Georgia Tech 2015 RoboBoat Competition
Mustafa Kothawala, Henry Chen, Jesse Chen, Ge Gao, Lansing Wei, Ian Donn, Roi Atalla,
Joshua Winchester, David Moroniti, Dr. Dimitri Mavris
{mkothawala, dmoroniti3, d.mavris}@gatech.edu
June 15, 2015
Abstract – Leucothea is a fully autonomous surface vehicle built to participate in the
Association for Unmanned Vehicle Systems International Foundation’s 2015 Roboboat
Competition held in Virginia Beach, Virginia. This paper describes the design and
manufacture of the hull, propulsion system, Unmanned Aerial Vehicle, electronics and
sensor system, and software infrastructure as well as Leucothea’s approach to solving the
mission challenges.
I. Introduction
The Marine Research Group (MRG) in the Aerospace Systems Design Lab (ASDL) at
Georgia Tech has selected an abstraction based paradigm for robotics pioneered by Georgia
Tech’s research on the Open Control Platform. This modular paradigm allows for the reuse of
sensors, drivers and code across multiple vehicles of different types. Our approach for RoboBoat
2015 is to continue this successful practice in adapting abstracted building blocks from previous
projects. This year the team created a new ASV platform which involved fabricating and testing
a new hull and propulsion system while the team adapted previous year’s electronics, sensors,
and software to best fit the new platform. The team this year is comprised of undergraduate and
graduate students from varying engineering disciplines.
II. Mission Breakdown
This section provides a breakdown of all the missions in the competition and how those
missions translate to requirements for Leucothea. General requirements for each submission are
a weight less than 140 lbs and overall dimensions less than 6 ft long, 3 ft wide, and 3 ft tall.
Pre-Mission Tests – Weight, Thrust, Speed
Prior to beginning the missions, the weight and maximum thrust of the vehicle are measured.
Points are given for low weight and high thrust. The final test is a speed test, were the vehicle
must identify the starting gate buoys based on size and location, then drive at full speed until it
passes through the exit gates.
Mission One – Navigation Test & Obstacle Avoidance
The first mission tests the ASV’s navigational and object avoidance capabilities. A
‘minefield’ of buoys is marked by entrance and exit gates. The area must be entered through the
designated entrance gate, which can be obtained wirelessly. Then the area must be navigated
through while avoiding the buoys, and finally the ASV must exit the area through the correct exit
gate, which is obtained wirelessly. Points are awarded for the ability to enter and exit the area
through the correct gates and for hitting 3 or less buoys. This mission tests the ASV’s ability to
recognize and map objects, to create paths, to avoid objects, and to maneuver.
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Mission Three – Automated Docking
The automated docking mission requires the ASV to dock at different bays, which are
labeled with colored symbols. A sequence is given out wirelessly that the ASV must follow
when docking. Points are awarded for entering a bay and entering the bays in the correct
sequence. This mission tests the ASV’s ability to visually recognize color and objects, to
navigate to a set destination, and to maneuver.
Mission Four – Acoustic Triangulation
In the acoustic mission, the ASV is given a GPS location of a cluster of buoys that have an
underwater acoustic beacon and a specific frequency to search for. The ASV must locate the
specified beacon, identify the color of the buoy and circle it. Points are awarded for successfully
locating the acoustic beacon, identifying its buoy color, and circling it. This mission tests the
ASV’s ability to listen for underwater sound signals, identify certain sound signals, triangulate
signals, navigate to certain GPS locations, communicate through WiFi, recognize objects and
their color, and circle around an object.
Mission Five – Interoperability Challenge
The interoperability challenge tests the ASV’s capabilities of communicating and controlling
another vehicle as well as object recognition. It requires an aerial vehicle to take off from the
ASV, navigate to a specified GPS location, capture and recognize a hexadecimal character, and
return to and land on the ASV. Points are awarded based on the design of the aerial system and
the ability to recognize the hexadecimal character.
III. Mechanical Design
A. ASV Hull
For this year’s entry, a new hull was designed and manufactured. The new hull was designed
for speed, draft, and stability as well as a test bed to experiment new prototype manufacturing
technologies.
Hull Design
To design a hull, significant time was invested researching existing U-class ASV’s[1][2],
gaining a functional understanding of hydrostatics, and boat design methodology[3]. The
configuration selection problem was approached with as large a design space as possible using a
matrix of alternatives to outline all of the design choices. Trade studies were conducted to choose
parameters based on their ability to meet mission requirements such as speed, maneuverability,
weight, and sensor integration capabilities. The final result is a semi-displacement catamaran
with a rounded bottom hull.
