Audience Etiquette A Doctoral Defense is a formal presentation. If

you are in the general audience (not formally involved in the defense):1. Cut off all electronic devices that might make noise.2. Hold jokes, questions, comments, or interjections

during the presentation portion of the defense. There will be an open question period at the end of the defense. Interrupting will make the defense longer and harder to follow.

3. If you have to leave the room during the defense, don’t come back in. Take care of business beforehand.

The Autonomous Control and Navigation of a Trained Canine

Winard “Win” Britt, GAVLAB and IACCommittee Co-Chairs: Dr. John A. Hamilton, Jr., Department

of Computer ScienceDr. David M. Bevly, Department of

Mechanical EngineeringCommittee: Dr. Saad Biaz, Department of

Computer ScienceOutside Reader: Dr. Paul Waggoner, Canine Detection

Research Inst.

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Acknowledgements This project is financially supported by

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

My doctoral studies were partially supported by the Information Assurance Scholarship Program.

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Special Thanks The engineers of the GAVLAB The canine trainers and veterinary

professionals at the Canine Detection Research Institute

The past and present undergraduates of Team K9

The past and present canines of Team K9

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Outline Introduction & Key Contributions System Architecture

Canine Hardware & Sensors Software & Control Algorithm

Experiments, Results, and Discussion Concluding Remarks Questions

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Problem StatementCan a canine trained to respond to audio

and vibration commands be autonomously directed to given waypoints without human guidance?

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Motivation Canines have sophisticated built-in

sensors for the detection of narcotics and explosives with a high degree of accuracy and at better range than people.

Most canine teams require one or more support staff per canine deployed in leash-range.

If canines could be made to be largely autonomous, they could be used without direct human supervision keeping canines and people safer.

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Unique Challenges Dogs do not exhibit deterministic responses

(like vehicles and robots do) and are influenced by prior training and their environment in ways that machines are not.

Sensor data from a command pack is less comprehensive than human vision.

Hardware must be small, comfortable, and be able to withstand canine abuse.

Gathering canine field trial data is slow work.

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Key Contributions

Developed a unique state machine algorithm to model the human guidance of a trained canine.

Developed a system to autonomously and remotely command a trained canine using non-invasive actuation.

Demonstrated the autonomous control system in field trials with a canine in scenarios comparable to human-guided scenarios.

Demonstrated some scenarios in which autonomous control of a canine surpasses that of the human operator.

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Related Work Canines units have been used (not autonomously) as

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

Many sensor schemes to detect and analyze the pose and movements of animals.

Autonomous control of animals in a coarse-grained fashion has been performed on cows to prevent overgrazing.

My own past work included exploring the feasibility of the guidance of a canine using a General Regression Neural Network optimized with Evolutionary Computation.

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Rationale for Departure from Initial Neural Network Approach

Training data is noisy (human guidance, canine response) which leads to false positives.

Difficult to get trials with a large, robust set of specific anomalies.

It is difficult to directly tune a neural network in any intuitive way, even when expert knowledge is available.

Original problem was formulated as a binary control problem, later reformulated as a 5+ class control problem.

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System Architecture

The Trained Canine Remote Command and Navigation Autonomous Canine

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The Trained Canine Male Labrador Retriever, 4 years old, 32

kg Trained to perform “blind retrieves” Trained to perform explosive (C4)

detection, which takes precedence over other training.

Directional training came last.

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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”, “stop”, “recall”, and “forward” to guide the K-9 through his handset.

Radio: Transmits the parsed sensor information and the currently active commands over the wireless link at 38400 bps.

Canine Major: Responds to the tone to follow along the intended path.

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

Rabbit 4100: Collects and parses the sensor data from the various sensors and command module, then sends to the Xbee modem.

UBLOX GPS: Provides latitude, longitude, velocity, and course.

XSens: Provides filtered acceleration , roll, pitch, and yaw (heading).

A “Remote Controlled” Canine

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

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Remote Command and Navigation phase

Develop, test, and refine hardware and software

Demonstrate a remote-controlled canine unit.

Record and quantify human-directed canine trials.

Understand the limitations of the canine and to be able to estimate the success rate a human operator can garner in field trials with conditions favorable to human guidance.

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Sensor and Command Data Summary

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Human Guided Trial Setup Establish a series of actual and foil

waypoints. Measure location using GPS. Success is defined as the human

successfully remotely guiding the canine to each waypoint (within 7 m) in succession.

A “one point” failure is not arriving at even the first waypoint. A “multi-point” failure is arriving at some, but not all waypoints.

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Sample Trial

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Human Guided Trial Results

•Difference between “simple trial” success rate and “complex trials” not statistically significant (p = 0.34).•The 2-11-09 trial set is anomalous (p = 0.006). •Overall mission success rate is about 66%.

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Command Module

Rabbit Processor

GPS Receiver Xbee Radio

Modem

Data Sink

GPS Satellites

Operator: Inputs destination coordinates. Starts/Stops recording data. Operates control algorithm.

Radio: Transmits the parsed sensor information and the currently active commands over the wireless link to the laptop. Transmits back commands from controller on laptop.Canine Major:

Responds to the tone to follow along the intended path.

