Licentiate Thesis in Vehicle and Maritime Engineering ...kth.diva-portal.org/smash/get/diva2:1502112/FULLTEXT01.pdf · Licenta ThsVldMhTrm lamclEgTRrll ImcreAa rel-ntn s S-CLE-SLElFLEO

Licentiate Thesis in Vehicle and Maritime Engineering

Hydrobatics: Efficient and Agile Underwater RobotsSRIHARSHA BHAT

Stockholm, Sweden 2020www.kth.se

TRITA-SCI-FOU 2020:44ISBN 978-91-7873-721-5

kth royal institute of technology

SriharSha Bhat H

ydrobatics: Efficient and Agile U

nderwater Robots

KTH

2020

Hydrobatics: Efficient and Agile Underwater RobotsSRIHARSHA BHAT

Licentiate Thesis in Vehicle and Maritime EngineeringKTH Royal Institute of TechnologyStockholm, Sweden 2020

Academic Dissertation which, with due permission of the KTH Royal Institute of Technology, is submitted for public defence for the Degree of Licentiate of Engineering on Monday the 14th of December 2020, at 2:00 p.m. in U1, Brinellvägen 28A, Stockholm.

© Sriharsha Bhat

Cover photo: Swedish Maritime Robotics Center (SMaRC) TRITA-SCI-FOU 2020:44ISBN 978-91-7873-721-5 Printed by: Universitetsservice US-AB, Sweden 2020

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AbstractThe term hydrobatics refers to the agile maneuvering of underwater vehicles. Hydro-batic capabilities in autonomous underwater vehicles (AUVs) can enable increasedmaneuverability without a sacrifice in efficiency and speed. This means innova-tive robot designs and new use case scenarios are possible. Benefits and technicalchallenges related to hydrobatic AUVs are explored in this thesis. The dissertationcontributes to new knowledge in simulation, control and field applications, and pro-vides a structured approach to realize hydrobatic capabilities in real world impactareas.

Three impact areas are considered - environmental monitoring, ocean productionand security. A combination of agility in maneuvering and efficiency in performanceis crucial for successful AUV applications. To achieve such performance, two tech-nical challenges must be solved. First, these AUVs have fewer control inputs thandegrees of freedom, which leads to the challenge of underactuation. The challengeis described in detail and solution strategies that use optimal control and model pre-dictive control (MPC) are highlighted. Second, the flow around an AUV duringhydrobatic maneuvers transitions from laminar to turbulent flow at high angles of at-tack. This renders flight dynamics modelling difficult. A full 0-360 degree envelopeflight dynamics model is therefore derived, which combines a multi-fidelity hydro-dynamic database with a generalized component-buildup approach. Such a modelenables real-time (or near real-time) simulations of hydrobatic maneuvers includingloops, helices and tight turns.

Next, a cyber-physical system (CPS) is presented – it safely transforms capabili-ties derived in simulation to real-world use cases in the impact areas described. Thesimulator environment is closely integrated with the robotic system, enabling pre-validation of controllers and software before hardware deployment. The small andhydrobatic SAM AUV (developed in-house at KTH as part of the Swedish MaritimeRobotics Center) is used as a test platform. The CPS concept is validated by usingthe SAM AUV for the search and detection of a submerged target in field operatingconditions.

Current research focuses on further exploring underactuated control and motionplanning. This includes development of real-time nonlinear MPC implementationsrunning on AUV hardware, as well as intelligent control through feedback motionplanning, system identification and reinforcement learning. Such strategies can en-able real-time robust and adaptive control of underactuated systems. These ideaswill be applied to demonstrate new capabilities in the three impact areas.

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Keywords: Autonomous Underwater Vehicles, Modeling, Simulation, Control,Field Testing, Cyber-physical Systems.

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SammanfattningTermen hydrobatik avser förmåga att utföra avancerade manövrer med undervattens-farkoster. Syftet är att, med bibehållen fart och räckvidd, utvigda den operationellaförmågan i manövrering, vilket möjliggör helt nya användningsområden för maxi-mering av kostnadseffektivitet. I denna avhandling undersöks fördelar och tekniskautmaningar relaterade till hydrobatik som tillämpas på undervattensrobotar, vanligenkallade autonoma undervattensfarkoster (AUV). Avhandlingen bidrar till ny kunskapi simulering, reglering samt tillämpning i experiment av dessa robotar genom enstrukturerad metod för att realisera hydrobatisk förmåga i realistiska scenarier.

Tre nyttoområden beaktas - miljöövervakning, havsproduktion och säkerhet. In-om dessa nyttoområden har ett antal scenarios identifierats där en kombination avsmidighet i manövrerbarhet samt effektivitet i prestanda är avgörande för robotensförmåga att utföra sin uppgift. För att åstadkomma detta måste två viktiga tekniskautmaningar lösas. För det första har dessa AUVer färre styrytor/trustrar än frihetsgra-der, vilket leder till utmaningen med underaktuering. Utmaningen beskrivs i detaljoch lösningsstrategier som använder optimal kontroll och modellprediktiv kontrollbelyses. För det andra är flödet runt en AUV som genomför hydrobatiska manövrarkomplext med övergång från laminär till stark turbulent flöde vid höga anfallsvinklar.Detta gör flygdynamikmodellering svår. En full 0-360 graders flygdynamikmodellhärleds därför, vilken kombinerar en multi-tillförlitlighets hydrodynamisk databasmed en generaliserad strategi för komponentvis-superpositionering av laster. Det-ta möjliggör prediktering av hydrobatiska manövrar som t.ex. utförande av looping,roll, spiraler och väldigt snäva svängradier i realtids- eller nära realtids-simuleringar.

