RoboSub 2020: VantTec Technical Design ReportVTec U-III

Pedro Sanchez, Alejandro Gonzalez, Melissa Sanchez, Ivana Collado, Alejandra Monsivais,Alonso Ugarte, Pedro Gamez, Gabriel Leal, Fernando Resendiz, Cristina Garcia, Diego

Martinez, Saul Bermea, Gabriel Bermudez, Gerardo Berni, Andres Sanchez, EdisonAltamirano, Penelope Ceron, Roberto Mendivil, Sebastian Martinez, RogelioSalais, Nadia Garcia, Jorge Medina, Ernesto Monroy and Leonardo Garrido

Tecnologico de MonterreyMonterrey, Mexico

vanttecmty@gmail.com

Abstract—The overall strategy, vehicle design elements,and experimental result from VantTec for RoboSub 2020are presented. The strategy was constructed as a two-yearplan, and from experience from RoboBoat competitions.The higher priority was given to have a robust andreliable hardware and software system to use as a basefor future editions, and to develop autonomy capabilitiessuch as object and sound detection and localization,and path-following control. Hence, the Gate and Buoyschallenges are the priority tasks to tackle, as they do notrequire any additional hardware components than the basesystem. Members were divided in mechanical, electrical,and software areas, with a balance between software orhardware experience. Moreover, the main mechanical andelectrical design elements are the frame, enclosures, PCBs,and sensors and actuators, which bring the desired ro-bustness and reliability. Likewise, the system architecturedesign connects the required components for hardwareand software communications, achieving determinism andtask prioritization. The software architecture design allowsfor modular integration of the required software compo-nents. Next, computer vision and path-following controlalgorithms allow the VTec U-III unmanned underwatervehicle to perceive obstacles and navigate through plannedwaypoints. Furthermore, as the COVID-19 pandemic grew,the proposed strategy adapted to exchange manufacturingtime for design time, and all of the challenge-orientedhardware components were designed. Similarly, the courseapproach was fully implemented in simulation, whereresults show successful completion of path-following con-trol, and the Gate, Buoys, and, additionally, Torpedocompetition tasks. Finally, results indicate the computervision algorithm is capable of detecting the proposedRoboSub 2020 obstacles.

Index Terms—Unmanned underwater vehicle, robotics,autonomy, computer vision

I. COMPETITION STRATEGY

This section discusses the team member organiza-tion, the course strategy, and the engineering designstrategy.

A. Team OrganizationVantTec has participated in RoboBoat competi-

tions since 2017, and the team decided to participatein the RoboSub 2020 competition in the Fall 2018semester, as a two-year project. During the firstyear of the project, only 5 members composed theteam, using that year to research and develop asystems engineering strategy. Thus, the team couldlearn about the required hardware (given the lackof experience in the underwater environment), andfinish the first design phase, beginning manufactur-ing and prototyping in the Fall 2019 semester. Atthe beginning of Fall 2019, sub-teams were createdfor mechanical, electrical, and software areas, com-posed by members from mechatronics engineering,digital systems and robotics engineering, mechani-cal engineering, and computer science majors. Fi-nally, sub-team leaders have the higher experiencein their respective areas, and the number of teammembers with hardware or software expertise isbalanced. Additionally, VantTec, as an establishedrobotics team, already has members responsible forgroup management, finances, and media.

B. Course StrategyThe two-year preparation allows the team to

attempt a strategy capable of solving all of the

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challenges. However, as a new RoboSub team, andwith previous experience as a rookie team in similarcompetitions, the strategy instead attempts reliabil-ity and robustness as a basis. This approach arisesfrom the fact that VantTec has worked better whileattempting a technically-and-time-feasible strategy,rather than overloading the members with respon-sibilities to try and tackle all of the competitiontasks. Thus, even while the intention to design andmanufacture all of the necessary components, and tothink of solutions for every challenge, exists, higher-priority is given to the UUV platform and tasks thatdo not require additional actuation (apart from UUVmotion control).

