Megan DiVallECE 539Dec. 14, 2010

NEURAL NETWORK LEARNING OF ROBOT

NAVIGATION TASKS

Use neural network classification tools studied in the class on a real set of data

Compare performance of tools to each other

The data:UC-Irvine Machine Learning Repository – “Wall Following Robot Navigation Data Set”

Donated by researchers at Federal University of Ceará, Brazil

THE “WHAT” OF THE PROJECT

Opportunity to apply lessons from class to a “real-life” problem in field of interest

Compare performance of different tools using the same set of realistic data

Compare performance of tools from class to those used in associated studyPerceptron performed poorly without short-term memory mechanisms, problem is not linearly separable

THE “WHY” OF THE PROJECT

1. Research/choose tools to use2. Format data to be usable by each tool;

create training/testing groups3. (If needed) Modify tool’s

programming/settings to produce good results

4. Perform tests noting classification rate, ease of use, speed of calculation, etc.

EXPERIMENTAL PROCEDURE

Perceptron Just plain perceptron; won’t work well if problem is

not linearly separable

Multilayer Perceptron Used in original study

K-nearest neighbor classifier Not used in original study

Maximum likelihood classifier using uni-variate Gaussian model Not used in original study

CHOSEN TOOLS

Perceptron will not do well; original study found problem to not be linearly separable

Other tools may or may not do well but probably better than the perceptronMultilayer perceptron did well in original study

One or two tools will prove superior both in classification rate and calculation ease/speed

EXPECTED RESULTS

What tool would I be most likely to use if I was programming a real robot?

Would the performance of the “best” tool be good enough for real applications?

Could anything be done to improve performance of the “best” tool?

How do my results compare to expected real-world robot navigation performance?

DISCUSSION