I nästa steg presenteras ett cyber-fysikaliskt system (CPS) – där funktionalitetsom härrör från simuleringar kan överföras till de verkliga användningsområdena påett effektivt och säkert sätt. Simulatormiljön är nära integrerad i robot-miljön, vilketmöjliggör förvalidering av reglerstrategier och mjukvara innan hårdvaruimplemen-tering. En egenutvecklad hydrobatisk AUV (SAM) används som testplattform. CPS-konceptet valideras med hjälp av SAM i ett realistiskt sceanrio genom att utföra ettsökuppdrag av ett nedsänkt föremål under fältförhållanden.

Resultaten av arbetet i denna licentiatavhandling kommer att användas i den fort-satta forskningen som fokuserar på att ytterligare undersöka och utveckla ny me-todik för reglering av underaktuerade AUVer. Detta inkluderar utveckling av real-tidskapabla ickelinjära MPC-implementeringar som körs ombord, samt AI-baseradereglerstrategier genom ruttplaneringsåterkoppling, autonom systemidentifiering ochförstärkningsinlärning. Sådan utveckling kommer att tillämpas för att visa nya möj-ligheter inom de tre nyttoområdena.

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Nyckelord: Autonoma Undervattensfarkoster (AUV), Modellering, Simulering,Reglerteknik, Fältprovning, Cyber-fysikaliska System(CPS).

Preface

The project on hydrobatics is part of the SwedishMaritimeRobotics Center (SMaRC),and is funded by the Swedish Foundation for Strategic Research / Stiftelsen för Strate-gisk Forskning (SSF).

I would like to thank my principal supervisor Assoc. Prof. Ivan Stenius for hisinvaluable support, guidance and camaraderie throughout the course of the PhD. Ihope this collaboration leads us to even more exciting research and exploration. Iwould like to thank co-supervisor Prof. Dimos Dimarogonas for his guidance oncontrol theory and research directions, and I wish to deepen this collaboration dur-ing my upcoming work on topics in underactuated control.

Colleagues working on the SAM project at SMaRC have been extraordinary intheir support and tenacity, and it has been a privilege to work with Josefine Sever-holt, Carl Ljung, Anton Svensson, Nils Bore, Ignacio Torroba, Özer Özkahraman,Christopher Sprague and Yiping Xie on the field tests in sun, rain, wind, snow andice! I want to acknowledge the excellent contributions of Tianlei Miao in his master’sthesis which provided valuable input for one of the papers included in this work. Ithas also been an enriching experience to collaborate with other SMaRC colleagues atKTH including Clemens Deutsch, Elias Strandell Erstorp, Viktor Lidström, Sebas-tian Thune, Filip Söderling, Lazaro Moratelli, Niklas Rolleberg, Prof. Peter Sigray,Prof. Jakob Kuttenkeuler, Assoc. Prof. John Folkesson and Prof. Petter Ögrenamong others. Thanks also to Joana Fonseca, for our collaboration in teaching.

I would like to thank other partners at SMaRC for providing valuable opportuni-ties for discussion, workshops and experiments. These include Prof. Anna Wåhlinfrom the University of Gothenburg (for valuable discussions on environmental sens-ing, guidance on the Hugin AUV, and opportunities for field tests), Prof. Nina Kirch-ner from Stockholm University (for ASIAQ, and field tests at Askö), Prof. Fred-erik Gröndahl, Jean-Baptiste Thomas (for access and insight into algae farms) andProf. Magnus Burman (for ASIAQ) from KTH, and the organizations MMT Swe-

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den, Saab, FMV, FOI and WASP (for collaboration, technical discussions, access totest sites, workshops and joint tests).

A word of gratitude also to friends, family and well-wishers spread all overthe world (including but not limited to India, Singapore, Sweden, Germany, USA,France, Japan, Lebanon, China ...), with whom I have shared several memorable ex-periences. I do not want to mention names here, since that would inevitably meanI would miss someone out. Most importantly, I want to thank my parents for theirincessant support and love, and I would not be at this stage without them.

Whenever I faced challenges through the course of my research, I have been in-spired and motivated by reading works by Isaac Asimov, Haruki Murakami, MilanKundera, Anna Akhmatova and Percy Bysshe Shelley, listening to enthralling piecesby the Late-Romantic and Modern composers Dmitri Shostakovich, Gustav Mahler,Pyotr Tchaikovsky and Igor Stravinsky; as well as following superlative sporting per-formances by Novak Djokovic, Rafael Nadal, Roger Federer, and the cricket teamsof India and the West Indies. I would not have had the fortitude to come this far if itwere not for these stalwarts.

My apologies in case I have not mentioned someone here, it is not intentional.Last, I hope the reader learns something new after reading the content in this thesis.

With warm regards,

Sriharsha Bhat,November 2020,Stockholm, Sweden.