Hence, taking into account the RoboSub 2020task ideas, the higher-priority challenges are Gateand Buoys. Thus, the goal of the first RoboSubparticipation for VantTec is to successfully com-plete two challenges, with a reliable hardware andsoftware base system. This system will work as astarting point for future RoboSub editions.

Moreover, a simulation environment was plannedto be built in the Spring 2020 semester to test the al-gorithms and software architecture. The simulationenvironment became a priority as the COVID-19pandemic grew, allowing the team to attempt addi-tional courses. The Torpedo task is also attempted insimulation, and the time selected for manufacturingand in-water testing was instead occupied by themechanical team to further work on the design of theadditional challenge-oriented hardware subsystems(torpedo launcher, marker dropper, and robotic armgripper).

C. Engineering Design Strategy

The design engineering strategy relied on a yearof research for members to learn and gain experi-ence on the submarine hardware requirements. Dur-ing this year, ideas were developed for the overallsystems engineering approach, including full-designconcepts, frames, required sensors and actuators,and challenge-oriented subsystems.

To follow the team strategy, reliability implies theUUV design protects the electronics from water,motion is robustly achievable with thrusters, andthe hardware and software architectures can includedifferent subsystems without damaging other sub-systems. Hence, the priorities were selected as fol-

Fig. 1. CAD rendering of VTec U-III mechanical design.

lows: reliability in UUV design, automatic controlimplementation, computer vision object detectionand localization implementation, and hydrophonesystem implementation. Thus, with the selected pri-orities, the team can focus on specific design tasksto successfully accomplish the proposed objectives.

II. VEHICLE DESIGN

A. Mechanical

The VTec U-III unmanned underwater vehicle(UUV) mechanical design is based on robustness,modularity and ease of manufacturing, which bringreliability into the system. The design also takes intoaccount aspects of hydrostatics and buoyancy. Thecomponent distribution considers a weight balanceconfiguration to facilitate modeling analysis. Thematerials and components are found in Appendix A.

1) Frame: The frame consists of structural piecesthat support three acrylic electronic enclosures, andadditional systems such as the torpedo launcher, themarker dropper, and the gripper. The frame also hasthe function of protecting the thrusters in case ofcollision, and of dealing with the static and dynamicloads. An open-frame with external oval supports,and inner transversal and longitudinal supports wasdesigned. Aluminum 6061 is used for the exteriorframe of the vehicle, as well as for the inner lateralsupports. In the case of the central supports, ABSfilament is used due to the durability ABS provides,and the complex shape manufacturing capabilities of3D-printing.

2) Enclosures: The enclosures house the elec-tronic boards, sensors and batteries. Three en-closures are categorized as one main enclosures

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Fig. 2. CAD rendering of torpedo launcher system.

and two secondary enclosures. The main enclosurecontains the PCB boards, sensors, and processors.Due to RoboSub rules size requirements, a 6-inch-diameter cylinder enclosure was selected, along aclear end cap to assist camera location and visu-alization. A custom-made PLA rack allows easy-removal, organization and safety of the internalelectronics. Both of the secondary enclosures are3-inch-diameter cylinder enclosures, each holdingan independent battery. The three enclosures havealuminum end caps with holes to route cables forthe actuators. Finally, the enclosures have siliconesealant to avoid leaks, as well as plugs in the endcaps either to route cables safely or seal the unusedholes.

3) Actuators: A six-thruster configuration wasselected to actuate the system, using BlueRobotcsT-200 thrusters. Four thrusters are placed in thecorners at a 45º angle, providing an X-shapedconfiguration. Two additional thrusters are facedupwards along the transversal axis, maximizing themovement along the normal axis. To secure stabilityand control, the thrusters were located in the farthestpossible ends along the longitudinal and transversalaxis.

The torpedo launcher (Fig. 2) is composed bytwo linear actuators and a torpedo-container. Thetorpedoes are custom-made and 3D-printed usingPLA with 100% infill, achieving a roughly neutrallybuoyancy.