A parametric sizing and synthesis tool was created to arrive at the vehicle design point using
high level vehicle geometric dimensions and expected payload. The outputs of the tool are
estimated displacement, drag polar, and thrust model. The drag polar and thrust model were
created using a hydrostatics study that accounts for the major sources of drag: wave drag, viscous
drag, air resistance, and parasitic drag (drag caused by submerged units such as thrusters or
sensors). Figure 1 shows the relationship between drag and velocity and therefore thrust
required.
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Design Point
Figure 1: Thrust Required and Drag Profile of ASV Hull
The final design is shown in Figure 2 and is 5.5 feet long and 2.5 feet wide.
Figure 2: CAD Rendering of ASV Hull
Hull Fabrication
To fabricate the hull with the least cost in time and money three manufacturing methods were
examined: composite hand layup, additive manufacturing, and thermoforming. Composite lay-up
includes creating a male mold, then a female plug which is used to combine a high strength
woven fiber, usually fiberglass or carbon fibers, with an encapsulate matrix material, usually a
polymer. Then the parts made are joined or assembled together to get the complete part. Additive
manufacturing involves 3D printing a multitude of sections due to print area limitations and then
joining them together to create the final part. Thermoforming[8] is heating a polymer to a
temperature where it becomes malleable and using molds to give the necessary form to the part.
A trade study was performed on the three manufacturing methods to determine the fastest
method of production. Based on the trade study, the composite hand layup method would take
18.5 days, the thermoforming method would take 9.5 days, and the additive manufacturing
would take 139 days. Based on the trade study thermoforming was chosen as fabrication method
for the ASV hull and the entire process is outlined in Figure 3.
Figure 3: Thermoforming process and final result
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For thermoforming, first the tooling was created. The hull was divided into four sections,
right and left bottom, top, and lid to be formed individually, then assembled to create the final
hull. Each of the four parts required unique molds. The molds were created using medium
density fiberboard stacked to produce the correct height and the molds were machined using a 3axis K2 Computer Numeric Controller (CNC). After the CNC, the mold surfaces were smoothed
using filler and sandpaper so that no imperfection would be left on the final part.
The next step was vacuum forming the pieces using plastic. The material chosen for the hull
was 3/16 inch marine grade high density polyurethane (HDPE) for its excellent thermoplasticity,
low weight, and ruggedness. Each part was then set up in a vacuum forming machine that has a
single sided heating element, integrated vacuum system, and compatibility to accommodate up to
4’ by 6’ sheet of plastic. The plastic was heat up to 280 degrees Fahrenheit and then pulled over
the part with the vacuum system turned on. The part was then left to cool until it could be safely
removed.
The final step in the hull manufacturing process is the assembly of the parts. Each part was
first trimmed of its excess plastic. The strategy to join the parts was to pop rivet overlapping
sections of each part with a rubber seal in between. To stiffen the hull an internal structure,
including two bulkheads and two longerons, was added. The composite bulkheads were
constructed from Nida-Core Structural Honeycomb material sandwiched between fiberglass plies
and the longerons were pultruded fiberglass composite L-channels. The lid was created using the
same process as the bulkheads with the addition of UV solar panel unit. The solar panel unit was
constructed of two 22 panel units wired in series. The panels were then encapsulated in silicone
and attached to the lid using optical grade silicon and are protected using UV resistant plastic.
B. Propulsion System
Prior to designing the propulsion system, the team looked at past vehicle performance and
scoring results to help understand how propulsion capabilities translated into scoring potential.
Before the vehicle can begin attempting missions, performance metrics are measured and scored
in a series of tests: dry weight, maximum thrust, and top speed. Of these tests, thrust and top
speed are influenced by a selected propulsion design. While the point value for these two tests is
lower than that of a successfully completed mission, they provide an opportunity for teams to
distinguish themselves. Every team who successfully completes a mission will receive the same
score for that mission, so a team that has more thrust or higher top speed would then have a
higher total score than their peers. This motivates the team to use a propulsion system that has
high thrust and top speed so long as it retains the maneuverability required to successfully
complete all missions.
The design of a new hull presents an opportunity to evaluate different propulsion systems and
select one that provides the desired combination of thrust, top speed, and maneuverability. The
team began by first qualitatively comparing different styles of mounting a propeller to a hull,
shown below in Table I.
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Table I: Qualitative Comparison of Propulsion Systems[8]
Pod-Thruster
Propeller and driveshaft
Pump-jet
High
Medium
Low
Drag
High
Low
Medium
Cost
Low
Medium
High
Complexity
The three options are combinations of two decisions: place the motor in the water or inside
the vehicle, and place the propeller outside the vehicle or embedded within. Each choice has an
impact on drag, which affects top speed, and system complexity.
