% 8 Targets8 UAVs 50%60% 816 Targets UAVs 16 Targets16 UAVs 40%40% 1632 Targets UAVs 40% 32 UAVs % Targets Hit % Targets Hit % Targets 30%40% % Targets Hit % Targets Hit % Targets 30% 20%20% 20%20% 10%10% 0% 0%0% 4816 444816 8 8 16 16 32 32 TargetsUAVsTargetsUAVs 31 FutureFuture WorkWork • Systematic evaluation of other mission types, criteria, performance metrics • Evolutionary design of control strategies • Human-in-the-loop control • Extend approach to Unmanned Ground Vehicles operating in urban scenario • Commercialize these and other results 32.

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