The marker dropper (Fig. 3) is divided in threeparts: a container, a sliding platform and a linearactuator. The linear actuator is attached to thesliding platform located inside the container. Once

Fig. 3. CAD rendering of marker dropper system.

Fig. 4. CAD rendering of arm and gripper system.

the actuator extends, it releases one of the storedmarkers. The designed markers are 3D-printed PLAsquares with the extruded logo on the top surface.

The selected design features for the robotic armwere simplicity and reliability, thus a linear one-degree-of-freedom arm was designed. The gripper,shown in Fig. 4, is 3D-printed and modular to beeasily changed depending on the competition rulesof the year. The gripper is actuated by a HS5086servo motor, which controls the open-close motion.The arm is conformed by a rack and pinion locatedin a fix base, also controlled by a HS5086 servomotor. Helical gears are selected since they havehigher load bearing capacity.

B. Electrical and Electronics

The electrical and electronics design was drivenby the requirements stemming from the challengesfrom previous editions of the competition, the needfor deterministic firmware behavior and precise dataacquisition, high computing power for algorithmexecution and, finally, internal component reliabilityand safety. To tackle these requirements, a decen-

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tralized architecture was devised, distributing thelow and high-level tasks between an electronic con-trol unit (ECU) and a central processing unit (CPU).Additionally, two power supplies were included(11.1V and 14.8V) with their respective circuitryto accomplish the different voltage requirementsof the components (3.3V, 5V, 12V and 16V). Thefollowing sections address these design choices indepth.

1) Sensor Selection: A high-quality, reliable In-ertial Measurement Unit (IMU) is a crucial partof any autonomous vehicle, specially in underwaterscenarios, where the lack of a GPS signal con-straints the UUV to rely only on accelerometerand gyroscope signals to obtain velocity and po-sition values. Doppler Velocity Log (DVL) and/orunderwater GPS technologies could assist the state-estimation problem, but adding the disadvantage ofsignificantly increasing cost. Therefore, the Vector-nav VN-200 IMU was chosen for the VTec U-III, since other models present an advantage onlywhen the use of GPS signal is available, and itis of previous VantTec ownership. Furthermore, toprovide an accurate way of measuring depth (otherthan Z-axis accelerometer data), the BlueRobotics30bar pressure-based depth sensor was added to thesystem.

Additionally, since challenge locations are iden-tified using acoustic pinger signals, the inclusion ofhydrophones on the submarine is necessary. For this,due to budget constrains, one high-end TeledyneRESON TC-4013 and two lower-end Aquarian H1Cmodels were chosen to provide enough informationfor target position triangulation purposes.

Object detection and identification are critical ac-tivities in all of the challenges, as well as providingadditional support when it comes to navigation. AStereoLabs Zed Mini stereo camera system wasproposed over other computer-vision-oriented sen-sors for front-facing view because of its capabilityto both detect objects using camera images, andlocalize the objects with the depth perception ability.A Raspberry Pi Camera Module is also part of thearchitecture, providing a bottom-facing view fromthe vehicle perspective. Both cameras also fit theenclosure internal space constraint.

For system safety, a BlueRobotics SOS LeakSensor was selected to provide an emergency stop

Fig. 5. Hardware architecture with power distribution.

signal for the internal electronics in case of sealingfailure. Finally, a visible, accessible mechanical kill-switch is included to cut off power to all of thevehicle subsystems for emergency scenarios.

2) System Architecture Design: Leak and depthsensing, thrust actuation, as well as hydrophonesignal sampling were designated as critical tasksof the UUV, since they are involved in naviga-tion and safety mechanisms. Thus, to additionallyaccomplish clock frequency and connectivity re-quirements, an STM32F103C8T6 was chosen as theECU, which is in control of actuator manipulationand most of the data acquisition. To ensure deter-minism and task prioritization, the ECU runs on aReal-Time Operating System (RTOS).

In contrast, a CPU with high-processing poweris required because of the guidance, navigation andcontrol (GNC) algorithms. Thus, an NVIDIA JetsonTX2 was selected over other controller models,due to the integrated Graphical Processing Unit(GPU), which optimizes computer vision algorithmperformance.