Pod-mounted thrusters consist of a motor and propeller mounted below the vehicle, a simple
design that incurs a large drag penalty referred to as lower unit drag. The previous Georgia Tech
RoboBoat vehicle utilized 4 SeaBotix pod-based thrusters, so their performance parameters are
well known. To achieve the higher desired thrust, the team would have to buy new units, which
is very expensive as the SeaBotix cost almost $1,500 each, or design and fabricate their own.
This could be done with traditional manufacturing techniques.
A propeller connected to a driveshaft moves the motor into the vehicle to reduce lower unit
drag, but since the rules require all propellers to be shrouded, this reduction is small. Fabricating
the shroud and connecting a driveshaft to a motor would be straight-forward, as many of these
parts are commercially available in the hobby RC market.
Finally the pump-jet integrates the propeller and motor into the outer mold line of the
vehicle, greatly reducing the drag but adding an increased level of manufacturing and instillation
complexity. This integration style gives the pump-jet design the highest potential for improving
maximum thrust and top speed. The team also has significant experience with 3D printing and
additive manufacturing techniques, which reduces the risk of the higher manufacturing
complexity.
Figure 4 : CAD Rendering and Printed Result of Pump-Jet Propulsion System and Steering System
Steering
Two types of steering are typically employed by marine vehicles: Rudders or vectored thrust.
As an additional complication, pump-jet propulsion requires a thrust-reverser to enable the
vehicle to move backwards, which must be integrated with the steering design. The team
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investigated both methods of steering and decided upon a rudder steering system because the
minimum length for the nozzle to be effective was larger than vehicle sizing limits.
The design utilizes rudders on either size of the nozzle, and a dual-exhaust ‘ramshorn’ flow
channel that is dropped over the nozzle to redirect flow around the pontoons and towards the
front of the vehicle, as shown in Figure 4. The rudders were shown to work well at higher
speeds, and the performance of the reverser meant that at lower speeds, tight turning could be
achieved by reversing one of the two jets.
Propeller Design
The propeller was designed using OpenProp, a MATLAB toolbox that uses lifting line theory
to predict propeller performance optimizing efficiency and power, created by MIT and
Dartmouth. A top speed of approximately 10 ft/s was selected based on thrust analysis, shown in
Figure 1, implicating a propulsion system that produces about 30 lbs of thrust total, or 15 lbs per
unit. OpenProp enables parametric design based on desired thrust at a velocity by varying RPM,
pitch distribution, blade count, and rotor size, which, based on 10 m/s velocity and 30 lbs thrust,
creates a design space of 720 combinations.
The results of OpenProp were for a 5-bladed propeller of 75mm diameter, spinning at 5000
rpm. The propellers were 3D printed from the CAD model and installed for testing and
validation. The results show the vehicle produces approximately 28 lbs of thrust at 6500 rpm,
which is a higher than predicted RPM. This discrepancy is most likely caused by effects the
analysis does not capture like swirling and dirty inflow, imperfect manufacturing, etc.
Figure 5: Rapid Prototyping Process for Marine Propeller Using OpenProp and 3D Printing
C. Aerial System
The interoperability mission poses a unique design challenge for an aerial system due to the
requirements for launching from and returning to the ASV. To solve this problem, the team
decided upon a tethered multirotor as the UAV. The tether serves as the power line, altitude
control, and a guide for landing. The tether, composed of power and ground wires and a Kevlar
string, is managed through a powered spool attached to the ASV. Powering the multirotor from
the boat has the advantage of increasing endurance by uncoupling battery size from UAV
weight. Endurance is further extended by utilizing the vehicle’s 80 watt solar array to charge the
ASV-mounted battery. The UAV consumes 120 Watts delivered through the tether wire at 240
Volts. The power is then converted to 12 Volts to power the UAV motors, electronic speed
controllers, microprocessor, camera, and antennae. The UAV was built from carbon fiber and 3D
printed parts to minimize structural weight. The stabilization and autonomy of the UAV is
handled by the open source MultiWii platform. The communication between the multirotor and
the ASV is handled by the 3DR Radio. A downward facing WiFi enabled camera is used to take
pictures and transmit them to the ASV for image processing.
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Figure 6: Aerial System
IV. Electrical Design
The electrical system for Leucothea was designed with safety, endurance, and ease of
troubleshooting in mind. All electrical lines are laid out simply and expansively as well as
labeled to increase ease of use throughout the team.
