The Autonomous Control and Navigation of a Trained Canine

The Autonomous Control and Navigation of a Trained Canine Winard “Win” Britt Committee Co-Chairs: John A. Hamilton, Jr., Department of Computer Science David M. Bevly, Department of Mechanical Engineering Committee: SaadBiaz, Department of Computer Science Outside Reader: Paul Waggoner, Canine Detection Research Inst.

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.

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

Outline • Introduction & Key Contributions • System Architecture • Canine • Hardware & Sensors • Software & Control Algorithm • Experiments, Results, and Discussion • Concluding Remarks • Questions

Problem Statement Can a canine trained to respond to audio and vibration commands be autonomously directed to given waypoints without human guidance?

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.

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.

Key Contributions • 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. • Demonstrated scenarios in which autonomous control of a canine surpasses that of the human operator.

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. • Machine Learning has been successfully applied to a wide variety of classification and modeling problems, including a number of efforts relating to vehicle and robotic control.

System Architectures Two relevant phases consist of: • Remote command and navigation phase • Autonomous Canine Phase

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.

A “Remote Controlled” Canine GPS Satellites Command Module: Issues tone commands to the K-9 and outputs those commands to the Rabbit. Rabbit 4100: Collects and parses the sensor data from the various sensors and command module, then sends to the Xbee modem. Sensor Pack Rabbit Processor GPS Receiver XSens Xbee Radio Modem Command Module UBLOX GPS: Provides latitude, longitude, velocity, and heading. XSens: Provides filtered acceleration and orientation data. Radio: Transmits the parsed sensor information and the currently active commands over the wireless link at 38400 bps. Handset: transmits the current command wirelessly to the tone generator. Canine Major: Responds to the tone to follow along the intended path. Data Sink Handset for Command Module Trainer: Issues the tone and vibration commands for “left”, “right”, “stop”, “recall”, and “forward” to guide the K-9 through his handset. Operator: Starts/Stops recording data for various experimental trials.

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.

Sensor and Command Data Summary

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.

Sample Trial

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 failure rate is about 33%.

GPS Satellites Sensor Pack Autonomous K-9 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. Rabbit Processor Command Module: Receives commands from the rabbit and issues them to the K-9. GPS Receiver Xbee Radio Modem GPS: Provides latitude, longitude, velocity, and heading. 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. Command Module Data Sink Operator: Inputs destination coordinates. Starts/Stops recording data. Operates control algorithm. K-9: Responds to the tone to follow along the intended path.

Autonomous Canine Phase • Develop, test, and refine control algorithms. • Perform trials to validate the effectiveness 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.

Goals of the Autonomous Control Algorithm • 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) • Should decouple canine performance from the skill of the canine operator

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. • Changes in states are accompanied by a new command actuation (a new tone or vibration) High Level Control Algorithm State Machine View

Advantages of a State Machine • The idea of states and transitions is understandable by the trainers and veterinary experts, allowing their insight to be more readily captured in the form of rules. • Calculatingsystem parameters from data analysis and field trials is (relatively) straightforward. • False positives (calling non-anomalous behavior anomalous) are virtually non-existent.

Anomaly Detection • Anomaly detections are caused either by undetectable obstacles (rare), canine error(common), or sensor error (uncommon). • Maintain state variables based on desired heading (as calculated to the next waypoint) and distance from the next waypoint. • Sustained increasing distance from the target and/or sustained deviation from the target will cause a stop and a new directional command to be given. • Large angular 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).

Anomaly Correction • Tricky business - the human trainer typically "recalls" after anomalies • Typically the canine makes mistakes (wrong turns) for a reason - he wants to search something. • Issue: If canine ignores the tones/vibrations once, how likely is it that he will suddenly stop ignoring them. Risk could be mitigated with additional hardware.

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)

Analysis of 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.

“Unfair” Scenarios • Some trials are tricky (impossible?) for the human to perform without line of sight.

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.

Conclusion • I was able to demonstrate the autonomous command of a trained canine to multiple waypoints using non-invasive methods. • Automating the guidance of a canine is a complex, cross-disciplinary task that required expertise and contributions from several fields.

Questions • Questions? • Comments? Nice comments are nice too.

Backup Slides

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.

Phase I (Legacy Training) GPS Satellites Rabbit: Collects and parses the sensor data from the various sensors, then sends to the Xbee modem. Sensor Pack Rabbit Processor GPS Receiver IMU Xbee Radio Modem Binary Tone Generator GPS: Provides latitude, longitude, velocity, and heading. IMU: Provides acceleration and rate of turn. Radio: Transmits the parsed sensor information over the wireless link. Handset: transmits the current command wirelessly to the tone generator. K-9: Responds to the tone to follow along the intended path. Data Sink Handset for Binary Tone Generator Operator: Records the tone changes manually. Trainer: Issues the tone commands to guide the K-9 through his handset.

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.

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

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

The Autonomous Control and Navigation of a Trained Canine