To allow for data transfer between the ECU andthe CPU, multiple communication protocols wereexplored. Ultimately, in view of its expansion ca-pabilities, robustness and existing supporting infras-tructure on both Linux and the STM32 HAL library,Controller Area Network (CAN) was determinedas the vehicle internal bus protocol. Specific dataframes were designed in concordance with the bytesize of the variables of interest, while their respec-tive message identifiers were assigned following theCAN 2.0b standard, described in [8]. Sensor andactuator distribution between both controllers wasdriven by the connectivity needs of each component,as depicted on Fig. 5.

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3) Power Distribution: The power demand ofthe T-200 thrusters (16V @ 24A) is comparablyhigher than that of the controllers (5V @ 1A for theECU, 16V @ 1.2A for the CPU), actuators (rangingfrom 5V to 12V @ max 1.8A) and sensors (rangingfrom 3.3V to 5V @ max 1A). In consequence, athruster-dedicated battery was included in the design(BlueRobotics Li-Ion 14.8V @ 18Ah), both as asafety measure and to ensure vehicle autonomy. Forthe rest of the subsystems, a Zippy Li-Ion Battery(11.1V @ 8Ah) was selected. Additional circuitrywas designed to protect sensitive components andto provide the different voltage levels required foreach element.

C. Software

The UUV software components development wasaccomplished following a methodology partiallybased on the Software Development Life Cycle de-fined in the ISO-12207 Standard [9]. The resultingprotocol included the following tasks, listed in orderof execution: Requirements Analysis, ArchitectureDesign, Unit Design, Unit Development, Unit Test-ing, Integration and, finally, Integrated System Veri-fication and Validation. The coming sections presentthe results of the proposed development process, aswell as a description of the different subsystems.

1) Software Architecture Design: The architec-ture design was driven by two main objectives:to exploit the potential of the different availablecontrollers and peripherals, and to accomplish thesoftware requirements involved in the solution of thecompetition challenges. After an in-depth analysisof these objectives and related literature, such asthe GNC system scheme proposed by Fossen in [6],the minimum required software components weredefined, depicted in Fig. 6.

For the ECU components, a communicationsbridge module for data transfer between the ECUand the CPU; an actuator control module for thethrusters, gripper, torpedo launcher, and markerdropper; a sensor monitor module to sample theleak and depth sensors, as well as the signal fromthe kill switch. These applications run on a Real-Time Operating System (RTOS), which manages thepriorities of the tasks and ensures that the samplingrates, as well as the information processing andactuation, remain deterministic.

Similarly, the Ubuntu 16.04-based CPU runsRobot Operating System (ROS) Kinetic, where thevarious software components interact between themas ROS Nodes. These modules are: an ECU bridge,to provide a link between the low-level ECU func-tions and the resulting commands from the CPUalgorithms; a controller module, which receivesdesired velocity, depth and heading references, andoutputs desired thrust values; a guidance module,whose inputs are waypoint lists, while its outputs aredesired controller values; a mission manager mod-ule, in charge of triggering the user-requested mis-sions and sending commands to both the guidanceand controller modules; a master node, which de-codes user input and provides the according signalsdirectly to the controller or triggers pre-programmedmissions; a perception module, for processing cam-era input; and an odometry module, which receivesaccelerometer and gyroscope information from theIMU, processes it, and publishes velocity and posedata.

Additionally, the ground station also runs ROSKinetic and is connected via Ethernet to the UUV.Currently, the only module running on the groundstation is the keyboard reader module, which for-wards key presses to the master node.

2) Modelling and Identification: The mathemat-ical model of the UUV took a higher priority whenthe COVID-19 pandemic caused restrictions on theavailability of the hardware. Thus, what once wassupposed to be a supporting tool, became the onlysolution to allow the development of the vehicle tocontinue. The mathematical model is based on themethodology proposed by Fossen in [6], while theidentification of the parameters was done based onthe procedure developed by Eidsvik [7].

