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Automated Modeling of the Guidance of a Trained Canine

Winard “Win” Britt

Committee Chair: John A. Hamilton, Jr., Department of Computer Science

Committee: David M. Bevly, Department of Mechanical Engineering

Saad Biaz, Department of Computer Science

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The K-9 Team David M. Bevly, Associate Professor ME Winard Britt, PhD Candidate CS Jeffrey Miller, PhD Candidate ME Stephan Henning, UG EE Conrad Bass, UG ME The Canine Detection Research Institute

(part of the AU Vet School) Staff

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Acknowledgement This project is financially supported by

the Office of Naval Research YIP award N00014-06-1-0518.

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Outline Motivation Goals Related Work Architecture Methodology Validation Concluding Remarks

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Motivation K-9s can detect narcotics and explosives

via scent to a high degree of accuracy. However, most K-9 teams require one or

more support staff per K-9 deployed in the field.

If K-9s could be made to be largely autonomous, they could be used without direct human supervision.

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Why not use robots? Comparatively, dogs have…

better obstacle avoidance greater mobility more sophisticated “sensors” like smell,

sight, etc.

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Problem StatementGiven a trained canine (K-9) which

responds to audio and vibration commands, can the K-9 be autonomously directed to given waypoints without human intervention?

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Goals A contribution to K-9 guidance in the form

of a “remote control" K-9 unit with an advanced sensor pack.

A contribution to K-9 guidance in the form of being able to classify K-9 behavior as “on course" or “off course.“

A contribution to K-9 guidance in the form of being able to model more sophisticated K-9 commands such as “left" and “right" and “stop" to guide the K-9 to a goal.

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Goals (2) General control algorithms for the

guidance of autonomous K-9 units. To produce the ability to determine if a K-

9 model is failing to predict commands due to the dog’s exhaustion or reaction to unobservable events.

To develop dynamic modeling algorithms which factor in changing K-9 behavior over time.

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Goals (3) A contribution to the area of machine

learning in the form of a comparison of methods for the K-9 control problem.

A contribution to the area of evolutionary computation in the form of a comparison between two evolutionary algorithms for ML optimization.

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Related Work K-9 units have been used (not autonomously)

as a means of detection of explosives and narcotics with tremendous success.

Machine Learning has been successfully applied to a wide variety of classification and modeling problems, including a number of efforts relating to vehicles and robots.

Evolutionary Algorithms have been successfully applied to the optimization of Machine Learners for a number of difficult problems.

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Unique Challenges Dogs do not exhibit deterministic behavior

(like vehicles and robots) The concept of a dog “behavior” is complex. Sensor data from a command pack is a

subset of the data a human trainer has when guiding a dog.

Building a new embedded system and interfacing it with sensors is time-consuming.

Resource constraints on the embedded system limit algorithm choices.

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System Architectures Three phases consist of:

Training Phase I: An initial effort performed by the GAVLAB prior to the beginning of this project. The goal was to interface some basic sensors and see how well they worked on a dog.

Training Phase II: A full sensor suite along with an interfaced command pack. The goal was to make the data collection considerably more precise.

Autonomous K-9 Phase: Once models have been developed, use them to guide the K-9 without human intervention.

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Sensor Pack

Binary Tone

Generator

Rabbit Processor

IMUGPS Receiver

Xbee Radio Modem

Data Sink

GPS Satellites

Handset for

Binary Tone

Generator

Operator: Records the tone changes manually.

Trainer: Issues the tone commands to guide the K-9 through his handset.

Radio: Transmits the parsed sensor information over the wireless link.

K-9: Responds to the tone to follow along the intended path.

Handset: transmits the current command wirelessly to the tone generator.

Rabbit: Collects and parses the sensor data from the various sensors, then sends to the Xbee modem.

GPS: Provides latitude, longitude, velocity, and heading.

IMU: Provides acceleration and rate of turn.

Phase I (Legacy Training)

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Results of Training Phase I Verified K-9 Training and Responses to tones. Verified that reasonable sensor data could be

obtained from GPS on-board the K-9.• Created a successful (85% accurate) model of K-9 behavior using General Regression Neural Networks and Evolutionary Computation [Britt, Bevly 2008] using only available sensor inputs.

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Sensor Pack

Command Module

Rabbit Processor

XSensGPS Receiver

Xbee Radio Modem

Data Sink

GPS Satellites

Handset for

Command Module Operator:

Starts/Stops recording data for various experimental trials.

Trainer: Issues the tone and vibration commands for “left”, “right”, and “stop” to guide the K-9 through his handset.

Radio: Transmits the parsed sensor information and the currently active commands over the wireless link.

K-9: Responds to the tone to follow along the intended path.

