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The Autonomous Control and Navigation of a Trained Canine

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

of Computer Science

Dr. David M. Bevly, Department of Mechanical Engineering

Committee: Dr. Saad Biaz, Department of Computer Science

Outside 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 Statement

Can 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 K-9s 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 behavior (like vehicles and robots) and are influenced by prior training and their environment in ways that robots 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 control 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 through 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

K-9 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.

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System Architecture

The Trained Canine Remote command and navigation phase Autonomous Canine Phase

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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

ReceiverXbee 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 K-9 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 humans.

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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 7m) 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.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, 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 K-9

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

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Autonomous Canine Phase

Develop, test, and refine 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 (low false-positives)

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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 much more quickly (sub 1 second) than normal 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 is sensor data is

valid4. Calculate derived metrics5. Verify that reported command matches

intended command (robustness)6. Check for anomaly7. Calculate and issue new command via Xbee

radio

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Observing Events

Derived 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

Deviated 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 Since Improvement in Course

Integer Deviation from Desired Course over time

Detect anomalies

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Detailed Control Algorithm Transitions

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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 without line of sight.

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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. Improved schemes 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. Used 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

ReceiverXbee 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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Rationale for Departure from Initial Neural Network Approach

Training data is noisy (human guidance is inconsistent) which leads to false positives.

Difficult to get a large, robust set of specific anomalies to model effectively.

Difficult to tune a neural network in any intuitive way, even when expert knowledge is available

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

and transitions) is readily understandable by the trainers, allowing their insight to be more 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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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