3) Guidance and Control: The motion controllerdevelopment focused on the essential degrees offreedom (DOF): surge u, sway v, heave w, andyaw ψ. For surge and sway, speed controllers weredeveloped, whereas heave and yaw have positioncontrollers, since this provides depth and headingcontrol for the vehicle. The four DOF use non-linear Proportional, Integral, and Derivative (PID)controllers with feedback linearization, similar to[10], which were hand-tuned using the previouslyobtained mathematical model. For path-followingcontrol capabilities, a the Line-of-Sight (LOS) guid-

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Fig. 6. Software architecture for the complete UUV system.

ance law presented by Fossen in [6] was developed.This feeds the controller the desired heading valuesto navigate to a specific point in space. The sameROS node selects speed and depth references.

4) Computer Vision: The computer vision sys-tem of the UUV is composed of 2 sensors: a ZEDstereo camera and a Raspberry Pi camera module.The custom trained Convolutional Neural Network(CNN) YOLO-Tiny 3 [1] uses the RGB imageobtained from the ZED for buoy detection. Thestereo point cloud is used separately to calculatethe distance, size, and orientation of buoys andother obstacles, such as the gate and the octagon.The Raspberry Pi camera is used as a down-facingcamera, and the RGB image from this camera isused for detecting directional markers to differentchallenges.

The Convolutional Neural Network (CNN)YOLO was chosen as the detection strategy dueto previous experience and positive results obtainedin other VantTec projects, such as RoboBoat [5].For training the CNN, images from past RoboSubcompetitions were taken and edited as a representa-tion for RoboSub 2020 buoy images. To reduce theamount of images that had to be manually capturedand edited, a data augmentation library was created.The created library introduces variation in qualita-tive elements like color, illumination, distortion andorientation. The data augmentation strategy has beenpreviously proven effective for obstacle detection in[2].

Image labeling is a time consuming andmonotonous task. Thus, to lower the amount ofimages that had to be manually labeled, an auto-matic labeling strategy was implemented. First, theMATLAB Image Labeler Toolbox was used to label

a small amount of images of each object class. Then,an Aggregate Channel Features (ACF) detector [3]was trained for each class using MATLAB. After-wards, each detector was applied on the entire dataset and labels where manually verified and modifiedif necessary. Lastly, MATLAB image label objectswere converted to YOLO format, and YOLO-Tiny3 was trained using the open source neural networkframework Darknet [4].

5) Simulation and Testing: The existence of asimulation environment allowed the software teamto verify and validate the software architecture andits modules while the UUV was not available,effectively increasing the efficiency and speed of thedevelopment process. As the COVID-19 pandemiccame into the picture, the relevance of the simula-tion environment became more evident; therefore, aconsiderable part of the work of the software teamwent into creating a more robust simulation.

Using the previously-obtained mathematicalmodel, the states of the vehicle under the currentinputs could be obtained. These values were thenoutput to RViz, where the 3D CAD model wasused to display the current pose of the vehicleinside the simulation space. Additionally, a 120degree Field of View (FOV) cone with a radius of3 meters was also depicted inside the simulation,as seen in Fig. 7.

Different software modules were developed tobuild the test environments for each of the chal-lenges, shown in Fig. 8. These modules serve asa mock-up of what the perception module wouldoutput in a real-world scenario, since they take intoaccount the pose of the UUV to only publish theobjects that are inside the conical FOV.

For the 3D models of the challenge elements,

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Fig. 7. 3D model of the UUV with FOV representation in RViz.

Fig. 8. 3D models for the gate (left), buoy (center) and shootout(right) challenges.

simplified depictions were designed. Visual ele-ments were discarded, such as the images of thepolice and gangster for the gate and shootout chal-lenges. It is assumed that the vehicle already has theidentification information that would be generatedby the computer vision algorithms. Nonetheless, incase of the buoys and the shootout board, visual aidssuch as color differences and orientation indicatorswere included to facilitate the verification of thealgorithms.