Handset: transmits the current command wirelessly to the tone generator.

Rabbit: Collects and parses the sensor data from the various sensors and command module, then sends to the Xbee modem.

GPS: Provides latitude, longitude, velocity, and heading.

XSens: Provides acceleration, rate of turn, and mag. data.

Phase II (Training - A “Remote Controlled” K-9)

Command Module: Issues tone commands to the K-9 and outputs those commands to the Rabbit.

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At the end of Training Phase II Sufficient data from a new, precise

command pack to effectively build models based on General Regression Neural Networks, Radial Basis Function Networks, and Support Vector Machines.

Sufficient data to perform sensor data aggregation (GAVLAB)

Demonstrate a remote-controlled K-9 unit.

18Sensor Pack

Command Module

Rabbit Processor

XSensGPS Receiver

Xbee Radio Modem

Data Sink

GPS Satellites

Operator: Inputs destination coordinates. Starts/Stops recording data.

Radio: Transmits the parsed sensor information and the currently active commands over the wireless link.

K-9: Responds to the tone to follow along the intended path.

Rabbit: Collects and parses the sensor data from the various sensors, filters that data, uses the model to issue a new command to the command module, then sends to the Xbee modem.

GPS: Provides latitude, longitude, velocity, and heading.

XSens: Provides acceleration, rate of turn, and mag. data.

Phase III (Autonomous K-9)

Command Module: Receives commands from the rabbit and issues them to the K-9.

Filter: Algorithm

to aggregate GPS/IMU data for

accuracy.

Model: Algorithm to

interpret filtered

sensor data and issue

new commands

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At the end of the Autonomous K-9 Phase

On-board software should be able to receive a destination GPS waypoint and the dog should be commanded successfully to that waypoint.

Many trials will be performed to validate the effectiveness of the approach in terms of ability to get the K-9 to the specified waypoints.

Different paths and path setups will be applied in order to validate the control algorithm.

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Current Status Trained K-9s to effectively respond to

tones. Working Phase II System (A “Remote

Control” K-9) in trials now. The ability to model K-9 behaviors as on

or off course [Britt, Bevly, Dozier 2008]. New modeling algorithms have been

developed and are awaiting sufficient Training Phase II data.

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Continuing Efforts Gather sufficient trial data to train further models of

K-9 guidance. Compare performance of various Machine Learners

and Optimization techniques. Build the Phase III system. Validate the Phase III system through field trials with

the K-9. Develop control algorithms to recognize model failure

in the cases where the dog becomes unresponsive. Develop dynamic algorithms to deal with and adjust

to minor K-9 fatigue.

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Conclusion

The Autonomous K-9 project stands to improve the utility and flexibility of using K-9s in law enforcement and military applications.

Automating a K-9 is a complex, cross-disciplinary task that requires a number of components.

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Conclusion (2) To the ends of producing an Autonomous

K-9 that can be autonomously directed to given waypoints without human intervention : It has been demonstrated that K-9 units can

be trained to effectively follow tones and vibrations.

Hardware has been developed and tested to issue commands and track the K-9’s movements.

Modeling algorithms have been developed which can accurately predict whether the K-9’s behavior is “on-course” or “off-course”

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Questions Questions? Comments? Nice comments are nice

too.

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Backup Slides

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Machine Learners in One Slide Given a numeric vector of input

“features”, predict one to many desired outputs.

Output must be correlated to the features!

Two phases: Development or “training” of a model from

existing data with known answers. Application of the model on new data

where the answers are unknown.

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Goals of Specific Machine Learners General Regression Neural Networks (GRNNs): Share

properties with the mature K-Nearest Neighbor algorithm, needs very little data to begin to give results.

Radial Basis Function Networks (RBFNs): By topology, better suited to describe one of a fixed number of commands. More computational overhead than GRNNs, but usually less storage overhead.

Support Vector Machines (SVMs): Heavyweight in training, but fairly compact models. Considered state of the art in many types of classification problems.

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Evolutionary Algorithms in One Slide

Given a function with inputs x1…xn and some numeric objective function which evaluates the quality of a candidate solution:

Randomly generate a population of candidate solutions by assigning values for x1…xn.

Use the objective function to evaluate their quality. Combine/mutate candidate solutions to create new

candidate solutions. Evaluate. Replace some fraction of existing solutions with

children according to some strategy. Iterate until time runs out or a solution with desire

fitness is found.

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Goals of Specific Evolutionary Algorithms

Evolutionary Hill-Climber: Single population EA in which mutation is the only way to create new candidate solutions. The current candidate solution is replaced by the child when the child is better.

Steady-State Genetic Algorithm: Multiple members of the population allow crossover and mutation to create new candidate solutions. The worst candidate solution is always replaced when replacements occur.