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