Amador Valley Robotics Club 1

Amador Valley Robotics Club:Design of Marlin AUV 2018

Surya Ramesh (President); Mechanical: Calvin Qin (VP), Ethan Apalis, Ian Chu, Benjamin Nguyen, DanielZhou, Jocelyn Zhu; Electrical: Wonjoon Lee (VP), Shreyas Krishnaswamy, Amogh Prajapati, Sarah Tandean,Maxim Vovenko, Athan Yang, Lucas Yang; Software: David Zhang (VP), Arnav Garg, Varun Iyer, TimothyKanarsky, Jeffrey Li, Jeremy Li, Pedro Pachuca, Justin Shih, Victor Shu, Emil Tu, Daniel Yang, Jonathan

Yang; Business: Rachel Lam, Marianna Szambelan

Abstract—Amador Valley Robotics Club(AVBotz) is a student-led organization at AmadorValley High School in Pleasanton, CA. The teamconsists of more than 30 students split into foursubdivisions: mechanical, electrical, software, andbusiness. Building from previous generations ofexperience, the club has upgraded and refinedMarlin, an autonomous underwater vehicle(AUV), to be prepared for changes made to thecompetition in RoboSub 2018. This year, instead ofpursuing a complicated design, a lot of focus wasplaced onto refining low-level, but fundamentalparts to Marlin. This includes software changesto the control system, such as the integrationof a Kalman filter for accurate state estimation.In addition, rather than attempting all of thetasks, our new objective is to focus our effortsupon a smaller selection of tasks that we canbe sure to complete. Reflecting these aspirations,the mechanical and electrical systems on Marlinwill focus primarily on providing accurate vision,acoustic, and state data rather than manipulatingobjects.

Figure 1: AVBotz Team 2018

I. COMPETITION STRATEGY

Departing from the overambition of previ-ous years, AVBotz has refocused on a smaller

selection of tasks - gate, dice, roulette, andpinger. This consolidated approach has re-sulted in a simpler and more reliable systemthat is easier to debug and test. For example,while a pump-based ball system was pro-posed for “Buy a Gold Chip” and “Cash In”tasks, it was never implemented because ofthe strict constraints for software. We believeit is unlikely that the sub would be able toautonomously position itself within the nar-row parameters needed for the pump intaketo effectively draw in the golf ball. Instead, asimple pneumatics ball-dropping system wascreated to take advantage of the two bluechips that Marlin starts with, in order to com-plete the “Play Roulette” task. For the “En-ter Casino” and “Shoot Craps” tasks, regu-lar OpenCV processes coupled with machinelearning models ensure that Marlin is able tolocate these objects underwater. We chose tofocus on these tasks because, apart from drop-ping chips onto a roulette table, they can becompleted using Marlin’s different degrees offreedom. For example, ”Shoot Craps” can becompleted using a series of well-coordinatedmovements, including ramming and backingup to optimize the number of points obtained.In addition, while we implemented a torpedosystem, we decided to forego the “Play Slots”task as it would require many extra hoursof pool testing to calibrate the path of thetorpedo.

To realize our vision, our team has workedhard this year. A majority of the focus wasplaced on planning and design during the

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school year while much of the testing hap-pened during the summer. As a high schoolteam, many students are busy with collegeapplications and standardized testing on topof their regular coursework and jobs duringthe school year. Because of this, we only meetonce a week. However, once summer begins,we increase our meetings to 6 days a weekand put in countless hours at team members’swimming pools as well as our school’s pool.

Figure 2: Our AUV: Marlin

II. DESIGN CREATIVITY

A. Mechanical

Figure 3: DVL Mount modeled inSolidworks

The mechanical subdivision is responsiblefor the design, manufacturing, and mainte-nance of Marlin’s structural components. Theobjective of this year was mainly to im-prove some of the shortcomings we identifiedlast year, and to accommodate radical new

changes on the AUV for the updated compe-tition. Some designs for certain competitionareas were designed but not implemented onMarlin due to software integration difficulties.Mechanical objectives also included creatingall of the new props in order to simulatethe competition arena. These included theorange pole and gate, dice, and roulette tableamong others. Some highlights of this yearincluded a brand new DVL mount designedby mechanical, which allowed us to mountthe Explorer DVL we purchased (see Fig-ure 3). In addition, the pneumatics dropperwas modified, allowing the team to completemore challenges set forth by the competition.Finally, a new pneumatics box was createdin order to fix the waterproofing problemspresented by the previous one. The new pneu-matics box boasts an IP68 rating and allowsmechanical to easily access the contents with-out worrying about water leakage. Overall,the changes presented and executed by me-chanical ensures the rest of the divisions canperform their jobs without worrying about thestructural integrity of Marlin.