Appended Papers andAuthor Contributions

This thesis is a compilation of the following appended papers, which includes thedescribed author contributions.

Paper AS. Bhat and I. Stenius, "Hydrobatics: A Review of Trends, Challenges and Opportu-nities for Efficient and Agile Underactuated AUVs," 2018 IEEE/OES AutonomousUnderwater Vehicle Symposium (AUV), Porto, Portugal, 2018, pp. 1-8.

S. Bhat performed the study, authored the publication and presented the work atthe conference. I. Stenius contributed with supervision, detailed discussions on themethod, highlighting key contributions, writing and reviewing the text.

Paper BS. Bhat, I. Stenius and T. Miao, "Real-time flight simulation of hydrobatic AUVsover the full 0-360 degree envelope", Manuscript submitted to the IEEE Journal ofOceanic Engineering, 2020, pp.1-17.

S. Bhat wrote the paper, devised the method together with I.Stenius, generatedresults and discussed them. I. Stenius had the initial idea, supervised the work, wasclosely involved in discussions, added figures and text, and established the methodtogether with S. Bhat. T. Miao performed studies in hydrodynamics that providedresults for the paper.

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Paper CS. Bhat, I. Stenius, N. Bore, J. Severholt, C. Ljung and I. Torroba Balmori, "Towardsa Cyber-Physical System for Hydrobatic AUVs," OCEANS 2019 - Marseille, Mar-seille, France, 2019, pp. 1-7.

S. Bhat wrote and presented the paper, was one of the initiators of the idea to-gether with I. Stenius and N. Bore, developed the control system and compiled theresults. I. Stenius supervised the work, initiated the idea, and supported with hard-ware and software development as well as experiments, writing and review. N. Borewas responsible for a majority of the software development. J. Severholt and C.Ljung were responsible for hardware development and I. Torroba contributed withsimulation results and localization modules.

Paper DS. Bhat, I. Torroba, Ö. Özkahraman, N.Bore, C.Sprague, Y.Xie, I. Stenius, J. Sever-holt, C. Ljung, J.Folkesson and P. Ögren, "A Cyber-Physical System for HydrobaticAUVs: System Integration and Field Demonstration" 2020 IEEE/OES AutonomousUnderwater Vehicle Symposium (AUV), St.Johns, Newfoundland, Canada 2020, pp.1-8.

S. Bhat took a leading role in writing and presenting the paper, developed thecontrol system and was closely involved with describing the method and generatingthe results. I. Stenius supervised the work, together with J. Folkesson and P. Ögren.Ö. Özkahraman developed the mission planning and execution software, I. Torrobathe localization module, C. Sprague the path planner and Y. Xie the target detectionsoftware. N. Bore developed the simulation environment, while J. Severholt and C.Ljung developed the hardware systems. All authors were closely involved with fieldexperiments that were presented in the paper. Ö. Özkahraman and I. Torroba wereclosely involved with writing and editing the text.

Contents

1 Introduction 11.1 Impact areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2 Motivating hydrobatics . . . . . . . . . . . . . . . . . . . . . . . . 51.3 Research question and contributions . . . . . . . . . . . . . . . . . 51.4 For the interested reader . . . . . . . . . . . . . . . . . . . . . . . 7

2 Underactuation 92.1 The challenge of underactuation . . . . . . . . . . . . . . . . . . . 92.2 An underactuated hydrobatic AUV . . . . . . . . . . . . . . . . . . 102.3 Addressing the challenge . . . . . . . . . . . . . . . . . . . . . . . 122.4 For the interested reader . . . . . . . . . . . . . . . . . . . . . . . 15

3 Flight Dynamics 163.1 Component buildup workflow . . . . . . . . . . . . . . . . . . . . 173.2 Multi-fidelity look-up tables (LUTs) . . . . . . . . . . . . . . . . . 193.3 Results of hydrobatics simulations . . . . . . . . . . . . . . . . . . 203.4 For the interested reader . . . . . . . . . . . . . . . . . . . . . . . 22

4 Cyber-physical Systems 264.1 Controller validation with Simulink . . . . . . . . . . . . . . . . . 274.2 Software validation with Stonefish . . . . . . . . . . . . . . . . . . 284.3 Results of field tests . . . . . . . . . . . . . . . . . . . . . . . . . . 294.4 For the interested reader . . . . . . . . . . . . . . . . . . . . . . . 30

5 Conclusions and outlook 325.1 Real-time nonlinear MPC . . . . . . . . . . . . . . . . . . . . . . . 335.2 Intelligent control and motion planning . . . . . . . . . . . . . . . . 345.3 New scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Bibliography 37

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List of Figures

1.1 The big picture: linkages of applications in underwater robotics toUN Sustainable Development Goals (pictograms from [4]). . . . . . 2

1.2 Envisioned examples of use case scenarios and impact areas (cour-tesy: top left Fredrik Gröndahl, top middle: Nina Kirchner, and topright Saab AB.). . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.3 AUVperformance trade-offs: Flight andHover style vehicles. (AUVscourtesy Saab AB). . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.4 Linkages between contributions made in the thesis. . . . . . . . . . 71.5 The SAM AUV developed at KTH as part of SMaRC, is a hydrobatic

research platform. . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.1 The hardware subsystems on SAM: 1) Battery pack; 2) LongitudinalCenter of Gravity (LCG) trim; 3) Variable Buoyancy System (VBS);4) Transversal center of gravity (TCG) trim; 5) Thrust vectoring andpropulsion (picture courtesy Josefine Severholt). . . . . . . . . . . . 11