Rabbit: Collects and parses the sensor data from the various sensors, filters that data, reads new commands from the Xbee radio, issues those commands to the tone generator, then sends data back through the Xbee modem.

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

Autonomous Canine

Command Module: Receives commands from the rabbit and issues them to the canine.

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Autonomous Canine Phase Develop, test, and refine state machine

control algorithms. Perform trials to validate the feasibility of

the approach in terms of ability to get the canine to the goal waypoints. Different paths and environments will be

used in order to validate the control algorithm.

Compare autonomous guidance to human guidance.

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Goals of the Autonomous Control Algorithm

Should decouple canine performance from the skill of the canine operator

Should always give the correct commands to the canine in a timely manner.

Should not "overload" the canine with commands

Should have sensitivity to anomalous behavior, but enough leniency to account for normal variations in canine behavior.

Strategy: Use a state machine model.

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Advantages of a State Machine The idea of rules (represented as states

and transitions) is readily understandable, allowing expert insight to be readily captured.

Calculating system parameters from data analysis and field trials is relatively straightforward.

False positives (calling non-anomalous behavior anomalous) can be avoided by carefully defining anomaly detection rules.

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The Autonomous Control Algorithm A state-based algorithm

developed from analysis of the human guided trials.  "States" in this case are "behaviors".

Transitions between states are events: either changes observed in canine behavior or the completion of mission related tasks.

Events are observed by trends in GPS sensor data: position, course, velocity.

Changes in states are accompanied by a new command actuation (a new tone or vibration)

High Level Control Algorithm State Machine View

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Anomaly Detection Anomaly detections are caused either by

undetectable obstacles (rare), canine error(common), or sensor error (uncommon). 

Sustained increasing distance from the target and/or sustained deviation in course from the target will cause a stop and a new directional command to be given.

Large course deviations (going the wrong way completely) and deviations following turns (the canine did not take the turn command) will be corrected more quickly (sub 1 second) than other anomalies (1.5 s).

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Anomaly Correction Tricky business - the human trainer typically "recalls" after

anomalies. We will stop and issue a new correct command. Typically the canine makes mistakes (wrong turns) for a

reason - he wants to search something.   Risk could be mitigated with additional hardware.

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Control Algorithm Operation Cycle1. Read data packet from Xbee radio sent by Command

Pack2. Parse data into sensor measurements3. Use heuristic to determine if sensor data is valid4. Calculate derived metrics (based on the mission

information that the control algorithm has)5. Verify that reported command matches intended

command6. Check to see if an “event” has occurred: anomaly,

waypoint success, mission success.7. Change state if necessary.8. Calculate and issue command via Xbee radio.

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Observing EventsDerived Metric Units Based on… Used to…Distance to Target Meters Lat, Lon of canine

and destination waypoint

Determine waypoint success

Desired Course Degrees Lat, lon of canine and destination waypoint

Calculate deviation from desired course.

Deviation from Desired Course

Degrees Measured course, Desired Course to immediate goal

Calculate commands after an anomaly

Deviation from Desired Course 2

Degrees Measured course, Desired course to following goal

Calculate commands after a waypoint success.

Readings Since Improvement in Distance

Integer Distance to Target over time

Detect anomalies

Readings Off Course

Integer Deviation from Desired Course over time

Detect anomalies

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Sample AK9 Trial

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Autonomous Canine Trials Under “Fair” Comparison

•Difference between “simple trial” success rate and “complex trials” not statistically significant (p = 0.51).•Difference between human and autonomous canine guidance is not statistically significant (p = 1.0)

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Demo Videos “Fair” Scenario “Unfair” Scenario

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Remarks on Missions The control algorithm issued the correct

command in all cases but one (the “Doggie U-Turn”)

Common failure to respond to turns were caused by the canine looking in the wrong direction (could be mitigated with an additional sensor on his head)

Even in environments with buildings blocking some of the line of sight to GPS satellites, sensor performance was sufficient to complete trials.

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“Unfair” Scenarios

Some trials are tricky (impossible?) for the human to perform.

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Future Work Address the “Doggie U-Turn” with the Xsens Improve radio range and bandwidth to

demonstrate system in longer range scenarios.

Demonstrate system on multiple canines. Integrate pose analysis information into

autonomous canine system. Refine strategies for discovering/optimizing

control system parameters.

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Conclusion

I was able to demonstrate the autonomous control of a trained canine to multiple waypoints using non-invasive methods. Developed a novel state-machine based control

algorithm to model the human guidance of a trained canine.

Demonstrated and analyzed the effectiveness of the algorithm in field trials with a canine.

Automating the guidance of a canine is a complex, cross-disciplinary task that required expertise and contributions from several fields.

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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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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 course.

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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Blind Field Trial Results Small number of trials performed to guarantee that the

canine could be commanded even without any waypoints In a wide, open, featureless field. No markings (not even small artificial markings) were

present on waypoints The canine was given arbitrary initial heading Average distance from goal waypoint: 13.5 m

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Trial Statistics Average max speed of Major during

Forward: 8.65 m/s. Average distance to first waypoint:

49.26m Average distance to secondary

waypoints: 28.31m Success rate on left/right commands:

78% Success rate on Forward, Stop, Recall:

99+%