B. Electrical

Figure 4: Overview of Marlin’s electricalsystem

The electrical subdivision is responsible forall of Marlin’s internal and external electricalcomponents (see Figure 4). This year, elec-trical mainly focused on improving Marlin’srack as a whole to ultimately expand and

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enhance Marlin’s efficiency and performance.Recognizing the limits of the current elec-trical setup, this electrical looked to par-tially remodel the electrical system. Theseadjustments include using a new Cincon DC-to-DC converter to increase power capacityfrom 300W to 600W, and mounting a newpicobox x300 power supply. This expansionof power capacity and the rearrangement ofpower rails now allow Marlin to supportan Nvidia Quadro P6000 GPU, which en-ables Marlin to handle more accurate andmore advanced levels of vision processing.In addition, the electrical team replaced theoriginal Point Grey 1.3MP front camera witha Blackfly S Color 20.0 MP (Sony IMX183)machine vision camera. By utilizing the newmodularity of the electrical system, the teamwas also able to mount a Teledyne ExplorerDoppler Velocity Log (DVL). The mountingof the DVL enables Marlin to precisely obtainvelocity data to establish a more accurate con-trol system. Electrical also added a newly re-modeled kill switch and acoustic system. Asa result of this remodeling, Marlin now hasa centralized external power switch as a partof the kill switch (original function of killingmotor power is maintained). Furthermore, theupdates to the kill switch allowed for more ef-ficient debugging of the electrical system. Asfor the acoustic system, remodeling was doneto better align the system with the changes inthe hydrophone algorithm (Time Differenceof Arrival to Phase Shift). By keeping whatworked and diligently replacing what failed,the current electrical system of Marlin is themost powerful of any vehicle in the historyof AVBotz.

C. SoftwareThe software subdivision is responsible

for writing the software stack that powersMarlin for RoboSub, ranging from lower-level hardware communication to higher-leveltask and route planning strategies. Both thecontrol system and mission system were com-pletely rewritten, reflecting changes madeto the competition and weaknesses in priorsoftware systems. Nautical, the new controlsystem, handles low-level communication to

Figure 5: Atmega2560 Microcontroller

the hardware on Marlin and accurate stateestimation. It operates on an ATMega2560microcontroller, and interfaces to the sensors(AHRS, DVL, etc.) and thrusters throughserial UART pins (see Figure 5). Estimatedstates in Nautical are represented using sixdegrees of freedom: X, Y, Z, roll (phi), pitch(theta), and yaw (psi). The AHRS returnsaccurate attitude data, while a Kalman filter isused to fuse accelerometer, DVL, and depthsensor readings to provide accurate positiondata along the body axes. The model for theKalman filter is represented using position,velocity, and acceleration across the X, Y, andZ axis. On the other hand, six PID controllersmapping to the different degrees of freedomon Marlin are used to move efficiently be-tween states.

Figure 6: Particle Filter Localization

Aquastorm, the new mission system, han-dles high-level task and route planning. It

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is split into different processes run on mul-tiple threads that communicate over sharedmemory. The mission process determines theoptimal route for Marlin to take, based onapproximated locations, time constraints, andpoint values. The modeling process handleslocalizing Marlin to the competition pool,using a particle filter of particles that repre-sent Marlin’s possible locations (see Figure6). Known state updates are used to updatethese particles each iteration of the filter, andas Marlin detects objects in the competitionpool, the particles are redistributed using re-cursive Bayesian estimation. Eventually, theparticles converge to the true position ofMarlin.