2.2 An illustration of a proposed underactuated control framework in-cluding a model predictive control, motion planning, system identi-fication and supervisory control. . . . . . . . . . . . . . . . . . . . 14

3.1 Flow separation around a slender body at high angles of attack.Symmetric vortex shedding is seen here, which would transition toasymmetric vortices and turbulent wakes at higher angles (from [40],reprinted in [41], used with permission from Elsevier). . . . . . . . 17

3.2 A real-time AUV simulator can reflect real world-flight dynamicsbehavior and subsystems for generating hydrobatic maneuvers andtuning control strategies. Here, the SAM AUV is depicted, both inthe simulation environment of the hydrobatics simulator and in re-ality during field tests. Actuator subsystems on the AUV such as theLCG and VBS are also modeled in simulation [15]. . . . . . . . . . 18

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LIST OF FIGURES xi

3.3 A schematic illustration of look-up tables capturing the nonlinearhydrodynamic forces for each sub-component over the full envelope.Thedynamics of all components are combined to obtain the full AUV dy-namics model [15]. . . . . . . . . . . . . . . . . . . . . . . . . . . 19

3.4 The simulation framework in Simulink including component modelswith lookup tables, actuator models, sensors, state estimators, linksto external systems, and plots. On the right example lookup tablesover the full envelope for the lift and drag coefficients of the hull arepresented. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.5 Trajectory of a simulated tight looping maneuver (unit: m). Themaneuver takes the AUV over 0− 360◦ in pitch [15]. . . . . . . . . 22

3.6 Trajectory of a turbo-turnmaneuver (unit: m). The AUVgoes through0− 360◦ in yaw [15]. . . . . . . . . . . . . . . . . . . . . . . . . . 22

3.7 Trajectory of a helix maneuver (unit: m). Such a maneuver can en-able inspection of targets in confined spaces [15]. . . . . . . . . . . 23

3.8 Trajectory of a stationary LCG sweep maneuver (unit: m). The ma-neuver enables static pitch control [15]. . . . . . . . . . . . . . . . 23

3.9 Comparison of yaw angle and rate for a turbo-turn maneuver be-tween a simulation run(red) and field test(blue). Qualitiatively sim-ilar behavior is seen between simulation and reality [15]. . . . . . . 24

3.10 The behavior of the AUV in field tests during the Turbo-Turn maneu-ver in (a) is qualitatively similar to the simulated motion in (b). Thisenables the use of the simulator as a development tool, as maneuversand control strategies can be pre-tested in simulation [15]. . . . . . 25

4.1 The SAM cyber-physical system architecture integrating a user inter-face for mission planning, control, autonomy and localization soft-ware, AUV hardware, and simulation tools. Two simulation environ-ments are integrated – the Stonefish simulator to rehearse missionsand integrate software, and the hydrobatics simulator to simulateadvanced maneuvering [17]. . . . . . . . . . . . . . . . . . . . . . 27

4.2 Controllers can be evaluated first in simulation models in Simulink,and later deployed to the ROS environment for system integrationand hardware deployment. . . . . . . . . . . . . . . . . . . . . . . 28

4.3 Mission rehearsal with multiple SAM AUVs in the Stonefish simula-tion environment (left) and in the Neptus mission planning interface(right). SAM-1 is executing a mission while SAM-2 follows SAM-1.A sunken target (car) can be seen in the background [17]. . . . . . . 29

xii LIST OF FIGURES

4.4 The spiral search mission in Askö in which the car is found. Theplotted points are from the localization module. Colour indicatestime, blue(start) to red(end). The red marker shows where the caris, known from a prior multibeam echosounder survey. Green linesshow the planned path. Green marker shows the estimated locationof the car from the on-board localization unit [17]. . . . . . . . . . 30

4.5 The trajectory of SAM in 3D (blue to red gradient in time). Thedesired waypoints are shown in green. Note the different scales ofthe horizontal and vertical axes.The AUV could hold depth with anaccuracy of 0.5m [17]. . . . . . . . . . . . . . . . . . . . . . . . . 31

5.1 Future ideas in intelligent control for underactuated robotics. . . . . 355.2 Photographs of new scenarios: kelp farms (top, picture courtesy

KosterAlg) and calving glacier fronts (bottom, picture courtesy EliasStrandell Erstorp/Sebastian Thune). . . . . . . . . . . . . . . . . . 36

List of Tables

2.1 Overlapping control between subsystems on SAM [16]. . . . . . . . 12

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Chapter 1

Introduction

Our society can perhaps turn to the oceans to find answers to grand challenges such asclimate change, and the need for sustainable development and security. Oceans coverthe majority of the earth’s surface and are key to life – they contribute significantlyto biodiversity and oxygen generation, and absorb 50% of globalCO2 emissions [1].They have been intricately linked to human society throughout history, but are stilllargely unexplored and unknown. Exploring the world below water may yet holdthe answer to deep questions about the origin of life and our past and future as aspecies. Technological advances in autonomous underwater vehicles (AUVs) canspark a revolution in exploring and understanding our oceans by rendering access toand observation of areas previously considered inaccessible. The next generation ofunderwater robots may revolutionize our view of our world with new perspectivesand information; just like how satellites and space exploration have impacted earthand planetary observation since the 1970s. Hydrobatic AUVs can be part of thisrevolution [2, 3].