A. Power Systems
The ASV is powered through four independent power systems: one for the computer,
sensors, propulsion and coolant system, and finally the multirotor. The four systems separate
critical functionality for redundancy, and enable rapid battery replacement during testing. The
computer and sensor systems have an endurance in excess of 6 hours off a single 14.8V 5Ah
battery each, while the propulsion system is swapped every hour or so based on usage. The
multirotor has a unique power system due its special power delivery system described in Section
III. Power is stored for each system in separate lithium ion batteries and the multirotor battery is
continuously recharged through the ASV solar panel grid.
B. Computer and Communications
The computer on board is the Intel NUC which has an i5 processor with Intel Wireless-AC
network chip. This computer does all of the ASV’s tasks including reading sensors, commanding
the boat, and higher level autonomous functions. The ASV communicates through a WiFi router
but can be commanded through a 2.4 GHz RC connection. Control of the ASV from the RC
system or boat passes through a hardware multiplexer and can be switched using the RC
transmitter.
V. Sensors and Actuators
A. LIDAR
The LIDAR system used for the point cloud segmentation is the Hokuyo UTM-30LX sensor.
It has a 0.1 to 30 meter range with and accuracy of 50 mm and 270° scanning range. It scans at
40 Hz and is talks to the computer through USB. The LIDAR is mounted to the front of the ASV
on a one degree of motion gimbal and is used to scan for objects such as gates, mines, and docks
and for general object avoidance.
B. IMU
The Inertial Measurement Unit used on the ASV is the Microstrain 3DM-GX3 which
includes triaxial accelerometer, gyro, and magnetometer sensors as well as temperature sensors
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and utilizes MEMS technology to save weight. It has a data output rate of up to 100 Hz and
provides attitude data for autonomous functions.
C. GPS
The GPS unit used is the NOVATEL FlexPak6. It has a root mean square accuracy of 1 m
and updates at 10 Hz. The GPS unit is used to determine the position and velocity vector of the
ASV as well as navigation.
D. Cameras
A Sony PlayStation Eye is used as the main ASV camera. It captures video and pictures in
standard definition at 60 fps and is used for a multitude of tasks such as capturing gate colors and
docking displays. The camera on the UAV is a WiFi enabled CCTV camera, which captures
video at 720p at 30 fps. It is used for computer vision in the interoperability challenge.
E. Hydrophones
Two TC4013 Teledyne RESON Hydrophones are used for beacon triangulation in the
acoustic mission. The hydrophones can detect frequencies from 1Hz to 170 KHz and the
sensitivity is -211 dB re 1 V/μPa.
VI. Software
A. System Architecture
The boat’s system architecture has been successfully used on previous AUVSI RoboBoat
entries. The software architecture resembles the Sense-Think-Act paradigm with an additional
behavioral control aspect [5]. In the literature this is commonly referred to as Hybrid ReactiveDeliberative Control [6]. Figure 7 shows an overview schematic of the software architecture. In
the Sense-Think-Act paradigm, each of these three fundamental stages feeds data into the next
stage. In this architecture, sensor data acquisition, processing and actuation are done
sequentially. The high level logic and decision stage (or thinking stage) rectifies multiple
behavioral objectives though the use of a series of voting matrices. Each voting matrix, or
behavior, (e.g. obstacle avoidance, hold heading, buoy navigation, etc.) outputs a local target
position preference in the form of a vote matrix. All behaviors’ votes are then fused in an arbiter,
with the highest weighted destination location selected. This method of control is referred to as
the Distributed Architecture for Mobile Navigation (DAMN)[7]. The output of the DAMN arbiter
is then fed into the acting stage that consists of the position and velocity controller as well as the
actual thruster motors.
Autonomous functions combine all the sensory inputs through pre-specified behavioral
routines or behaviors and generate motor commands through the DAMN arbiter which generates
target positions based on the behavior weights, and high-level controls are realized as a finite
state machine described below.
The high level control - or mission planner - is realized as a finite state machine that controls
the weight of each behaviors vote and therefore its importance on the target position in the
DAMN arbiter. By setting weights to zero, it can also dynamically deactivate behaviors. The
transitions from one state to another are based on the detection of objects marking the start or
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end of any mission segment. Additionally, the mission planner ensures that the boat always
remains in a defined state and never gets stuck in an obstacle or attempting a task.
Figure 7: Autonomous Software System Architecture
B. Autonomous Functions
1. Environment Sensing
The primary source of environmental data will come from a single LIDAR mounted to tilting
mechanisms, allowing for 3D point cloud generation. Each LIDAR scan generates an array of
ranges to the nearest object corresponding to a horizontal field of view from the LIDAR. The
incorporation of a tilting mechanism provides a three dimensional array of distances constructed
from these horizontal slices. Each ’slice’ of LIDAR data is translated into the boat’s coordinate
frame and then into a fixed global frame using information from the IMU and the Kalman filters
position estimator. The resulting data is a point cloud depicting the surrounding environment that
compensates for the dynamics of the vehicle. This approach has been successfully used on other
maritime platforms constructed by Georgia Tech.