III. EXPERIMENTAL RESULTS

In-water experiments were not possible due toCOVID-19. Thus, all of the challenges were solvedusing the previously mentioned simulation environ-ment. Computer vision algorithms were tested onlywith pre-existing data sets. The following sectionsdiscuss the solution approaches and results.

A. Computer VisionData augmentation results expanded the data set

from 1095 to 1459 total images, and allowed objectclass balancing for impartial detector training. Someexamples of data augmented images are shownin Fig. 9. Examples of detected objects using thetrained YOLO CNN are shown in Fig. 10. Table Ishows the performance of the trained object detec-tor. The results obtained outperform previous workwith YOLO-Tiny 3 from previous RoboBoat project

Fig. 9. Data Augmentation Examples

Fig. 10. YOLO detector Examples

results [5], demonstrating the benefits of the dataaugmentation strategy. As mentioned before, thecomputer vision strategy also relays on the detectionof obstacle size and orientation using the ZED Ministereo point cloud, however this has not been testeddue to the COVID-19 pandemic.

B. Waypoint Navigation

As a first validation of the guidance and controlsubsystems, a set of pre-defined waypoints wereinput to the guidance module. The goal was tocommand the vehicle to follow a specific path, withvarying changes of heading and depth, to ensure thatthe algorithm presented no mathematical errors andthat the vehicle would be capable of reaching anypoint the missions would input. Fig. 11 depicts the

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TABLE IPERFORMANCE OF YOLO TINY 3 DETECTOR

gate police gangster gun badge marker mAP IoU F1(AP%) (AP%) (AP%) (AP%) (AP%) (AP%) (%) (%) Score96.67 94.08 94.58 97.86 98.78 99.93 96.98 82.98 93.0

Fig. 11. Waypoint navigation simulation.

simulated waypoint navigation from two differentperspectives, demonstrating the capability of theUUV to follow a path in three dimensions.

C. Gate Challenge

The gate challenge solution consisted of a statemachine, whose inputs are the detected objectsaround the UUV, the ego vehicle pose, and theselected side to follow, be it gangster or policeofficer. Once the mission is triggered, the UUV willexecute a search algorithm, which consists of a yawsweep to locate any obstacles that in range. If theobstacles are any other than the gate, it will advanceto a point in front of it before starting to sweeponce more. Said displacement alternates directionsbetween positive and negative sway to maximize thesearch area. Once the gate is located, it is assumedthat the computer vision algorithm successfully de-tects which side of the gate corresponds to whichfigure. Here, a new reference frame is created withthe origin located in the center of the gate. Then,three waypoints are calculated according to the poseof the gate and the desired side, placing one in front,one in the middle and one in the back, so thatthe UUV crosses and passes the gate completely.Results produced by this algorithm are shown inFig. 12.

D. Buoy Challenge

Tackling the buoy challenge required more cre-ativity. Since the buoys can be oriented differently,

Fig. 12. Gate challenge simulation.

the identification of the image becomes more dif-ficult. Therefore, a two-step detection and identifi-cation process was the selected approach for thischallenge. Using the same search algorithm fromthe gate challenge, the UUV searches and tracks theposition of each of the buoys. Once it has alreadyfound both of them, the euclidean distance betweenthem is used to obtain a midpoint, which serves asthe center of a circle, whose radius is proportionalto the distance between the buoys. This circle isdiscretized into a waypoint array, which is fed tothe guidance module. The vehicle then orbits aroundthe buoys facing always toward the center point, inan attempt to identify the image that correspondsto each of the previously registered buoys. Afterassigning an ID to the buoys, the UUV approachesthe one that matches the previously assigned side.A depiction of the generated trajectory and overallexecution of the algorithm can be seen in Fig. 13.

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Fig. 13. Buoy challenge simulation.

