IROS 2012 talk

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A. Marino and G. Antonelli and A.P. Aguiar and A. Pascoal, Multi-robot harbor patrolling: a probabilistic approach, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, Algarve, PT, pp. , 2012.

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IROS 2012 talk

  1. 1. A new approach to multi-robot harbour patrolling: theory and experiments Alessandro Marino, Gianluca Antonelli, A. Pedro Aguiar, Ant´nio Pascoal o Universit` di Salerno, Italy a Universit` di Cassino e del Lazio Meridionale, Italy a Universidade do Porto, Portugal Instituto Superiore T´cnico, Portugal e Marino, Antonelli, Aguiar, Pascoal Vila Moura, 8 October 2012
  2. 2. Problem formulation Multi-robot harbor patrolling Mathematically strong overlap with (time varying) coverage deployment resource allocation sampling exploration monitoringslight differences depending on assumptions and objective functions Marino, Antonelli, Aguiar, Pascoal Vila Moura, 8 October 2012
  3. 3. The rules of the game Totally decentralized Robust to a wide range of failures communications vehicle loss vehicle still Flexible/scalable to the number of vehicles add vehicles anytime Possibility to tailor wrt communication capabilities Not optimal but benchmarking required Anonymity To be implemented on a real set-up obstacles. . . Marino, Antonelli, Aguiar, Pascoal Vila Moura, 8 October 2012
  4. 4. Proposed solutionProper merge of the Voronoi and Gaussian processes concepts Communication required only to exchange key data Motion computed to increase information Map-based Framework to handle Spatial variability regions with different interest Time-dependency forgetting factor Asynchronous spot visiting demand Marino, Antonelli, Aguiar, Pascoal Vila Moura, 8 October 2012
  5. 5. 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 set union of the cells gives back the set the intersection of the cells is null computation of the cells is a decentralized algorithm without communication needed Marino, Antonelli, Aguiar, Pascoal Vila Moura, 8 October 2012
  6. 6. Voronoi partitions IISpontaneous distribution of restaurants Marino, Antonelli, Aguiar, Pascoal Vila Moura, 8 October 2012
  7. 7. Voronoi partitions IIIVoronoi in nature Marino, Antonelli, Aguiar, Pascoal Vila Moura, 8 October 2012
  8. 8. Voronoi partitions IVVoronoi in art: Escher Marino, Antonelli, Aguiar, Pascoal Vila Moura, 8 October 2012
  9. 9. Background I how much do I trust that Variable of interest is a Gaussian process a given point is safe? Given the points of measurements done. . . Sa = (xa , ta ), (xa , ta ), . . . , (xaa , taa ) 1 1 2 2 n n and one to do. . . Sp = (xp , t) Synthetic Gaussian representation of the condition distribution µ = µ(xp , t) + c(xp , t)T Σ Sa (y a − µa ) ˆ −1 σ = K(f (xp , t), f (xp , t)) − c(xp , t)T Σ −1 c(xp , t) ˆ Sa c represents the covariances of the acquired points vis new one Marino, Antonelli, Aguiar, Pascoal Vila Moura, 8 October 2012
  10. 10. Description I The variable to be sampled is a confidence map Reducing the uncertainty means increasing the highlighted term   µ = µ(xp , t) + c(xp , t)T Σ Sa (y a − µa )  ˆ −1 σ = K(f (xp , t), f (xp , t)) − c(xp , t)T Σ −1 c(xp , t) ˆ Sa   ξ − > ξ example Marino, Antonelli, Aguiar, Pascoal Vila Moura, 8 October 2012
  11. 11. Description II Distribute the computation among the vehicles each vehicle in its own Voronoi cell Compute the optimal motion to reduce uncertainty Several choices possible: minimum, minimum over an integrated path, etc. Marino, Antonelli, Aguiar, Pascoal Vila Moura, 8 October 2012
  12. 12. Accuracy: exampleGlobal computation of ξ(x) for a given spatial variability τs τs ξ(x) x1 x2 x3 x4 x Marino, Antonelli, Aguiar, Pascoal Vila Moura, 8 October 2012
  13. 13. Accuracy: exampleComputation made by x2 (it does not “see” x4 ) τs ξ(x) x1 x2 x3 x4 x Marino, Antonelli, Aguiar, Pascoal Vila Moura, 8 October 2012
  14. 14. Accuracy: exampleOnly the restriction to V or2 is needed for its movement computation τs ξ(x) V or2 x1 x2 x3 x4 x Marino, Antonelli, Aguiar, Pascoal Vila Moura, 8 October 2012
  15. 15. Accuracy: exampleMerging of all the local restrictions leads to a reasonable approximation τs ξ(x) V or2 x1 x2 x3 x4 x Marino, Antonelli, Aguiar, Pascoal Vila Moura, 8 October 2012
  16. 16. AccuracyBased on: communication bit-rate computational capability area dimension Marino, Antonelli, Aguiar, Pascoal Vila Moura, 8 October 2012
  17. 17. Numerical validationDozens of numerical simulations by changing the key parameters: vehicles number faults obstacles 2 sensor noise area shape/dimension 3 4 comm. bit-rate space scale time scale Marino, Antonelli, Aguiar, Pascoal Vila Moura, 8 October 2012
  18. 18. Some benchmarkingWith a static field the coverage index always tends to one Coverage Index 1 0.8 0.6 [] 0.4 0.2 0 200 400 600 800 1000 step Marino, Antonelli, Aguiar, Pascoal Vila Moura, 8 October 2012
  19. 19. Some benchmarking Comparison between different approaches 2 same parameters1.5 lawnmower rigid wrt vehicle loss[] 1 deployment suffers Lawnmower from theoretical Proposed Random flaws0.5 Deployment 00 200 400 600 800 1000 1200 step Marino, Antonelli, Aguiar, Pascoal Vila Moura, 8 October 2012
  20. 20. Two marine patrolling experiments 3 ASVs july 2011 2 AUVs february 2012 Instituto Superior T´cnico e with GraalTech at NURC 100 × 100 m 150 × 150 × 5 m 1 m/s 1.5 m/s GPS localiz. localiz. asynch 5 time/min WiFi comm. comm. 32 byte/min duration as long as batteries on 33 minutes results under evaluation Marino, Antonelli, Aguiar, Pascoal Vila Moura, 8 October 2012
  21. 21. CO3AUVsCooperative Cognitive Control of Autonomous Underwater Vehicles fundings : FP7 - Cooperation - ICT - Challenge 2 Cognitive Systems, Interaction, Robotics kind : Collaborative Project (STREP) acronym : CO3 AUVs duration : Feb 2009-Gen 2012 http://www.Co3-AUVs.eu Marino, Antonelli, Aguiar, Pascoal Vila Moura, 8 October 2012

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