2. Point Cloud Segmentation
Point clouds contain a large amount of data in the form of thousands of points from which we
need to extract useful information, i.e. clusters, shapes, and features. Points are clustered based
on centroid and a matching algorithm identifies primitive shapes. All of these recognized
primitive shapes and any clusters that could not be matched with a shape are then GPS-tagged.
The tagged objects are stored which aids the vehicle in navigation and object avoidance.
3. Camera Aided Object Recognition
Leucothea uses a PlayStation Eye Camera described in Section V for ASV computer vision
and OpenCV was chosen as the image processing library due to its large community and
versatility. Both the PS Eye and OpenCV are not C# compatible so appropriate drivers were
installed from Code Laboratories and Emgu CV was used to act as a bridge for OpenCV.
Within OpenCV, we have developed a number solution to help navigate the Roboboat
course. Buoy detection is implemented through color detection and circle detection. First, the
image captured is threshed to a binary image using limits on the hue, saturation, and brightness
values of each pixel. This results in a binary image for each of the colors used for the buoys. The
threshed image is then blurred and eroded to remove artifacts, and then the remaining blobs are
dilated. Within these binary images, a Hough transformation is used to detect circles, and report
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their estimated center points within the camera. Given that we know the field of view of the
camera, we can then correlate these findings with the data gathered from the Lidar, resulting in
increased confidence in what has been found, and fewer false positives.
To read the seven-segment display in the interoperability challenge, contour detection was
used. The image was first resized to a smaller resolution to reduce error, and then the image is
cropped to a size determined by a bounding box of the contours. The image is then threshed to a
binary image using limits on each pixel’s intensity. The binary image is smoothed using a
Gaussian kernel to filter out noise. With this processed binary image, contours were determined
using the simple chain contour approximation method. Then the k-nearest neighbor algorithm
was used to determine the character. This algorithm classifies images by using a set of sample
data, which in this case consists of 3x5 images, comparing the sample data points with the
image, and then “voting” on the data points to choose a character.
Finally, to differentiate between the different shapes that will be displayed on the dock, the
image is once again threshed based on brightness values, obtaining a binary image. First, the
Hough circle transformation is used, and if no circles are found, edge and vertex detection is
used. Knowing the number of edges and vertices within the image, the shape that is seen by the
camera can be easily determined.
VII. Testing
Testing was an integral part in the teams approach to this competition. Each part of the ASV
was tested individually and then as a system to ensure robustness. The software is tested as it is
being designed by team members throughout the year through the team’s Github repository and
remote desktop setup of the computer and sensors. The hardware subsystems are tested
individually before being integrated into the boat. The ASV system was tested at Sweetwater
Creek State Park. The systems tested were the vehicle velocity and position estimator, object
avoidance, propulsion and steering, GPS navigation, both radio and WiFi communications,
mission recognition, and multirotor subsystem.
VIII. References
[1]Navy,
U. S. The Navy Unmanned Surface Vehicle (USV) Master Plan. http://www. navy.mil/navydata/technology/
usvmppr.pdf (2007).
[2]Martin, Antoine. UNMANNED MARITIME SYSTEMS: GLOBAL REVIEW OF TECHNOLOGY, ROADMAPS,ROLES,
CHALLENGES & OPPORTUNITIES, AND PREDICTIONS.
[3]T. Gooding, A Framework for Evaluating Advanced Search Concepts for Multiple Autonomous Underwater Vehicle (AUV)
Mine Countermeasures (MCM). Cambridge, MA: MIT, 2001.
[4]
A Vacuum Forming Guide. Formech International Ltd. Web. Retrieved10 Apr. 2015. http://inventionstudio.
gatech.edu/wp/wp-content/uploads/2010/11/FormechVacuumGuide.pdf.
[5]R. Brooks, A robust layered control system for a mobile robot. Journal of Robotics and Automation, vol. 2, no. 1, pp. 1423, 1986.
[6]G.
A. Bekey, Autonomous Robots: From Biological Inspiration to Implementation and Control (Intelligent Robotics and
Autonomous Agents). The MIT Press, June 2005.
[7]J.
Rosenblatt, DAMN: A Distributed Architecture for Mobile Navigation. Pittsburg, 1997.
[8]Official
Boater Safety Education. Boat Georgia Course, Retrieved 5 June 2015, https://www.boat-ed.com/
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