Control of Artificial Swarms with DDDAS · Control of Artificial Swarms with DDDAS...

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Control of Artificial Swarms with DDDAS R Ryan McCune & Greg Madey Department of Computer Science & Engineering, University of Notre Dame 2014 InternaConal Conference on ComputaConal Science Dynamic DataDriven ApplicaCon Systems (DDDAS) Track June 10, 2014 Cairns, Australia

Transcript of Control of Artificial Swarms with DDDAS · Control of Artificial Swarms with DDDAS...

Page 1: Control of Artificial Swarms with DDDAS · Control of Artificial Swarms with DDDAS RRyan"McCune"&"Greg" Madey" " Departmentof"Computer"Science"&"Engineering,"University"of"Notre"Dame"

Control of Artificial Swarms with DDDAS

R  Ryan  McCune  &  Greg  Madey    

Department  of  Computer  Science  &  Engineering,  University  of  Notre  Dame  2014  InternaConal  Conference  on  ComputaConal  Science  Dynamic  Data-­‐Driven  ApplicaCon  Systems  (DDDAS)  Track  

June  10,  2014  Cairns,  Australia  

 

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Overview •  Mo#va#ng  problem  –  UAVs  •  Solu#on  –  Swarm  Intelligence  •  Approach  –  DDDAS  •  Framework  for  swarm  control  with  DDDAS  •  Applica#on  example  – Swarm  intelligent  ant  foraging  – UAV  clustering  

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Operator Overload •  Unmanned  Aerial  Vehicles  (UAVs)  – No  on-­‐board  pilot  – Intelligence,  Surveillance,  and  Reconnaissance  (ISR)  missions  – Becoming  smaller  and  cheaper  with  increased  capabili#es  

•  How  to  efficiently  operate  many  UAVs?  

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Swarm Intelligence •  Biologically  inspired  – Ant  colonies,  flocks  of  birds,  fish  

•  Emergent  phenomena  –  Simple,  local  behavior  of  agents  –  Complex,  global  behavior  of  system  

•  Self-­‐organiza#on  – Decentralized  

•  Emergent  behavior  solves  problems  

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DDDAS •  Entails:  – Dynamic  incorpora#on  of  addi#onal  data  into  execu#ng  simula#on  

– Simula#on  results  control  measurement  process  

•  Symbio#c  feedback  loop  •  Improved  predic#ve  capabili#es    

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UAVs  (Sensors)  

Sim  (App)  

Sensor  Data  

Control  

UAV  Swarm  Applica#on  

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Framework for Swarm Control with DDDAS •  Swarm  Applica#on  Architecture  – One  (or  few)  agent  parameters  – Swarm  performance  reported  by  single  sta#s#c  

•  Feedback  Control  Loop  – Calibrate  with  real-­‐#me  data  – Sweep  of  parameters  – Op#miza#on  via  simula#on  

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Swarm Application Architecture

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DDDAS Feedback Control Loop

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Example – General Ant Foraging

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•  Ants  search  for  food  to  bring  back  to  nest  

•  Randomly  search  environment  •  Deposit  pheromones  while  searching  – Likely  to  follow  pheromones  – Random  Ac#on  Probability  (RAP)  

•  Shortest  path  emerges  

RAP  =  ρ  

1  –  ρ  Follow  highest  pheromone    

ρ  Random  direc#on  

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Ant Foraging - An Implementation[1] •  Ants  deposit  2  pheromones  – Green  lead  to  home,  deposit  while  foraging  – Blue  lead  to  food,  deposit  while  returning  home  

 

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[1]  Panait,  Liviu,  and  Sean  Luke.  "A  pheromone-­‐based  u#lity  model  for  collabora#ve  foraging."  Proceedings  of  the  Third  Interna#onal  Joint  Conference  on  Autonomous  Agents  and  Mul#agent  Systems-­‐Volume  1.  IEEE  Computer  Society,  2004.  

•  1  ant  hill  –  Sta#onary  

•  1  food  –  Unlimited  

•  Many  ants  

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[1]  Panait,  Liviu,  and  Sean  Luke.  "A  pheromone-­‐based  u#lity  model  for  collabora#ve  foraging."  Proceedings  of  the  Third  Interna#onal  Joint  Conference  on  Autonomous  Agents  and  Mul#agent  Systems-­‐Volume  1.  IEEE  Computer  Society,  2004.  

Ant  Hill  

Food  

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Swarm Clustering •  Adapted  from  ant  foraging  – Many  food  instead  of  1  food  – Many  ant  hills  instead  of  1  ant  hill  •  Ant  hills  can  move  (right)  

– Only  1  pheromone  type,  not  2  •  Deposit  when  looking  for  food  •  Follow  to  return  to  ant  hill  •  No  pheromone  leads  to  food  •  Once  any  food  is  found  randomly,        pheromone  leads  to  nearest  ant  hill  

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1.  Ant  finds  food  

2.    Ant  returns  to  nest  

3.  Nest  moves  closer                      to  food  

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Tanker  Moves  

Behavior

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Simulation Snap-Shots •  Swarm  clustering  simula#on  

•  Voronoi  diagram  overlay  – Tankers  as  seed  points  

•  Ants  are  dots  

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Swarm Application Architecture

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•  Swarm  parameter    – Random  Ac#on  Probability  (RAP)  

•  Performance  sta#s#c  – Pheromone  concentra#on  Variance  

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Swarm Clustering with DDDAS

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Conclusions •  Swarm  intelligence  to  control  UAVs  •  U#lize  DDDAS  paradigm  for  improved  capability  

•  Introduce  framework  for  swarm  control  with  DDDAS  

•  Swarm  clustering  example  applica#on  

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Acknowledgements •  Air  Force  Office  of  Scien#fic  Research  (AFOSR)  DDDAS  program  award  #  FA9550-­‐11-­‐1-­‐0351  

•  GAANN  Fellowship  provided  by  the  Department  of  Educa#on  – Managed  by  the  University  of  Notre  Dame  Computer  Science  Department  

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