Design of Vessel to Compete in the2016 Maritime RobotX Competition

Matthew Begneaud, Ethan Davidson, Ronald Kisor,Chandler Lagarde, and Joshua Vaughan

University of Louisiana at LafayetteDepartment of Mechanical Engineering

Lafayette, Louisiana, USA

Abstract—This paper covers the systems and techniquesdeveloped by the University of Louisiana at Lafayette teamto enable autonomous navigation and task completionin the Maritime RobotX competition.The competitioninvolves developing an autonomous marine system usinga pre-existing marine craft as a development platform:Marine Advanced Research’s Wave Adaptive Modular-Vessel. The vessel must autonomously navigate environ-ments while avoiding obstacles and identifying targetswhich dictate goals and give instructions for other tasks.The system must be equipped with various sensors andsubsystems to be capable of completing the competitiontasks. These capabilities include environment mapping,object and color detection, acoustic-pinger localization,projectile launching, and underwater vision.

I. INTRODUCTION

The University of Louisiana at Lafayette (UL Lafayette)team primarily consists of four undergraduate seniorsin Mechanical Engineering, whose senior project is todevelop and program the WAM-V system to competein the Maritime RobotX competition. The competitiongives students the opportunity to gain experience in thefield of autonomous, unmanned vehicles. Teams mayconsist of college undergraduate and graduate students,faculty advisors, and even high school students. Theexperience of working on a complex system, such as anautonomous marine vessel, that is capable of completingthe tasks in this competition is invaluable to students andexposes them to one of the quickest growing fields inengineering and technology.

The UL Lafayette team began work on the systemin the Summer of 2016. The systems were designedand developed using commercially available productsas well as custom designed parts by utilizing rapidproduction methods such as water jet fabrication. A 3DCAD model of the UL Lafayette WAM-V can be seenin Figure 1.

Because the system is operating in a marine envi-ronment, the components were designed to be water-tight and weather-proof. The system was also designed

Fig. 1: 3D Render of the UL Lafayette WAM-V(Propulsion System Not Shown)

for future research use in mind. In order to take fulladvantage of the capability of the vessel’s dynamics andpotential maneuverability, the system utilizes propellerangling as well as propulsion differential to steer thevessel. To enable environment mapping, the vessel hastwo LiDAR sensors, a forward-facing stereo camera,and mono cameras in the starboard, port, and sterndirections.

The system utilizes a distributed computer architecturewith all computers communicating over a network uti-lizing the Robot Operating System (ROS) [2]. UtilizingROS allows for the use of many previously developedpackages which speed up the development of the systemand supports the use of a distributed architecture. Keep-ing computing systems separate also minimizes systemcomputation lag and increases robustness by isolatingfailure points in the system.

In the next section, the strategy behind the overallsystem design will be discussed. Then, in Section III,the physical design of the system will be covered.Section IV will discuss the computing architecture andsoftware design of the system. Testing methods will becovered in Section V. Finally, conclusions will be madein Section VI.

University of Louisiana at Lafayette 2

II. DESIGN STRATEGY

The systems that were added to the vessel were designedto be simple and modular. For example, the onboardlauncher system is separate from the rest of the com-puting and electronic systems on the vessel. Using thisapproach prevents electrical, hardware, and/or softwarefailure from inhibiting the other systems in operation,contributing to the robustness of the system.

The approach for completing the competition tasksis to use environment mapping packages provided byROS while using OpenCV [1] and machine learningto identify targets of interest relative to the tasks. Thenavigation control system utilizes GPS and IMU sensorsfor positioning and heading feedback, as well as LiDARsensors for point-cloud mapping to supplement local-ization of the vessel in its environment. ROS navigationpackages are utilized for general navigation, while morespecific movements and trajectories are handled by cus-tom algorithms. The utilization of pre-written packagesfrom ROS and OpenCV allow for quick developmentusing tested and proven methods, allowing the team tomake quick progress.

III. PHYSICAL SYSTEM DESIGN

The components of the system are kept modular andwater-tight to allow for robust and safe operation. Thesystem is also designed to allow for modularity forfuture use of the vessel as a research platform. Thevessel utilizes Torqeedo electric motors for propulsion,taking advantage of propeller angling and propulsiondifferential to increase maneuverability. The propulsionand computing systems are powered separately, whichminimizes potential damage in the case of electricalfailures and minimizes system reinitialization time af-ter the use of a system kill-switch. The mechanicalsubsystems on the boat are also powered separately;these systems include the projectile launcher system andthe retractable underwater camera system. The payloadplate configuration of the systems are shown in Figure2. Each subsystem is described in more detail in thefollowing subsections.

