Coordinated control of autonomous marine vehicles for security applications

Transcription

1 Coordinated control of autonomous marine vehicles for security applications Gianluca Antonelli University of Cassino and Southern Lazio, Italy

2 ISME in brief Italian Joint Research Unit established in 1999 Sites: Ancona Cassino Genova Lecce Pisa Firenze Infrastructures from all departements > 30 researchers (not full time)

3 ISME research Main areas: Underwater robotics ROV AUV UW manipulation guidance navigation & control Underwater acoustics geoacoustics acoustics tomography imaging sonar Signal & data processing geographical information systems decision support systems classification & data fusion

4 ASVs interception of suspects vessels Security and surveillance of critical sites within harbors Teams of Autonomous Surface Vehicles (ASVs) for the optimal threat intercepts Threats individuated both by a distributed terrestrial radar system and actively by the ASVs Gianluca Antonelli Rovereto, 12 March 2014

5 ASVs interception of suspects vessels Offline Optimization of the ASVs Positioning based on two criteria: maximization of minimum interception distance minimization of maximum interception time Online Selection of the Best ASVs Selects the ASVs with lowest estimated time to the menace Takes into account the current traffic

6 Offline Optimization: interception distance Maximization of the Minimum Interception Distance P a position of the asset ; P m position of the menace P vector of all ASVs positions P m,i position where the menace m is intercepted by the i-th ASVs For a specified menace m the best intercepting vehicle is given by i o ( = argmax P m,i P a ) i [ ( The worst case: d worst = min Pm maxi P m,i P a )] Optimization of ASV positioning: P o = argmax d worst = argmax P P { [ min P m max i ( P m,i P a )]}

7 Offline optimization: interception time Minimization of the Maximum Interception Time Any extra ASVs (if N > k) minimizing the interception time Let t m,i be the time required for the menace m to be intercepted by the i-th ASV For a specified menace m the best intercepting vehicle is given by The worst case: i o = argmin i t (m,i) [ ] t worst = max mint (m,i) P m i Optimal positioning: { [ ]} ˆP o = argmint worst = argmin max mint (m,i) ˆP ˆP P m i

8 Simulations in real scenarios Red dot: asset; Colored squared: ASV; Smaller circle: d th ; Bigger circle: r

9 Simulations in real scenarios Red dot: asset; Colored squared: ASV; Smaller circle: d th ; Bigger circle: r

10 Comparison: changing detection radius r (a) r = 400, d worst = 214, t worst = 22.93, (b) r = 750, d worst = 373, t worst = 22.62

11 Comparison: changing required min. int. distance d th (a) d th = 178, d worst = 374, t worst = 20.64, (b) d th = 400, d worst = 460, t worst = 20

12 Comparison: changing asset s position

13 Comparison: changing the number of ASV (a) 5 USVs (b) 4 USVs

14 Multi assets simulations (a) northern asset assumed as target (b) souther asset assumed as target Gianluca Antonelli Rovereto, 12 March 2014

15 Current efforts 1.3m long, 0.4cm wide brushless motor Development of 10 cheap USVs to test the aforementioned algorithms Rudder+propeller control Gyro, accelerometers and GPS for localization RF-Modem for communication with base station PC-104 and dspic as computational power The setup can be further used to test other high level algorithms: adaptive sampling, coordination algorithms, etc.

16 Multi-robot harbor patrolling Problem formulation 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...

17 Proposed solution Proper merge of the Voronoi and Gaussian processes concepts Motion computed to increase information Framework to handle Spatial variability regions with different interest Time-dependency forgetting factor Asynchronous spot visiting demand Mathematically strong overlap with (time varying) coverage, deployment, resource allocation, sampling, exploration, monitoring, etc. slight differences depending on assumptions and objective functions

18 Proposed solution Proper merge of the Voronoi and Gaussian processes concepts Motion computed to increase information Framework to handle Spatial variability regions with different interest Time-dependency forgetting factor Asynchronous spot visiting demand Mathematically strong overlap with (time varying) coverage, deployment, resource allocation, sampling, exploration, monitoring, etc. slight differences depending on assumptions and objective functions