The webserver process creates a GUIthrough which the software team can interactwith the rest of Aquastorm and Nautical. Itis hosted on a Flask server, which allowsthe software team to use it in a regular webbrowser such as Chrome or Firefox. Imageand state data is sent from Aquastorm overZMQ sockets to the webserver so it is easilyaccessible for debugging.

III. EXPERIMENTAL RESULTS

Throughout the course of Marlin’s devel-opment in 2018, we experimented with andresearched options that were either removedor never implemented into the AUV. Al-though many developments were not contin-ued due to inconsistencies or better alterna-tives, our team still made many successfulchanges for RoboSub 2018.

A. Non-Linear Kalman FilterBefore implementing a linear Kalman fil-

ter, we researched the Extended KalmanFilter (EKF) and Unscented Kalman Filter(UKF). Both an EKF or UKF would haveallowed Nautical to represent the state modelusing non-linear functions. For example, onecould combine approximated speeds fromthrust settings with attitude data from theAHRS using trigonometric functions for moreexact state estimation. However, this provedto be computationally expensive on a micro-controller and excessive as the Explorer DVLreturned accurate readings.

Figure 7: Machine Learning for the ShootCraps Task

B. Machine LearningIn terms of machine learning and vision,

given the improvements to generalization thatneural networks realize with more data, train-ing images were collected whenever possibleat pool tests. Images were collected in a vari-ety of conditions, and in different pools to en-sure that the trained model would be robust.When training the model, we experimentedwith a variety of hyperparameters such aslearning rate and batch size. Resources suchas the Google Cloud Platform were utilizedin order to quickly optimize models, as welacked on-site resources to simultaneouslytrain multiple models. Efforts were made tolower the time to perform inference on animage, so that the system would perform inreal time. We experimented with differentarchitectures, including other Faster-RCNNs,but their accuracy was deemed to be insuf-ficient. Ultimately, adjusting the number ofpredictions in the first stage of Faster-RCNNinference allowed us to achieve reasonableinference times.

C. HydrophonesThis year for signal processing, we

switched from an FPGA to the PJRC Teensy3.6 microcontroller for its fast processor,built-in ADCs, and ease of development inC++ using well-supported existing libraries.Because of limited memory, we implementedan online DFT for only the four compe-tition frequencies by combining batches of

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transformed samples with an infinite impulseresponse filter. A majority of developing timewas spent testing and debugging various com-ponents of the software in order to ensurereliability and correctness. We determinedthat multilateration responded poorly to smallnoise in the phase shift estimates, so weinstead approximate the angle of arrival usinggeometric methods which sacrifice precisionfor stability.

D. Ball System

At the beginning of the season, a ballsystem was designed and created that uti-lized water pressure using a pump to pickup, move, and drop balls to complete tasks.However, for the ball system to work, theball intake port would have to be positionedin close proximity to the golf ball. Thisproved to be very challenging for the softwaresubdivision as that required a high level ofaccuracy, and eventually, the ball system wasscrapped in order to redirect the resourceson tasks that our team would have a higherchance of successfully completing.

IV. ACKNOWLEDGEMENTS

The AVBotz team would like to thank Mrs.Bree Barnett Dreyfuss for allowing us to hostweekly pool tests at the Amador Pool to testchanges made on the vehicle. We would alsolike to thank Luke Shimanuki for his invalu-able advice in hydrophones. Furthermore, wewould like to thank the following sponsorsfor donating their resources to help AVBotzpull through purchases and upgrade the sub:Datron, Nvidia, Tanius Technology, Positron-ics Incorporated, Teledyne Marine, Videoray,Cincon, Xilinx, Subconn, and PNI Sensor.Without the contributions of these sponsorsalong with our sponsors from previous years,AVBotz would not have been able to buildand refine Marlin.

REFERENCES

[1] Surat Teerapittayanon, et al. BranchyNet:Fast Inference via Early Exitingfrom Deep Neural Networks.

http://www.eecs.harvard.edu/ htk/publication/2016-icpr-teerapittayanon-mcdanel-kung.pdf,2016.

[2] Jia Yangqing, et al. Caffe: ConvolutionalArchitecture for Fast Feature Embedding.arXiv preprint arXiv:1408.5093, 2014.

