Evolving a Team of Self-organizingUAVs to Address Spatial CoverageProblemsIstván Fehérvári, Wilfried Elmenreich and Evsen ...
Overview• Unmanned areal vehicles• The coverage problem• Discrete simulation model• Evolutionary approach• Results• Conlus...
UAVs       Wilfried Elmenreich                   3
Battery-powered UAVs• Quadcopter platform with onboard sensors and  electronic for flight stabilization• Attached cameras ...
Small UAV Network for EmergencyAssistance UAV NetworkMission Control                                  Wilfried Elmenreich ...
Coverage/Detection Problem• Wireless sensor networks                 • Coverage problem in   – Civil                      ...
Planning and MonitoringFlight Paths• Example for flight paths of two UAVs (green/red  color)                              ...
Simplified Discrete Simulation Model• finite two-dimensional  lattice• each cell can contain at  most one agent or obstacl...
Keep it Simple!• No a priori knowledge about map size/position of  obstacles• No explicit communication among UAVs• No pos...
Wanted: the right UAV behavior model• Controls the UAV as autonomous agent• Processes inputs (from sensors) and produces  ...
EvolutionaryApproach   Wilfried Elmenreich                       11
Evolving the Control System• Simulation of target system as                           System model  testing playground    ...
Artificial Neural Networks• Each neuron sums up the  weighted outputs of the  other connected neurons• The output of the n...
Neural Networks are Evolvable        1.2                                                    3.20.0                 -1.2   ...
A Framework for Evolutionary Design• FREVO (Framework for Evolutionary Design)• Modular Java tool allowing fast simulation...
Framework for Evolutionary Design• FREVO defines flexible components for   – Controller representation   – Problem specifi...
Modeling the Coverage Problem in FREVO• Basically, we need a simulation of the problem• Interface for input/output connect...
Results                                 Wilfried ElmenreichPhoto: wikipedia.org                         18
Algorithms under Test• Non-cooperative evolved algorithms  – UAVs are not aware of other UAVs  – Basically evolving a „bet...
Coverage Snapshot for 10 UAVs                                Wilfried Elmenreich                                          ...
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 i...
Visit us!•                        (open source):    www.frevotool.tk• Project MESON (Design Methods for Self-  Organizing ...
Thank you very much for your attention! A short summary of the talk and the slides will be    available at http://demesos....
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Evolving a Team of Self-organizing UAVs to Address Spatial Coverage Problems

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  • Source: Photo by Wilfried Elmenreich
  • Evolving a Team of Self-organizing UAVs to Address Spatial Coverage Problems

    1. 1. 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
    2. 2. Overview• Unmanned areal vehicles• The coverage problem• Discrete simulation model• Evolutionary approach• Results• Conlusion and outlook Wilfried Elmenreich 2
    3. 3. UAVs Wilfried Elmenreich 3
    4. 4. 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
    5. 5. Small UAV Network for EmergencyAssistance UAV NetworkMission Control Wilfried Elmenreich 5 5
    6. 6. 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
    7. 7. Planning and MonitoringFlight Paths• Example for flight paths of two UAVs (green/red color) Wilfried Elmenreich 7
    8. 8. 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
    9. 9. 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
    10. 10. 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
    11. 11. EvolutionaryApproach Wilfried Elmenreich 11
    12. 12. 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
    13. 13. 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
    14. 14. 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
    15. 15. 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
    16. 16. Framework for Evolutionary Design• FREVO defines flexible components for – Controller representation – Problem specification – Optimizer Wilfried Elmenreich 16
    17. 17. 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
    18. 18. Results Wilfried ElmenreichPhoto: wikipedia.org 18
    19. 19. 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
    20. 20. Coverage Snapshot for 10 UAVs Wilfried Elmenreich 20 20
    21. 21. Performance Comparison Wilfried Elmenreich 21
    22. 22. Conclusion and Outlook Wilfried ElmenreichPhoto: wikipedia.org 22
    23. 23. 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
    24. 24. 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
    25. 25. 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

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