The term hydrobatics stems from aerobatics, and refers to agile maneuvering ofunderwater vehicles. Hydrobatic capabilities can enable autonomous underwater ve-hicles (AUVs) to be efficient in range and speed, as well as agile in maneuvering,thereby opening doors to disruptive designs and impactful new use case scenarios.The upcoming chapters will throw light on the societal benefits of hydrobatic AUVsand describe the challenges in generating elegant control strategies for hydrobatics.Novel ways of efficiently simulating such robots and translating simulations to real-world scenarios will be presented, and ideas for the future will be introduced.

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2 CHAPTER 1. INTRODUCTION

Figure 1.1: The big picture: linkages of applications in underwater robotics to UNSustainable Development Goals (pictograms from [4]).

1.1 Impact areasCurrent advances in AUVs are driven by the need for long-term underwater presencewith reduced human intervention and the capability to operate in unknown environ-ments. From the results of such research, AUVs will allow us to explore unknownenvironments and gather valuable data to help answer challenging questions on cli-mate and society that affect humanity as a whole. AUVs can prove to be useful toolsin turning the oceans into a sustainable resource for food and energy. AUVs cansafeguard society through challenging missions to track intruders and identify tar-gets far beyond the capabilities of human pilots and divers. Underwater robots canimpact UN Sustainable Development Goals [4] to different extents, as described inFigure 1.1. There is a direct link to G13: Climate Action and G14: Life BelowWater since AUVs can be used as sensor platforms to gather data on environmentalparameters. The use of AUVs can enable sustainable use of the oceans, and accel-erate other technologies such as renewable energy and biomaterials, linking to G6:Clean Water and Sanitation, G7: Affordable and Clean Energy, G12: Respon-sible Consumption and Production. Furthermore, the development of cutting-edgeAUV technologies and applications is closely linked to G9: Industry, Innovationand Infrastructure. In this context therefore, hydrobatics can be an enabler in keyimpact areas including environmental sensing, ocean production and security (seeFigure 1.2).

CHAPTER 1. INTRODUCTION 3

Figure 1.2: Envisioned examples of use case scenarios and impact areas (courtesy:top left Fredrik Gröndahl, top middle: Nina Kirchner, and top right Saab AB.).

Considering environmental sensing, AUVs can be a powerful tool for gather-ing data for health monitoring of the oceans. AUVs could be used to explore re-gions underneath the polar ice shelves, providing critical environmental measure-ment and images that would enable us to better understand phenomena such as cli-mate change, microplastic effects or acidification. Operations would take place inlargely unknown environments with great uncertainty and potentially extreme condi-


tions [5]. An AUVmay be deployed and recovered from a research vessel or throughan under-ice docking mechanism (e.g. [6]). During the mission, the vehicle wouldnavigate autonomously, detect andmap interesting features and generate high resolu-tion environmental data throughout the water column. With hydrobatic capabilities,it would be possible to aim sensors in any direction for high spatial resolutions andpanoramic images (e.g. [7]). A successful AUV in this scenario would have to beagile and maneuverable to avoid obstacles and disturbances in launch, recovery anddata-gathering/inspection; while being efficient and robust for long range transit andnavigation in a hostile environment.

The concept of blue growth refers to treating the oceans as a sustainable resource,and ocean production with seaweed/algae farming is one example [8]. Differentalgal species have the potential to be farmed for food, raw materials, energy and nu-trients. Algae farms will range from a few hectares to several square kilometres inarea, and teams of resident AUVs (such as [9]) can be used as valuable tools to in-spect the site, monitor crop health, and support licensing and harvesting operations.These AUVs will perceive and ’see’ the health of the crop by aiming environmen-tal sensors, and fuse sensor information to efficiently navigate through an obstaclerich environment by maneuvering between moorings, cables and plants. The AUVswould cooperate with each other, as well as with human divers to perform missionsfor inspection, monitoring and harvesting. Being resident at the farm, the AUVs willdock with seafloor stations to recharge, exchange data and receive new instructions[10, 11]. Hydrobatics becomes relevant here since docking, precision maneuvering,obstacle avoidance and sensor aiming capabilities are crucial for successful opera-tions.

Focusing on security, AUVs can offer forcemultiplier capabilities in target track-ing, mine-hunting, anti-submarine operations and reconnaissance. Agile AUVs couldcooperate with submarines and surface vessels in detecting and encircling targets, orperform reconnaissance surveys. Mobile docking for launch and recovery (e.g. fromsubmarines or larger AUVs) becomes a critical performance [12, 13]. Furthermore,AUVs will be perceptive and intelligent to detect and identify dangerous objects (e.g.mines) and intruders. Multiple AUVs could cooperate with other agents to hunt andencircle targets and reconnoitre unknown environments [14]. Such scenarios placehigh demands on autonomy, cooperation and maneuverability; where efficiency andagility are both a requirement. This means hydrobatic capabilities can offer benefi-cial performance characteristics.


