Experimental Results of Coordinated Coverage byAutonomous Underwater VehiclesAlessandro Marino, Gianluca AntonelliUniversi...
CO3AUVsCooperative Cognitive Control of Autonomous Underwater Vehiclesfundings : European FP7, Cognitive Systems, Interact...
Problem formulationMulti-robot harbor patrollingTotally decentralizedRobust to a wide range of failurescommunicationsvehic...
Proposed solutionProper merge of the Voronoi and Gaussian processes conceptsMotion computed to increase informationFramewo...
Proposed solutionProper merge of the Voronoi and Gaussian processes conceptsMotion computed to increase informationFramewo...
Backgroundtheoretical detailsAntonelli, Chiaverini, Marino, A coordination strategy for multi-robotsampling of dynamic fiel...
Voronoi partitions IVoronoi partitions (tessellations/diagrams)Subdivisions of a set S characterized by a metric with resp...
Voronoi partitions IIMarino, Antonelli Karlsruhe, 9 May 2013
Background IVariable of interest is a Gaussian processhow much do I trust thata given point is safe?Given the points of me...
Description IThe variable to be sampled is a confidence mapReducing the uncertainty means increasing the highlighted term...
Description IIDistribute the computation among the vehicleseach vehicle in its own Voronoi cellCompute the optimal motion ...
Accuracy: exampleGlobal computation of ξ(x) for a given spatial variability τsτsx1 x2 x3 x4xξ(x)Marino, Antonelli Karlsruh...
Accuracy: exampleComputation made by x2 (it does not “see” x4)τsx1 x2 x3 x4xξ(x)Marino, Antonelli Karlsruhe, 9 May 2013
Accuracy: exampleOnly the restriction to V or2 is needed for its movement computationτsx1 x2 x3 x4xξ(x)V or2Marino, Antone...
Accuracy: exampleMerging of all the local restrictions leads to a reasonable approximationτsx1 x2 x3 x4xξ(x)V or2Marino, A...
AccuracyBased on:communication bit-ratecomputational capabilityarea dimensionMarino, Antonelli Karlsruhe, 9 May 2013
Numerical validationDozens of numerical simulations by changing the key parameters:vehicles numberfaultsobstaclessensor no...
Some benchmarkingWith a static field the coverage index always tends to one0 200 400 600 800 10000.20.40.60.81step[]Coverag...
Some benchmarkingComparison between different approaches00LawnmowerProposedRandomDeployment0.51.52200 400 600 800 1000 1200...
Vehicle characteristicsinternal diameter .125 mexternal diameter .14 mlength 2 mmass 30 kgmass variation range .5 kg(at wa...
Experimental validationjoint experiment with Graaltech NURC (NATO Undersea ResearchCenter) facilities, La Spezia, ItalyMar...
Experimental validation2 F`olaga, 4 acoustic transponders, 1 gateway buoy110 × 80 × 4 m1.5 m/s33 minutesWHOI micromodem 80...
Experimental validationDue to poor communication, the algorithm runs by predicting themovement of the other# fields size (b...
Experimental validation - videoCoverage index200 400 600 800 1000 1200 1400 16000.10.20.30.4[]0.500time [s] 1800Marino, An...
Conclusionswe missed the sole intruder!Marino, Antonelli Karlsruhe, 9 May 2013
Experimental Results of Coordinated Coverage byAutonomous Underwater VehiclesAlessandro Marino, Gianluca AntonelliUniversi...
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ICRA 2013 talk 2

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Coverage of a given area by means of coordinated autonomous robots is a mission
required in several applications such as, for example, patrolling, monitoring or
environmental sampling. From a mathematical perspective, this can often be
modeled as the need to estimate a scalar field, eventually time varying as in
the security applications. In this paper, the problem is addressed for the
challenging underwater scenario, where localization and communication pose
additional constraints. The solution exploits the appealing properties of the
Voronoi partition of a convex set within a probabilistic framework. In addition,
the algorithm is totally distributed and characterized by a strong engineering
perspective allowing the handling of asynchronous communication or possible loss
or adjunct of vehicles. Beyond the test in dozen of numerical case studies, the
algorithm has been validated by a challenging underwater test in 3 dimension
involving two Autonomous Underwater Vehicles (AUVs). The experiments were run in
the La Spezia harbor, in Italy, in February 2012 as demo
of the European project \co3auvs.