A. EnclosuresThe WAM-V has five main enclosures onboard whichcan be seen in Figure 3, including a permanent enclo-sure, a removable computing enclosure, two enclosureshousing the propulsion system power, and an enclosurehousing components necessary for operation of thelaunching system. All enclosures are IP-67 rated andutilize waterproof wiring glands to seal out moisture.The permanent enclosure houses components that aretypically always used on the vessel, such as: power forthe computing systems, power for actuation of othersub-systems, GPS/INS sensor, IMU sensor, networking

1. LiDAR2. Stereo Camera3. IP Camera4. Underwater Camera5. GPS & IMU inside

1

1

2

3

3

3

4

5

Fig. 2: 3D Render of Payload Plate Layout

1

1. Computing Enclosure2. Permanent Enclosure3. Launcher Enclosure4. Propulsion Power Ensclosure

2

43

Fig. 3: 3D Render of Enclosure Layout

routers, and motor control computers. The computingenclosure houses most of the computers for the system,including four NVIDIA Jetson TX1 computers, oneRaspberry Pi computer, and the audio interface usedfor the hydrophone system.

The launcher enclosure houses a Raspberry Pi computerfor aiming the launcher and an AC power inverter topower the launcher motor. The two propulsion enclo-sures each house two twelve volt batteries configuredto supply twenty-four volts to each Torqeedo motor.

B. SensorsThe configuration of sensors can be seen in Figure 2.Sensors used on the WAM-V include a HemisphereVector VS131 GPS/INS and a MEMSIC 350 SeriesIMU. Environment mapping and object identificationare achieved using two Hokuyo UTM30LX-EW LiDARsensors, a forward-facing Stereolabs ZED stereo cam-era, and three Foscam IP cameras for starboard, port,and stern vision. One of the Hokuyo sensors is used forgeneral contour detection for mapping, while a secondHokuyo LiDAR is mounted directly below the first in

University of Louisiana at Lafayette 3

1

2

3 4

1. Launcher2. Pan & Tilt System3. Aiming Camera4. Ball Holder/Feeder

Fig. 4: 3D Render of Launcher System

a lower plane of view for detection of small buoysand docks. The ZED stereo camera aids in point-cloudmapping ahead of the vessel, as it is capable of depthperception.

C. Launcher SystemThe launcher system shown in Figure 4 utilizes a com-mercially available pitching machine, modified to shootracquetballs, mounted onto an outdoor surveillance panand tilt system. The system aims by use of visualservoing with OpenCV, via a small camera mountedon the barrel of the launcher. This allows the system toshoot projectiles accurately without needing to move thevessel and maintain a stable orientation for aiming. Thisalso increases accuracy by compensating for movementof the vessel caused by waves. The launcher enclosurehouses an AC inverter used to power the launchermotor and its pan and tilt control system, as well asa Raspberry Pi to control aim and firing of the launcherwith a set of relays.

D. Underwater Vision SystemThe retractable underwater camera system, shown inFigure 5, consists of a large-profile 80/20 pivoting armand frame. The arm is extended and retracted by useof a winch motor. Retraction of the underwater systemreduces the overall drag on the system. Retracting andextending movements are limited by frame-mountedlimit switches, which are actuated by a cam on the arm.A submersible camera is mounted on the end of the arm.

E. Acoustic Pinger Localization SystemThe hydrophone-based acoustic pinger location systemconsists of two hydrophones, one on the port side andone on the starboard side, as shown in Figure 6. Astraveling sound waves emitted by a pinger encounter the

12

2

3

1. Actuation Cam2. Limit Switch3. Camera Arm

Fig. 5: 3D Render of Retractable UnderwaterVision System

Hydrophone

Pinger

Fig. 6: Acoustic Pinger Localization System

vessel, the signal will be received by the hydrophones atdifferent times if the vessel is not heading directly to oraway from the pinger, as can be seen in Figure 6. Thesystem is able to determine the direction of an acousticpinger by measuring the time difference between signalpeaks on each hydrophone. Once the time differenceis sufficiently small, the pinger is either directly aheador behind the vessel. The direction can be checked byturning slightly and seeing which hydrophone is leadingthe other in signal peak time.

IV. COMPUTING ARCHITECTURE & SOFTWAREDESIGN

Subsystems on the vessel are mostly controlled onseparate computers communicating across a ROS net-work, allowing for isolation and parallel development ofsystems. The use of distributed architecture also allowsfor faster processing. A diagram of the computingarchitecture can be seen in Figure 7.

The processing of sensor data from the GPS/INS andIMU and the main algorithm computing is handled

University of Louisiana at Lafayette 4

Algorithm Computing

&GPS/INS & IMU

OpenCVStereo Camera

Underwater CameraRear Camera

OpenCVPort Camera

Starboard CameraStern Camera

Path Planning&

Navigation

MotorControl

Mode LED&

Arm Control

Launcher Aim &Control

NVIDIA Jeston TX1s

Raspberry Pis

Fig. 7: Diagram of Distributed Architecture

by one NVIDIA Jetson TX1. Image processing withOpenCV from all cameras is split across two Jetson TX1computers. LiDAR sensors are processed by the sameTX1 handling path planning and navigation in order tominimize system lag from transferring large data arraysat high rates over the network. Minor functions such ascontrol of the LED mode indicator and actuation of theunderwater vision arm are controlled by a Raspberry Picomputer.