1.2 Motivating hydrobaticsConsidering the impact areas presented, docking, perception in unknown environ-ments and swarms for cooperative autonomy become force multipliers to enhanceAUV operational capabilities. It is clear that hydrobatics can play a key role as anenabler of such operations and is closely linked with these technology trends.

Robotic operations underwater have unique requirements in comparison to land,air and space; including a very dynamic environment and limited communicationavailability meaning autonomy, endurance and perception are crucial. These op-erational challenges necessitate design prioritization based on mission critical per-formance. Slender, streamlined and underactuated flight-style AUVs (optimized forefficiency and speed) are usually utilized for long range missions, but these lackthe capability to perform advanced precision maneuvers. On the other hand, fully orover-actuated hover style AUVs are used if inspection or manipulation is considered,since agility in six degrees-of-freedom (DOF) is prioritized at the cost of speed andrange. There are performance gaps in both flight style and hover style AUVs (asillustrated in Fig. 1.3), and bridging this gap can be an enabler of entirely new capa-bilities.

Specifically, hydrobatic capabilities mean that the robots can be efficient overlong transits, while being agile in critical situations to achieve the objective. Thismeans that a traditional target conflict can be resolved – leading to disruptive designsand use cases!

1.3 Research question and contributionsGiven themotivations and impact areas described, a key research question is therefore-How can we push the boundaries of underactuated AUVs to make them more agilein 6DOF and therefore capable of hydrobatic maneuvers while still being efficientin speed and range?

Exploring this research question and turning the idea of hydrobatics into reality isthe focus of this work. Crucial technical challenges in underactuation and flight dy-namics are identified and ideas are proposed to address them. To translate these ideasto real-world scenarios, a framework is presented to evaluate new control strategiesin simulation environments, and to then deploy these strategies to hardware in fieldoperating conditions. The following contributions are thus made:

1. Paper A: A literature study is performed on trends, challenges and opportu-nities related to hydrobatic AUVs [2].


Figure 1.3: AUV performance trade-offs: Flight and Hover style vehicles. (AUVscourtesy Saab AB).

2. Paper B: An efficient means of simulating hydrobatic maneuvers in real timeover the full 0− 360◦ flight envelope is presented [15].

3. Paper C: A cyber-physical system concept to transform the control strategiesand autonomy packages developed in simulation to real hardware [16] is in-troduced.

4. Paper D: The cyber-physical system is demonstrated in a real-world inspec-tion scenario [17]

The linkage and flow between the different papers is presented in Figure 1.4.

There is also a certain urgency in developing this next generation of AUVs andtransforming ideas to demonstrations. Based on current evidence, we are runningout of time before the climate and environment are irrevocably altered [18]; and thusthe sooner new ideas are realized, the better chance of them being industrialized tohave a positive impact on these challenges. This motivates a large portion of thiswork to aim towards real-world demonstrations. The real-world AUV on which theideas are tested is the hydrobatic SAM AUV (see Figure 1.5) developed at KTH (aspart of the Swedish Maritime Robotics Center (SMaRC)).


Figure 1.4: Linkages between contributions made in the thesis.

1.4 For the interested readerThe impact areas in environmental sensing, ocean production and security are fur-ther described in Paper A. Technology trends in docking, perception and swarmsare highlighted in detail with references to recent literature in AUV research in thatpaper. Further information on use case scenarios is available in [19].


Figure 1.5: The SAM AUV developed at KTH as part of SMaRC, is a hydrobaticresearch platform.

Chapter 2

Underactuation

Underactuation is a very exciting and yet challenging aspect of hydrobatics. Hy-drobatic capabilities require controllability in unstable flow conditions with fewercontrol inputs than possible degrees of freedom. The complexity of the same neces-sitates several innovative techniques to perform and exploit hydrobatic maneuverswith minimum effort. The focus here is to describe and define the challenge, todemarcate its boundaries, and to suggest solution strategies.

2.1 The challenge of underactuationFollowing Newton’s second law of motion, the dynamics of an underwater robot aretypically represented by a second order ordinary differential equation as

ẍ = f(x, ẋ, u, t) . (2.1)In (2.1), x is the state vector describing positions, u is the vector containing all

control inputs and t is the time variable [20]. The dynamics of the system in (2.1),can usually be divided into a passive space f1 and a controllable space f2

ẍ = f1(x, ẋ, t) + f2(x, ẋ, t)u . (2.2)It can be seen from (2.2) that the system is now linearly dependent on the control

inputs u. By definition, this system is fully actuated if the rank of f2 is equal to thedimension of the state vector x, i.e.

rank (f2(x, ẋ, t)) = dim(x) . (2.3)A dynamic system is defined as fully actuated from a control perspective if for

each degree of freedom there is a unique control input actuating that degree of free-dom. All the state variables in x are connected to control inputs in u for such a

9

10 CHAPTER 2. UNDERACTUATION

system. Note that this definition does not take into account saturation effects onthe controller inputs. Now, a dynamic system (on the form of (2.2)) is defined asunderactuated if the rank of f2 is less than the dimension of the state vector x, i.e.

rank (f2(x, ẋ, t)) < dim(x) . (2.4)

In this case a unique control input does not actuate each degree of freedom, andcontrol inputs from uwill be able to influence some accelerations of x, but not all. Aform of underactuation may also be given by a system that is fully actuated accordingto definition in (2.3), but has severe saturation limits on the possible control inputs.

