1. Behavior Recognition of Autonomous Underwater Vehicles For CS 7631: Multi Robot Systems Michael “Misha” Novitzky School of Interactive Computing Georgia Tech

2. Motivation

3. Yellowfin

4. Yellowfin Untethered

5. The Papers  Behaviour recognition for spatially unconstrained unmanned vehicles, R. Baxter, D. Lane, and Y. Petillot, IJCAI 2009  Conditional Random Fields for Behavior Recognition of Autonomous Underwater Vehicles, M. Novitzky, C. Pippin, T. Collins, T. Balch, and E. West, under review at IROS, 2012.

6. Baxter et al. : Problem Description  How do we determine the behavior of an Unmanned Underwater Vehicle with only GPS coordinates?

7. Baxter et al.: Insights  Break from location dependent GPS coordinates and use compass heading  Behaviors encoded as: W – W – NW – N – NE -E

8. Baxter et al.: Approach  Took self-localization data from post UAV missions  Converted GPS coordinates to compass heading

9. Baxter et al.: Approach X1 y1 X2 y2 y3 Hidden Markov Models (HMMs) Given: Example Sequence Learn: Transition Probabilities Emission Probabilities

10. Baxter et al.: Approach  Run 8 HMMs (one for each behavior)  HMM with highest negative log-likelihood for K consecutive time slices = WINNER!

11. Baxter et al.: Implementation  All trajectories were created via simulation  Obviously, not using a real robot implies that this may not work in situ

12. Baxter et al.: Experiments  Simulated UAV trajectories  Added noise on encoding such as: N -> NW

13. Baxter et al.: Results  Precision

14. Baxter et al.: Results  Confusion under 70% noise

15. Baxter et al.: Critique  Authors demonstrated a system for behavior classification  Variable length testing  Implementation – restricted vehicles to compass directions – which is not really location agnostic  Only in simulation – will this work on real UUV?

16. Novitzky et al.: Problem Description  Can we recognize the behaviors of UUVs using two different approaches?  Which is best? When?

17. Novitzky et al.: Insights  Using environmentally agnostic encoding method  Use real sonar data  CRFs vs HMMs more accurate?

18. Novitzky et al.: Approach  Environment agnostic discretization:

19. Novitzky et al.: Approach  HMMs: one HMM per behavior  The largest negative log-likelihood is the WINNER! X1 y1 X2 y2 y3

20. Novitzky et al.: Approach X1 X2 Y Y Conditional Random Fields (CRFs) Given: Example Sequences Learn: Potential Functions One CRF: Each X is a label Y’s include all instances

21. Novitzky et al.: Implementation  Simulation  Real sonar data  YellowRay ROV  All analyzed using MATLAB

22. Novitzky et al: Experiments  Stationary Observer:  Simulation 600 Train 400 Test  Real Sonar Data

23. Novitzky et al: Experiments Simulated: Track & Trail

24. Novitzky: Results

25. Novitzky et al.: Critique  Not variable length testing  Not enough real sonar data  Simulated noise accurate? Guassian?  Actually use real vehicles and data!

26. Comparison of the Two  Encoding methods  Baxter et al. variable length testing  Baxter et al. has more behaviors  Novitzky et al. has real sonar data  if have ample training data use CRFs else use HMMs

27. Questions? Paul Robinette Andrew Melim