E. Shootout ChallengeFor the shootout challenge, it was assumed that

there would be one board with two sides. Since theaiming system was not ready for this competitiondue to complications with algorithm testing due toCOVID-19, the simulated challenge focused solelyon aligning the vehicle to the center of the board,so that the aiming algorithm could then take over.Starting with the search procedure described for thegate challenge, the UUV looks for the board. Onceit is located, its center point is used as a referenceto create a circle with a radius of 2.5 meters, toallow for the camera to have a clear view of theboard itself. The vehicle orbits the board, lookingfor the desired image (gangster or police officer)and once the orientation of the image matches thatof the UUV, it stops. Finally, the heading of thevessel is aligned with center point of the board (orwhere the whole would be), to prepare to shoot.

IV. CONCLUSIONS

The presented team organization and competitionstrategy for the RoboSub 2020 competition involvetaking advantage of the multidisciplinary nature ofthe team, while prioritizing those challenges whichdo not involve actuation other than that of thethrusters. The design of the vehicle was done from asystems engineering perspective, taking into accountthe influence of the mechanical, electronics and soft-ware aspects on the performance and the ability to

Fig. 14. Shootout challenge simulation.

accomplish the desired requirements to successfullyexecute the competition challenges. Furthermore, adigital twin of the UUV was developed to enablevirtual tests of the motion control and path followingalgorithms, as well as the solutions to each of thepriority challenges: gate, buoys and shootout. Theimpact of the COVID-19 pandemic constrained theavailable resources of the team, preventing furtherphysical development of the submarine. Nonethe-less, the results from the simulated environmentshow that the proposed solutions are technicallyfeasible, since all three challenge simulations werecompleted successfully.

ACKNOWLEDGMENTS

This work was supported by: Hacsys, VectorNav,Google, ifm efector, Uber ATG, RoboNation, Velo-dyne Lidar, NVIDIA, Skysset, Akky and Greenzie.Finally, VantTec appreciates the support from theuniversity, Tecnologico de Monterrey.

REFERENCES

[1] J. Redmon and A. Farhadi, “YOLOv3: An Incremental Im-provement,“ Technical Report.

[2] I. Navarro, A. Herrera, I. Hernandez, L. Garrido, “Data Aug-mentation in Deep Learning-based Obstacle Detection Systemfor Autonomous Navigation on Aquatic Surfaces”, MexicanInternational Conference on Artificial Intelligence, 2018.

[3] Dollar, P., R. Appel, S. Belongie, and P. Perona. “Fast FeaturePyramids for Object Detection.” Pattern Analysis and MachineIntelligence, IEEE Transactions. Vol. 36, Issue 8, 2014, pp.1532–1545.

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[4] J. Redmon, “Darknet: Open Source Neural Networks in C,”2013-2016, https://pjreddie.com/darknet/.

[5] A. Gonzalez, et al., “VantTec Technical Design Report 2019”,RoboBoat 2019, 2019.

[6] T. I. Fossen, “Handbook of Marine Craft Hydrodynamics andMotion Control”, John Wiley and Sons, Ltd., 2011.

[7] O. A. Eidsvik, “Identification of Hydrodynamic Parametersfor Remotely Operated Vehicles.”, Norwegian University ofScience and Technology, 2015.

[8] Robert Bosch GmbH, “CAN Specification, Version 2.0”, 1991,http://esd.cs.ucr.edu/webres/can20.pdf.

[9] ISO/IEC/IEEE, “ISO/IEC/IEEE-12207 International Standard- Systems and software engineering - Software life cycleprocesses”, IEEE, 2017.

[10] Vervoort J.H.A.M., “Modeling and Control of an UnmannedUnderwater Vehicle”, University of Caterbury, 2009.

APPENDIX ACOMPONENT SPECIFICATIONS

See Table II.

APPENDIX BOUTREACH ACTIVITIES

A. Conexion TecConexion Tec is an event organized by Tec-

nologico de Monterrey’s School of Science andEngineering which highlights the best engineer-ing projects each semester. VantTec participatedwith the technology and researched developed forRoboBoat and RoboSub in both spring and fallsemesters.