An AUV is a 6 degree-of-freedom mechanical system - the state vector x has adimension of 6, while the rank of f2 can range from as low as 2 or 3 for a flight stylevehicle and up to 6 (or even more) for a hover style AUV or Remote Operated Ve-hicle (see Figure 1.3). Existing 6DOF hover-style robots are usually fully-actuated(and sometimes even over-actuated); with a number of thrusters enabling decoupledcontrol of the motion in specific degrees of freedom. This makes them control trace-able between inputs and motions, and a degree of linearization of system dynamicsis possible. While having fewer actuators would make the system less complex (andthereby more robust and efficient); controlling its behavior to achieve precise mo-tions is not trivial.

Coupling between terms and complex dynamics (f1 in (2.2)) makes it particu-larly challenging to trace motions to controls [21]. The control challenge increaseswhen hydrobatic maneuvers come into the picture, since requirements on position-ing, response time and accuracy become more demanding. It is important to predictthe system dynamics to enable advanced maneuvers of underactuated vehicles. Thenatural dynamics as well as the unstable regimes of the system can then be exploitedto obtain the desired motions. Following the natural dynamics of the system (ratherthan opposing them by brute force in a fully actuated robot) also enables energy effi-cient control, as typically very limited control inputs are required to gently steer thevehicle along a natural dynamic path of the system. Therefore, effective methodsfor underactuated system control and motion planning are necessary in order to fullyexploit the abilities of the available actuators.

2.2 An underactuated hydrobatic AUVThe key challenge that arises due to underactuation is to control agile 6DOF ma-neuvers with minimum actuators and control inputs. We consider the slender SAMAUV that is being developed at the Swedish Maritime Robotics Center as a casestudy to further explore the underactuated control problem. SAM, short for Smalland Affordable Maritime Robot, is designed to demonstrate hydrobatic capabilities,

CHAPTER 2. UNDERACTUATION 11

Figure 2.1: The hardware subsystems on SAM: 1) Battery pack; 2) LongitudinalCenter of Gravity (LCG) trim; 3) Variable Buoyancy System (VBS); 4) Transversalcenter of gravity (TCG) trim; 5) Thrust vectoring and propulsion (picture courtesyJosefine Severholt).

and has a unique actuator configuration to enable agile maneuvers [16, 17]. TheSAM AUV is around 1.5m long and weighs close to 15kg. SAM’s actuators includecounter-rotating propellers (in a duo-prop configuration), a thrust vectoring nozzle,a movable battery-pack for longitudinal changes in center of gravity (c.g.) position(LCG), rotating weights for transversal changes to c.g. position (TCG) and a variablebuoyancy subsystem (VBS) that can pumpwater in and out of a tank (see Figure 2.1).

It is possible to combine the efficiency of the duo-prop propulsion system and theslender form factor with heightened maneuverability (thanks to the trim and buoy-ancy subsystems) by utilizing these actuator subsystems in tandem. SAM can bemade to pitch up to±90◦ using the LCG system, hover in position using the VBS tomaintain its depth; or even roll while stationary using the TCG. Furthermore, newcontrol sequences for tight turns and loops can be utilized using the combinationbetween the counter-rotating propellers and thrust vectoring.

Controlling such a system is however not straightforward, since the subsystemsdo not independently influence specific degrees of freedom. The interactions andinfluences between the actuator subsystems and the respective degrees of freedomare presented in Table 2.1. Such an actuator configuration means that most degreesof freedom in SAM cannot be independently influenced, and thus require nuance in


Table 2.1: Overlapping control between subsystems on SAM [16].

Subsystem x y z roll pitch yawLCG ×VBS × ×TCG ×Thrust vectoring × × × ×Counter-rotating propellers × ×

control. For example, the depth z can be influenced by theVBS, and also by the thrustvectoring and propulsion. When the VBS is used, the pitch angle is also influenced,since there is a change in the moment balance. Such interactions mean that single-input single-output control strategies can have limitations, and motivate a modelbased multi-input multi-output (MIMO) control approach. Furthermore, some ofthe subsystems have saturation limits, which means that additional constraints needto be considered.

2.3 Addressing the challengeThe focus is to derive efficient underactuated control strategies to realize hydrobaticmaneuvers, and to maintain stability and controllability in the presence of uncertain-ties, disturbances and constraints.

A set of linear single-input single-output controllers (e.g. using proportional-integral-derivative (PID) control) can be used to track individual output to controlsat the simplest level, but this has obvious limitations due to couplings and nonlin-earities unless decoupled states are tracked. Cascading these linear controllers canaddress some of these nonlinear couplings and this is a common strategy to enablediving or steering control. Such strategies enable us to obtain a functional systemin a simple and straightforward manner, but these are not necessarily optimal. Thenatural dynamics of the system are not considered, constraints cannot be easily ad-dressed and the control objective cannot be customized.

A key point is thus to incorporate the dynamics of the system in the control ac-tions, enabling efficient controls. A model of the system’s dynamics of the form of(2.1) would be useful in deriving such control strategies. This way, relevant cou-plings could be exploited by the controller. Techniques in optimal control and non-linear model predictive control (MPC) can be useful to bring in such a nonlineardynamics model and customized objectives and constraints into the picture. Thesecan help in prioritizing efficiency, adaptability and robustness.