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ICRA 2013 talk 2

  1. 1. Experimental Results of Coordinated Coverage byAutonomous Underwater VehiclesAlessandro Marino, Gianluca AntonelliUniversit`a di Salerno, ItalyUniversit`a di Cassino & ISME (Integrated Systems for Marine Environment), Italyantonelli@unicas.ithttp://webuser.unicas.it/lai/roboticahttp://www.isme.unige.it/Marino, Antonelli Karlsruhe, 9 May 2013
  2. 2. CO3AUVsCooperative Cognitive Control of Autonomous Underwater Vehiclesfundings : European FP7, Cognitive Systems, Interaction, Roboticskind : Collaborative Project (STREP)duration : 3 years, 2009-2012partners : Jacobs University, DE;ISME, I;Instituto Superior T´ecnico, P;GraalTech, Ihttp://www.Co3-AUVs.euMarino, Antonelli Karlsruhe, 9 May 2013
  3. 3. Problem formulationMulti-robot harbor patrollingTotally decentralizedRobust to a wide range of failurescommunicationsvehicle lossvehicle stillFlexible/scalable to the number of vehicles add vehicles anytimePossibility to tailor wrt communication capabilitiesNot optimal but benchmarking requiredAnonymityTo be implemented on a real set-up obstacles. . .Marino, Antonelli Karlsruhe, 9 May 2013
  4. 4. Proposed solutionProper merge of the Voronoi and Gaussian processes conceptsMotion computed to increase informationFramework to handleSpatial variability regions with different interestTime-dependency forgetting factorAsynchronous spot visiting demandMathematically strong overlap with (time varying) coverage,deployment, resource allocation, sampling, exploration, monitoring, etc.slight differences depending on assumptions and objective functionsMarino, Antonelli Karlsruhe, 9 May 2013
  5. 5. Proposed solutionProper merge of the Voronoi and Gaussian processes conceptsMotion computed to increase informationFramework to handleSpatial variability regions with different interestTime-dependency forgetting factorAsynchronous spot visiting demandMathematically strong overlap with (time varying) coverage,deployment, resource allocation, sampling, exploration, monitoring, etc.slight differences depending on assumptions and objective functionsMarino, Antonelli Karlsruhe, 9 May 2013
  6. 6. Backgroundtheoretical detailsAntonelli, Chiaverini, Marino, A coordination strategy for multi-robotsampling of dynamic fields, ICRA 2012experimental validation with surface vehiclesMarino, Antonelli, Aguiar, Pascoal, Multi-robot harbor patrolling: aprobabilistic approach, IROS 2012Marino, Antonelli Karlsruhe, 9 May 2013
  7. 7. Voronoi partitions IVoronoi partitions (tessellations/diagrams)Subdivisions of a set S characterized by a metric with respect to afinite number of points belonging to the setunion of the cells gives back the setthe intersection of the cells is nullcomputation of the cells is adecentralized algorithm withoutcommunication neededMarino, Antonelli Karlsruhe, 9 May 2013
  8. 8. Voronoi partitions IIMarino, Antonelli Karlsruhe, 9 May 2013
  9. 9. Background IVariable of interest is a Gaussian processhow much do I trust thata given point is safe?Given the points of measurements done. . .Sa = (xa1 , ta1 ), (xa2 , ta2 ), . . . , (xana, tana)and one to do. . .Sp = (xp, t)Synthetic Gaussian representation of the condition distributionˆµ = µ(xp, t) + c(xp, t)TΣ−1Sa(ya − µa)ˆσ = K(f(xp, t), f(xp, t)) − c(xp, t)TΣ−1Sac(xp, t)c represents the covariances of the acquired points vis new oneMarino, Antonelli Karlsruhe, 9 May 2013
  10. 10. Description IThe variable to be sampled is a confidence mapReducing the uncertainty means increasing the highlighted termˆµ = µ(xp, t) + c(xp, t)TΣ−1Sa(ya − µa)ˆσ = K(f(xp, t), f(xp, t)) − c(xp, t)TΣ−1Sac(xp, t)ξ− > ξ exampleMarino, Antonelli Karlsruhe, 9 May 2013