For more information please visit:https://www.facebook.com/conexiontec/

B. Global AI on Tour MTYMicrosoft Student Partners organized this event

to promote technologies related to artificial intelli-gence (AI). VantTec gave a virtual talk during thisevent, showcasing the history of the group and theAI applications for autonomous systems.

For more information please visit:http://globalaimty.net/

C. Expo ManufacturaIn partnership with sponsor ifm efector, a mobile

robot was developed to demonstrate the capabilitiesof ifm efector sensors. This robot showcased at theExpo Manufactura 2020, serving as a platform toattract clients and promote technology to studentsat the Expo.

For more information please visit:https://expomanufactura.com.mx

D. INCmtyINCmty is the largest festival of entrepreneurship

in Latin America, based in Monterrey, Mexico.In November, VantTec took part representing theuniversity’s School of Science and Engineering.

For more information please visit:https://incmty.com/

E. Rice University GCURSRice University organizes the Golf Coast Under-

graduate Research Symposium, which allows youngresearchers to present their work, meet Rice faculty,and receive valuable feedback. VantTec membersgave presentations on the work and research madeby the group.

For more information please visit:https://gcurs.rice.edu/

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TABLE IICOMPONENT SPECIFICATIONS

Component Vendor Model Specifications Quantity CostBuoyancy Control Blue Robotics Buoyancy Foam 16in x 8in x 1in 1 35Frame Own design VantTec 3.0 Aluminum 1060 1 206Waterproof enclosure Blue Robotics 6” series 11.75” 1 265Waterproof enclosure Blue Robotics 3” series 8.75” 2 204ROV Tether Blue Robotics Fathom 35m 1 158Plug Blue Robotics Leak proof plug - 23 26Penetrator Blue Robotics Leak proof plug - 23 61Thruster cable Blue Robotics Thruster Cable - 6 20Cable penetrator Blue Robotics M10 Cable Thruster 8mm 6 10Thruster Blue Robotics T-200 - 6 1048ESC Controller Blue Robotics Basic - 6 150High Level Control (ECU) STMicroelectronics STM32F103C8T6 1 20Linear actuator Hydroworks 2in stroke 12V - 4.6A 3 80Servomotor HiTEC HS5086WP 4.8V - 6.0V, 181º 2 60Kill switch Blue Robotics Kill Switch - 1 14Battery Blue Robotics Lithium-ion Battery 14.8V - 18Ah 1 289Battery Zippy Lithium-ion Battery 11.1V - 8Ah 1 220Step down Pololu 5V - 5A - 1 15CPU NVIDIA Jetson TX2 GPU and 8 GB memory 1 600CPU Carrier NVIDIA Quasar - 1 488Internal Comms Network - - CAN Bus 2.0b - -External Comms Network - - TCP/IP over Ethernet - -Programming Language - - C/C++ - -IMU VectorNav Technologies VN-200 - 1 4000Camera Stereolabs ZED Mini 1080p Resolution 1 450Camera Raspberry Pi Camera Module V2 8 Mega Pixel Resolution 1 450Hydrophone Telodyne RESON TC 4013 - 1 1200Hydrophone Aquarian H1C - 2 318Depth/Pressure sensor Blue Robotics 30 Bar - 1 80Leak sensor Blue Robotics Leak Sensor - 1 26Manipulator Own Design Own Design 3D Printed Gripper 1 5Algorithms: vision - - Yolo Tiny V3 1 0Algorithms: localization and mapping - - Reference frames and 3D Computer Vision internal development 1 0Algorithms: autonomy - - Line-Of-Sight Guidance 1 0Algorithms: autonomy - - Non-Linear PID Motion Control 1 0Open Source Software - - OpenCV 1 0Open Source Software - - ROS Kinetic 1 0Open Source Software - - FreeRTOS CMSIS V1.0 1 0Open Source Software - - Eigen (C++ Library) 1 0Team Size - - - 21 members 0HW:SW Expertise Ratio - - - 9:12 0Testing time: simulation - - - 300h 0Testing time: in water - - - 0h 0