Behavior Recognition of Autonomous Underwater Vehicles
For CS 7631: Multi Robot Systems
Michael “Misha” NovitzkySchool of Interactive ComputingGeorgia Tech
Motivation
Yellowfin
Yellowfin Untethered
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.
Baxter et al. : Problem Description How do we determine the behavior of an
Unmanned Underwater Vehicle with only GPS coordinates?
Baxter et al.: Insights Break from location dependent GPS
coordinates and use compass heading
Behaviors encoded as: W – W – NW – N – NE -E
Baxter et al.: Approach
Took self-localization data from post UAV missions
Converted GPS coordinates to compass heading
Baxter et al.: Approach
X1
y1
X2
y2 y3
Hidden Markov Models (HMMs)
Given:Example Sequence
Learn:Transition ProbabilitiesEmission Probabilities
Baxter et al.: Approach
Run 8 HMMs (one for each behavior)
HMM with highest negative log-likelihood for K consecutive time slices = WINNER!
Baxter et al.: Implementation
All trajectories were created via simulation
Obviously, not using a real robot implies that this may not work in situ
Baxter et al.: Experiments
Simulated UAV trajectories Added noise on encoding such as:
N -> NW
Baxter et al.: Results
Precision
Baxter et al.: Results
Confusion under 70% noise
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?
Novitzky et al.: Problem Description Can we recognize the behaviors of UUVs using
two different approaches? Which is best? When?
Novitzky et al.: Insights Using environmentally agnostic encoding
method Use real sonar data CRFs vs HMMs more accurate?
Novitzky et al.: Approach
Environment agnostic discretization:
Novitzky et al.: Approach
HMMs: one HMM per behavior The largest negative log-likelihood is the
WINNER!
X1
y1
X2
y2 y3
Novitzky et al.: Approach
X1 X2
Y
Conditional Random Fields (CRFs)
Given:Example Sequences
Learn:Potential Functions
One CRF:Each X is a labelY’s include all instances
Novitzky et al.: Implementation
Simulation Real sonar data YellowRay ROV All analyzed using MATLAB
Novitzky et al: Experiments Stationary Observer:
Simulation 600 Train 400 Test Real Sonar Data
Novitzky et al: Experiments
Simulated:Track & Trail
Novitzky: Results
Novitzky et al.: Critique Not variable length testing Not enough real sonar data Simulated noise accurate? Guassian? Actually use real vehicles and data!
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
Questions?
Paul Robinette
Andrew Melim