Keeping the software modular allows for easier debug-ging and allows nodes to be restarted individually with-out interfering with other systems and functionalities. Itis also simpler to develop modular programs, which canthen be used to create systems in the form of buildingblocks which can be recycled from other systems.

A. Visual Processing MethodsUsing various OpenCV [1] and machine learning meth-ods, the system is able to visualize its surroundings.The system has been trained to detect buoys and otherobjects of interest using machine learning. Shapes arealso detected using simple contour detection. The colorof the object is then detected using OpenCV to correlatethe object to the current task and identify targets ofinterest. Colors are detected using a combination ofcolor blob detection, masks, and thresholds utilizingthe Hue-Saturation-Value (HSV) color space to producebinary images [1].

Horizon detection is used to filter background noise inthe image, which detracts from object-detection accu-racy. Due to the abundance of blue in the competitionenvironment from the sea and sky, it can be difficult todetect blue objects. The sky is handled mostly by theuse of horizon detection, and water is filtered out withripple detection.

Object centroids are compared to the center of the imageto localize the targets relative to the vessel. This isuseful, for example, in the task of passing through buoygates. The median of the two buoy centroids can be used

Fig. 8: Processed Test Image of Buoy Gate MedianDetection

to identify the middle of the gate as a navigation target,as shown in Figure 8. Using object centroid detectionto localize targets with respect to the vessel is also usedby the system’s custom navigation and path-planningalgorithms.

B. Path Planning & NavigationThe WAM-V utilizes the ROS packages for tasks suchas environment mapping, odometry estimation, andnavigation. The mapping package takes in depth datafrom the LiDAR sensors and ZED stereo-camera tocreate a local costmap of the vessel’s surroundings. Theodometry estimation is achieved by use of a localizationpackage which takes in depth data from sensors as wellas data from the IMU and GPS/INS systems to estimatewhere the vessel is located in its local map.

The ROS navigation package, referred to as the Nav-igation Stack, receives commands in the form of goalpositions in the vessel’s odometry frame. The Naviga-tion Stack utilizes costmap and odometry data whilealso considering the dimensions of the vessel in orderto plan the shortest path to the goal that avoids hittingobjects. The path planner generates velocity commandswhich are sent to a base controller that attempts to keepthe vessel on the path [2].

Once the vessel reaches or senses targets of interest,the system bypasses the waypoint-based NavigationStack to directly send velocity commands to the basecontroller, allowing for more-direct control over thevessel’s movement. For example, when circling buoysfor tasks, the system switches to using visual processingto dictate movement commands for the vessel. Sidecameras are used for feedback to adjust the angularvelocity of the vessel with a PID controller to keepthe buoy in the center of the camera feed, as seen inFigure 9. The same visual-processing-based approach isused for other task-specific movements such as passingthrough buoy gates, parking in docks, and approachingtargets like the LED-panel-sequence buoy.

V. EXPERIMENTAL RESULTS

The team was unfortunately unable to coordinate sig-nificant on-water-testing of the vessel before shipping it

University of Louisiana at Lafayette 5

Error

Camera

Buoy

Fig. 9: Depiction of WAM-V Circling a Buoy

1

23

4

3

3 5

1. LiDAR2. Stereo Camera3. Network Camera4. IMU5. GPS

Fig. 10: 3D Render of Clearpath Husky TestingPlatform

to the competition site; however, testing of algorithmsand navigation was still made possible via a ClearpathHusky rover, which is also programmed with ROS.

The Husky rover was outfitted with a similar sensorconfiguration to the boat, as shown in Figure 10, andwas utilized for algorithm testing. This allows for allalgorithms to be written to publish velocity commandsand goal positions to the system. The only differencebetween testing and competition performance is thecontrol of the movement of the vehicle, as the roverand boat differ in their dynamics and motor control inorder to achieve desired velocities.

VI. CONCLUSION

UL Lafayette team’s design for the Maritime RobotXcompetition focuses on modularity of subsystems toincrease overall system robustness. The design of thesystem was highly influenced by a short developmentperiod and, therefore, benefitted from modular designdue to the ease of parallel development in such systems.The modularity of the systems also aids to the system’soverall robustness. The use of a distributed computing

architecture isolates main software systems, decreasingthe impact of failure points in one system on another.The vessel will also be used for future research.

ACKNOWLEDGMENTS

The team would like to thank Mr. Donald Mosing, theUL Lafayette College of Engineering and Department ofMechanical Engineering for providing funding for thisproject, ASV Global for their technical guidance andlending of equipment, and Road Narrows Robotics andBase Engineering for their sponsorship of this project.

REFERENCES

[1] Christopher M. Barngrover. Computer vision techniques forunderwater navigation. Master’s thesis, University of California,San Diego, 2010.

[2] William D. Smart Morgan Quigley, Brian Gerkey. Program-ming Robots with ROS: A Practical Introduction to the RobotOperating System. O’Reilly, 1005 Gravenstein Highway North,Sebastopol, CA 95472, 2015.