Behavior Recognition of Autonomous Underwater Vehicles For CS 7631: Multi Robot Systems

Behavior Recognition of Autonomous Underwater Vehicles For CS 7631: Multi Robot Systems Michael “Misha” Novitzky School of Interactive Computing Georgia 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 Hidden Markov Models (HMMs) Given: Example Sequence Learn: Transition Probabilities Emission Probabilities X1 X2 y1 y3 y2

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 • CRFsvsHMMs 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 X2 y1 y3 y2

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

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? Andrew Melim Paul Robinette

Behavior Recognition of Autonomous Underwater Vehicles For CS 7631: Multi Robot Systems