[3] Ng, Andrew. Machine Learning.coursera.org/learn/machine-learning/.Coursera. 2017.

[4] Jeff Donahue, et al. DeCAF: A DeepConvolutional Activation Feature forGeneric Visual Recognition. arXivpreprint arXiv:1408.5093, 2013.Coursera. 2017.

[5] Principles of PID Control and Tuning.Eurotherm. [Online]. Available:http://www.eurotherm.com/temperature-control/principles-ofpid-control-and-tuning

[6] A. Doucet and A. M. Johansen. ATutorial on Particle Filtering andSmoothing: Fifteen years later. [Online].Available: http://www.cs.ubc.ca/ ar-naud/doucet johansen tutorialPF.pdf,2008

[7] A. W. Eric and R. van der Merwe.The Unscented Kalman Filter forNonlinear Estimation. Presented inIEEE Adaptive Systems for SignalProcessing, Comm., and ControlSymposium. [Online]. Available:https://www.seas.harvard.edu/courses/cs-281/papers/unscented.pdf, 2000

[8] M. I. Ribeiro. Kalman and ExtendedKalman Filters: Concept, Derivationand Properties. [Online]. Available:http://users.isr.ist.utl.pt/ mir/pub/kalman.-pdf, 2004

[9] Bertrand Delguette and JulieGreenberg. The Discrete FourierTransform. [Online]. Available:http://web.mit.edu/gari/teaching/6.555/le-ctures/chDFT.pdf, 1999

[10] Barret Zoph, Vijay Vasudevan, JonathonShlens, and Quoc V. Le. LearningTransferable Architectures for ScalableImage Recognition. arXiv preprintarXiv:1707.07012v4, 2018.

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APPENDIX ASPECIFICATIONS

Table I: General

Team Size 30 peopleHW/SW Expertise Ratio 1:1Testing Time: simulation 100+ hoursTesting Time: in-water 100+ hours

Table II: Mechanical Components

Component Vendor Model/Type SpecificationsFrame Custom Aluminum T6061 Density 2.7 g/cm

Strong corrosion resistanceWaterproof Housing Custom Acrylic hull sealed with 2

rubber O-ringsDiameter: 9.5 in (24 cm)

Waterproof Connectors SubConn Circular Series [Varies Based on Series]Micro-Circular SeriesPower SeriesCoax Series

Thrusters VideoRay M5 Thrusters Power Input: 48V DCMax Thrust: 23lbsbuilt-in electronic speed con-trollers

Propellers VideoRay Standard propellers 90mm (3.5 inches)3 blade propeller with collet(smooth shaft)

Actuators Numatics 0438D01-04A Bore Size: 7/16”Stroke Size: 4.0”

CO2 Cartridge JT 90g CO2 Cylinder Cartridge Non-refillable

Table III: Electrical Components: Power Delivery

Component Vendor Model/Type SpecificationsBattery Venom 16000mAh 6S Battery Type: LiPo

Volts: 22.2V eachCell Count: 6SCapacity: 16000mAh

DC to DC Converter 1 Cincon CFB600-48S24 Input Voltage: 36V-75VOutput Voltage: 24VMax Output Current: 25APercent Efficiency: 92%

DC to DC Converter 2 Picobox x300 Input Voltage: 16V-24VOutput Voltage: 3.3V, 5V, 12VMax Power: 39W, 60W, 192WPercent Efficiency: >90%

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Table IV: Electrical Components: Low Level Control

Component Vendor Model/Type SpecificationsControl Board Rugged Circuits Rugged MEGA Microcontroller: ATMega 2560

expanded with Arduino ProtoshieldInertial Measurement Unit (IMU) PNI Sensor TRAX AHRS Communication: RS232

Static Heading Accuracy: .3◦

Non-static Heading Accuracy: 2.0◦

Tilt Resolution: .01◦

Depth / Pressure Sensor Ashcroft Model K1 Accuracy: ±0.50% or ±1.00% spanPressure Ranges: Vacuum to 20,000 PSI

Doppler Velocity Log (DVL) Teledyne Marine Explorer DVL Type: Phased Array TransducerFrequency: 600kHzMax Depth: 1000m

