Evolving a Team of Self-organizing UAVs to Address Spatial Coverage Problems
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Evolving a Team of Self-organizing UAVs to Address Spatial Coverage Problems

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Talk given at EMCSR 2012 conference in Vienna.

Talk given at EMCSR 2012 conference in Vienna.

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  • Source: Photo by Wilfried Elmenreich

Evolving a Team of Self-organizing UAVs to Address Spatial Coverage Problems Evolving a Team of Self-organizing UAVs to Address Spatial Coverage Problems Presentation Transcript

  • Evolving a Team of Self-organizingUAVs to Address Spatial CoverageProblemsIstván Fehérvári, Wilfried Elmenreich and Evsen YanmazMobile Systems Group/Lakeside LabsInstitute for Networked and Embedded SystemsAlpen-Adria Universität Klagenfurt
  • Overview• Unmanned areal vehicles• The coverage problem• Discrete simulation model• Evolutionary approach• Results• Conlusion and outlook Wilfried Elmenreich 2
  • UAVs Wilfried Elmenreich 3
  • Battery-powered UAVs• Quadcopter platform with onboard sensors and electronic for flight stabilization• Attached cameras for sensing the environment• GPS receiver for autonomous waypoint flights• Limitations on payloads, flight time, weather conditions www.microdrones.de               www.asctec.de Wilfried Elmenreich 4 4
  • Small UAV Network for EmergencyAssistance UAV NetworkMission Control Wilfried Elmenreich 5 5
  • Coverage/Detection Problem• Wireless sensor networks • Coverage problem in – Civil robotics • Environmental monitoring: – Snow removal, lawn wildfires, volcanoes, glaciers, mowing, car-body painting, storms, agriculture fields • Communication assistance: floor cleaning, etc. serve as data fusion centers; • Cellular decomposition serve as mobile base stations • Complete versus randomized (relay) (probabilistic) • Video surveillance: traffic • Known layout of the monitoring, convoy protection environment versus sensor- based coverage – Military, security • Time-to-complete: shortest • Battlefield assistance; target path, minimum energy, detection and tracking; search minimum number of turns, etc and destroy; border monitoring Wilfried Elmenreich 6 6
  • Planning and MonitoringFlight Paths• Example for flight paths of two UAVs (green/red color) Wilfried Elmenreich 7
  • Simplified Discrete Simulation Model• finite two-dimensional lattice• each cell can contain at most one agent or obstacle• agent can move to one of four directly neighboring cells (vN neighborhood)• Goal: have each cell being visited at least once after minimum time Wilfried Elmenreich 8
  • Keep it Simple!• No a priori knowledge about map size/position of obstacles• No explicit communication among UAVs• No position estimation mechanism• No map building• Looking for a self-organizing online algorithm• Until now, planned offline algorithms have been used (e.g. using a TSP solver) Wilfried Elmenreich 9
  • Wanted: the right UAV behavior model• Controls the UAV as autonomous agent• Processes inputs (from sensors) and produces output (to actuators) Control System „Agent‘s Brain“ Wilfried Elmenreich 10
  • EvolutionaryApproach Wilfried Elmenreich 11
  • Evolving the Control System• Simulation of target system as System model testing playground Goals (fitness function) Simulation of problem• Define goal via fitness function (e.g., maximize throughput in a network)• Run evolutionary algorithm to derive Explore solutions behavior fulfilling the given goal• Representation must be evolvable Evaluate • Mutation & Iterate • Recombination Analyze • difficult with an algorithm represented in results C or Java code… Wilfried Elmenreich 12
  • Artificial Neural Networks• Each neuron sums up the weighted outputs of the other connected neurons• The output of the neuron is the result of an activation function (e.g. step, sigmoid function) applied to this sum• Neural networks are distinguished by their connection structure – Feed forward connections (layered) – Recursive (Ouput neurons feed back to input layer) – Fully meshed Wilfried Elmenreich 13
  • Neural Networks are Evolvable 1.2 3.20.0 -1.2 3.2 2.2 3.2 3.2 1.2 -4.2 0.0 -0.1 -0.1 0.5 1.2 Recomb 0.2 3.2 3.2 ination 3.2 3.2 -1.2 3.5 3.2 -1.2 Mutation -4.2 -4.2 3.2 -0.1 0.0 -0.1 0.0 3.2 -1.2 0.2 0.2 3.2 -4.2 0.0 -0.1 0.2 Wilfried Elmenreich 14
  • A Framework for Evolutionary Design• FREVO (Framework for Evolutionary Design)• Modular Java tool allowing fast simulation and evolution• „Frevo“ means also a hot, „boiling“ dance around here Wilfried Elmenreich 15
  • Framework for Evolutionary Design• FREVO defines flexible components for – Controller representation – Problem specification – Optimizer Wilfried Elmenreich 16
  • Modeling the Coverage Problem in FREVO• Basically, we need a simulation of the problem• Interface for input/output connections to the agents – Inputs for detecting obstacles – Inputs for detecting other drones – Navigation output• Feedback from a simulation run -> fitness value – spatial coverage (number of cells visited at least once divided by the total number of unobstructed cells) – completion time (number of simulation steps needed Wilfried Elmenreich 17
  • Results Wilfried ElmenreichPhoto: wikipedia.org 18
  • Algorithms under Test• Non-cooperative evolved algorithms – UAVs are not aware of other UAVs – Basically evolving a „better“ random walk• Cooperative evolved algorithm – Extra input to recognize meeting other UAVs• Belief-based algorithm – Handcrafted solution based on random direction – Avoids cluttering of UAVs at border/meeting situations• Reference Algorithms – Random walk – Random direction Wilfried Elmenreich 19
  • Coverage Snapshot for 10 UAVs Wilfried Elmenreich 20 20
  • Performance Comparison Wilfried Elmenreich 21
  • Conclusion and Outlook Wilfried ElmenreichPhoto: wikipedia.org 22
  • Conclusion and Outlook• Three promising algorithms• Cooperation feature upon meeting of two UAVs does not significantly improve results – for realistic density of drones• Implementation in real system feasible – small computational effort• Future work – Compare with a priori planning algorithms – Challenges: dynamic environment, continuous space model Wilfried Elmenreich 23
  • Visit us!• (open source): www.frevotool.tk• Project MESON (Design Methods for Self- Organizing Systems): meson.lakeside-labs.com• Project cDrones: www.cdrones.com• Lakeside Labs Cluster www.lakeside-labs.com Wilfried Elmenreich 24
  • Thank you very much for your attention! A short summary of the talk and the slides will be available at http://demesos.blogspot.com Wilfried Elmenreich 25