19 Voronoi partitions Voronoi partitions (tessellations/diagrams) Subdivisions of a set S characterized by a metric with respect to a finite 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

20 Voronoi partitions

21 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 = { (x a 1,t a 1),(x a 2,t a 2),...,(x a n a,t a n a ) } and one to do... Sp = (x p,t) Synthetic Gaussian representation of the condition distribution { ˆµ = µ(x p,t)+c(xp,t) T Σ 1 Sa (y a µa) ˆσ = K(f(xp,t),f(xp,t)) c(xp,t) T Σ 1 Sa c(x p,t) c represents the covariances of the acquired points vis new one

22 Description The variable to be sampled is a confidence map Reducing the uncertainty means increasing the highlighted term ˆµ = µ(xp,t)+c(xp,t) T Σ 1 Sa (y a µa) ˆσ = K(f(xp,t),f(xp,t)) c(xp,t) T Σ 1 Sa c(x p,t) } {{ } ξ > ξ example

23 Description 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.

24 Accuracy: example Global computation of ξ(x) for a given spatial variability τ s τ s ξ(x) x 1 x 2 x 3 x 4 x

25 Accuracy: example Computation made by x 2 (it does not see x 4 ) τ s ξ(x) x 1 x 2 x 3 x 4 x

26 Accuracy: example Only the restriction to Vor 2 is needed for its movement computation τ s ξ(x) Vor 2 x 1 x 2 x 3 x 4 x

27 Accuracy: example Merging of all the local restrictions leads to a reasonable approximation τ s ξ(x) Vor 2 x 1 x 2 x 3 x 4 x

28 Accuracy Based on: communication bit-rate computational capability area dimension

29 Numerical validation Dozens of numerical simulations by changing the key parameters: vehicles number faults obstacles sensor noise area shape/dimension comm. bit-rate space scale time scale 3 4 2

30 Some benchmarking With a static field the coverage index always tends to one Coverage Index [ ] step

31 Some benchmarking Comparison between different approaches [ ] Lawnmower Proposed Random Deployment same parameters lawnmower rigid wrt vehicle loss deployment suffers from theoretical flaws step

32 Experimental validation with ASVs Laboratory of Robotics and Systems in Engineering and Science IST, Technical University of Lisbon

33 Experimental validation with ASVs 3 Medusas switched off only for low battery obstacle Laboratory of Robotics and Systems in Engineering and Science IST, Technical University of Lisbon

34 Experimental validation with AUVs Vehicle characteristics internal diameter.125 m external diameter.14 m length 2m mass 30 kg mass variation range.5 kg (at water density kg/m 3 ) moving mass max displacement m Lead acid batteries 12 V 72 Ah autonomy at full propulsion 8 h diving scope 0 50 m break point in depth 100 m speed with jet pump propeller 1.01 m/s 2 knots speed with blade propeller 2.02 m/s 4 knots cpu 1GHz, VIA EDEN dram 1GB, DDR2

35 Experimental validation with AUVs joint experiment with Graaltech NURC (NATO Undersea Research Center) facilities, La Spezia, Italy

36 Experimental validation with AUVs 2 Fòlaga, 4 acoustic transponders, 1 gateway buoy m 1.5 m/s 33 minutes WHOI micromodem 80 bps Time Division Multiple Access localization: every 8 s user comm: 31 byte/min with 14 s delay

37 Experimental validation with AUVs Due to poor communication, the algorithm runs by predicting the movement of the other # fields size (bytes) 1) vehicle ID 2 2) localization time 4 3) vehicle latitude 4 4) vehicle longitude 4 5) vehicle depth 4 6) target latitude 4 7) target longitude 4 8) target depth 4

38 [] Experimental validation with AUVs - video 0.5 Coverage index time [s] 1800

39 Conclusions we missed the sole intruder!

40 Acknowledgements in rigorous casual order Filippo Arrichiello Alessandro Marino Pino Casalino Alessio Turetta Sandro Torelli Enrico Simetti Stefano Chiaverini Alessandro Sperindé

41 Coordinated control of autonomous marine vehicles for security applications Gianluca Antonelli University of Cassino and Southern Lazio, Italy