  11. 11. Description IIDistribute the computation among the vehicleseach vehicle in its own Voronoi cellCompute the optimal motion to reduce uncertaintySeveral choices possible:minimum, minimum over anintegrated path, etc.Marino, Antonelli Karlsruhe, 9 May 2013
  12. 12. Accuracy: exampleGlobal computation of ξ(x) for a given spatial variability τsτsx1 x2 x3 x4xξ(x)Marino, Antonelli Karlsruhe, 9 May 2013
  13. 13. Accuracy: exampleComputation made by x2 (it does not “see” x4)τsx1 x2 x3 x4xξ(x)Marino, Antonelli Karlsruhe, 9 May 2013
  14. 14. Accuracy: exampleOnly the restriction to V or2 is needed for its movement computationτsx1 x2 x3 x4xξ(x)V or2Marino, Antonelli Karlsruhe, 9 May 2013
  15. 15. Accuracy: exampleMerging of all the local restrictions leads to a reasonable approximationτsx1 x2 x3 x4xξ(x)V or2Marino, Antonelli Karlsruhe, 9 May 2013
  16. 16. AccuracyBased on:communication bit-ratecomputational capabilityarea dimensionMarino, Antonelli Karlsruhe, 9 May 2013
  17. 17. Numerical validationDozens of numerical simulations by changing the key parameters:vehicles numberfaultsobstaclessensor noisearea shape/dimensioncomm. bit-ratespace scaletime scale23 4Marino, Antonelli Karlsruhe, 9 May 2013
  18. 18. Some benchmarkingWith a static field the coverage index always tends to one0 200 400 600 800 10000.20.40.60.81step[]Coverage IndexMarino, Antonelli Karlsruhe, 9 May 2013
  19. 19. Some benchmarkingComparison between different approaches00LawnmowerProposedRandomDeployment0.51.52200 400 600 800 1000 12001[]stepsame parameterslawnmower rigid wrtvehicle lossdeployment suffersfrom theoreticalflawsMarino, Antonelli Karlsruhe, 9 May 2013
  20. 20. Vehicle characteristicsinternal diameter .125 mexternal diameter .14 mlength 2 mmass 30 kgmass variation range .5 kg(at water density 1.031 kg/m3)moving mass max displacement 0.050 mLead acid batteries 12 V 72 Ahautonomy at full propulsion 8 hdiving scope 0–50 mbreak point in depth 100 mspeed with jet pump propeller 1.01 m/s 2 knotsspeed with blade propeller 2.02 m/s 4 knotscpu 1GHz, VIA EDENdram 1GB, DDR2Marino, Antonelli Karlsruhe, 9 May 2013
  21. 21. Experimental validationjoint experiment with Graaltech NURC (NATO Undersea ResearchCenter) facilities, La Spezia, ItalyMarino, Antonelli Karlsruhe, 9 May 2013
  22. 22. Experimental validation2 F`olaga, 4 acoustic transponders, 1 gateway buoy110 × 80 × 4 m1.5 m/s33 minutesWHOI micromodem 80 bpsTime Division Multiple Accesslocalization: every 8 suser comm: 31 byte/min with 14 s delayMarino, Antonelli Karlsruhe, 9 May 2013
  23. 23. Experimental validationDue to poor communication, the algorithm runs by predicting themovement of the other# fields size (bytes)1) vehicle ID 22) localization time 43) vehicle latitude 44) vehicle longitude 45) vehicle depth 46) target latitude 47) target longitude 48) target depth 4Marino, Antonelli Karlsruhe, 9 May 2013
  24. 24. Experimental validation - videoCoverage index200 400 600 800 1000 1200 1400 16000.10.20.30.4[]0.500time [s] 1800Marino, Antonelli Karlsruhe, 9 May 2013
  25. 25. Conclusionswe missed the sole intruder!Marino, Antonelli Karlsruhe, 9 May 2013
  26. 26. Experimental Results of Coordinated Coverage byAutonomous Underwater VehiclesAlessandro Marino, Gianluca AntonelliUniversit`a di Salerno, ItalyUniversit`a di Cassino & ISME (Integrated Systems for Marine Environment), Italyantonelli@unicas.ithttp://webuser.unicas.it/lai/roboticahttp://www.isme.unige.it/Marino, Antonelli Karlsruhe, 9 May 2013
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