Table V: Electrical Components: Main Computer

Component Vendor Model/Type SpecificationsCPU Intel i7-4790T Number of Cores: 4

Number of Threads: 8Processor Base Freq: 2.7GHzMax Turbo Freq: 3.9GHz

Motherboard Jetway NG9J-Q87 Mini ITX USB 2.0 Ports: 4USB 3.0 Ports: 2HDMI Ports: 1PCI-E 3.0 x 16 Slots: 1RJ45 LAN Ports: 2

RAM Corsair Vengeance 16GB 2x8GB DDR3 SODIMM RAMMemory Speed: 1600MHz

Storage Samsung 1TB mSATA 860 EVO SSD Max Seq Read Speed: 550 Mb/sMax Seq Write Speed: 520 Mb/s

GPU Nvidia Quadro P6000 CUDA Cores: 3840Memory: 24GB GDDR5XMax Power: 250W

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Table VI: Electrical Components: Cameras

Component Vendor Model/Type SpecificationsFront Camera FLIR BFS-U3-200S6 Resolution: 5472 x 3648

Megapixels: 20MPFrame Rate: 18FPSSensor Type: CMOS

Front Camera Lens Kowa LM6HC Focal Length: 6mmMount: C MountHorizontal Angle: 96.8◦

Vertical Angle: 79.4◦

Down Camera FLIR BFS-U3-13Y3C-C Resolution: 1280 x 1024Megapixels: 1.3MPFrame Rate: 170FPSSensor Type: CMOS

Down Camera Lens Theia SY125M Focal Length: 1.3mmResolution: ≤5MPMount: CS MountHorizontal Angle: 135◦

Vertical Angle: 119◦

Table VII: Electrical Components: Hydrophones

Component Vendor Model/Type SpecificationsHydrophones Teledyne Reson TC4013 Frequency Range: 1Hz to 170kHz

Resistant to seawaterData Acquisition and Signal Processing PJRC Teensy 3.6 I/O Pins: 62

Processor: 180MHz ARM Cortex-M4RAM: 256KSerial Ports: 6Analog to Digital Converters (ADC): 2

Table VIII: Software

Programming Language 1 C++Programming Language 2 PythonOperating System Ubuntu 18.04Open Source Software OpenCV (Image Processing Library)

Tensorflow (Machine Learning Library)ZMQ (high-performance messaging Library)Armadillo (Matrix Library)

Algorithms: Vision 1 OpenCV K-Means Clustering, Canny Edge Detection, Gaussian and MedianBlur, Contour Detection, Blob Detection

Algorithms: Vision 2 Tensorflow using the FasterRCNN frameworkAlgorithms: Acoustic Online DFT, First Order Infinite Impulse Response Filter, Phase ShiftAlgorithms: Localization and Mapping Monte Carlo Localization (Particle Filter)Algorithms: Autonomy Markov Decision Process

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APPENDIX BCOMMUNITY OUTREACH

This year our club participated in several outreach events, either as workshop leaders oras volunteers to help promote robotics and engineering often by showcasing our submarine.We held local events at three Pleasanton middle schools: Harvest Park, Hart and PleasantonMiddle Schools. To promote coding at younger ages we lead weekly after school lessons atHarvest Park and PMS. Many team members, especially from the software division, taughta 20 week course on Java and Python to 100 middle school students during the school year.Our members led workshops at a local hackathon, ACE Code Day, providing a plethoraof workshops on robotics and computer science, ranging from a crash course in machinelearning to an introduction in hardware components. Events like these allow us to share ourknowledge to 200-300 future engineers and potential club members, thus promoting our cluband furthering its mission. When we met with the local Lego Mindstorms robotics club atHarvest Park we arrived to find 50 middle school students ready to learn, to fix problems,and to ask questions to expand their knowledge. At HART’s annual STEAM night, whenour sub entered the multipurpose room, students were awed by the size and complexity ofour submarine. And finally, we transported our sub to the Livermore Innovation Fair, wherewe explained its inner workings and capabilities to hundreds of families and children of allages. Through all of these outreach events we hoped to inspire students to explore the fieldof robotics.