In the field of optimal control, the control problem (shown here in continuoustime) is reformulated as an optimization problem, with the goal of minimizing (ormaximizing) an objective function subject to a set of constraints as

min φ(xf ) +∫ tftif0(x, u)dt,

s.tẋ = d(x, u),

x ∈ X,u ∈ U,

(2.5)

where x represents the states, u represents the controls, φ(xf ) is the cost at the finalstate xf , ti and tf refer to the initial and final time, f0 is the objective function ateach time instant. The dynamics of the system are represented by a nonlinear ODEthrough the function d(x, u). The states and controls are constrained to setsX andU.The problem can be solved using a variety of analytical and numerical techniques,the most prominent being Dynamic Programming [22] or Pontryagrin’s MinimumPrinciple [23]. Direct numerical optimization methods such as Nonlinear Program-ming are also applicable [24].

A common objective term could be to minimize deviations from a setpoint.This objective can be augmented with additional terms to minimize effort, or min-imize time (or a combination of these criteria). The constraints include the sys-tem dynamics model (linear or nonlinear), initial (and final) states, limits on actua-tors/controls/states and customized safety limits. On solving the optimization prob-lem, a sequence of control inputs (u∗(t) ) and the respective optimal state trajectories(x∗(t)) are derived, and the optimal control can be applied to the system. However,the applied control is open-loop, and the effect of disturbances on the state are notconsidered. Disturbances can be accounted for if the optimal control u∗ is recalcu-lated for a pre-defined prediction horizon t+T at each time instant t (i.e. ti = t andtf = t + T in the optimal control problem above). In this case, we arrive at finitehorizon model predictive control (MPC).

The stability of the system can be guaranteed if a linearized system model isused at the last prediction step t+ T to check for infinite horizon convergence [25].Additionally, uncertainties can potentially be considered in the form of additionalconstraints, model parameters or controller augmentations (e.g. by constraining thesystem to a tube or funnel of reachable states through an additional controller thatpropogates the effect of uncertainties [26, 27, 28]). The model can be updated basedon measured response from the environment [29] – this can lead us to adaptive con-trol, or even dual control where themodel is identified and simultaneously controlled.


Figure 2.2: An illustration of a proposed underactuated control framework includinga model predictive control, motion planning, system identification and supervisorycontrol.

Different control objectives can be considered and updated from a motion planneror supervisory controller, thereby allowing for different behaviors and actions. It istherefore crucial to derive a flexible, light and real-time model predictive controllerthat is capable of running on target hardware (e.g. [30]), so that a foundation for fur-ther augmentations can be established. This is the focus of current research. Oncesuch strategies are attempted in the field or in simulation environments to generatetraining data-sets, techniques in reinforcement learning could also be used to aug-ment the control policies, enabling model-free adaptive control [31, 32, 33]. Theseideas can be brought together in a framework such as that presented in Figure 2.2.

While techniques in nonlinear model predictive control have shown promise withtheoretical and simulation results (especially in autonomus driving, aircraft flightcontrol and chemical process control), there are very few implementations on AUVsin field conditions (such as e.g. [30]). Ideas in feedback motion planning have beendemonstrated for flying [34] and grasping [35], but not yet underwater, where themeasurement uncertainty is significantly higher. Reinforcement learning, as well asother combinations of learning and control are currently gaining interest, and thereis therefore motivation to apply such combinations to hydrobatics. Therefore, a pri-ority is to derive elegant strategies for underactuated control using these techniquesin challenging environments. Solutions found for the underactuated control problemin hydrobatics can also then be potentially translated to other fields, especially forflight control, manipulation and biomimetics.

We will be able to apply new techniques to perform very elegant maneuvers withthe help of underactuated control strategies. To do so, however, better understandingand modeling of the AUV’s flight dynamics is crucial.


2.4 For the interested readerThe challenge of underactuation is described in detail in Paper A, and literaturefrom underactuated robotics is presented suggesting means to solve the problem.The SAM AUV is presented in greater detail in Papers C and D, together withimplementations of linear and cascaded controllers.

Chapter 3

Flight Dynamics

An AUV can enter high angles of attack during agile hydrobatic maneuvers, whichcan lead to unsteady, transitional and turbulent flow conditions around the body (seeFigure 3.1). Such conditions go far beyond the usual well described and understoodflow region before stall – this means modeling the flight physics becomes an ex-tremely complex task. Very few papers in the AUV literature attack this challenge[36, 37] and questions remain open. Most publications on AUV simulation modelsfocus on generalized frameworks and predictions at low angles of attack [38, 39],since most current AUV applications do not face such flow conditions except in ex-treme scenarios (such as heavy cross-flow). Modeling over the full envelope is verydifficult to perform in real time since most solution strategies are either not accurateenough, or not applicable in real-time.

Focusing on this challenge however, offers new opportunities. The ability tosimulate hydrobatic AUVs in real-time enables us to further explore their capabil-ities in the specific use case scenarios described previously. Accurate and efficientsimulation models can be used to prototype new hydrobatic maneuvers and to derivenew underactuated control strategies (based on different actuator configurations) atlow risk and cost (see Figure 3.2). Furthermore, having efficient prediction modelsenables the use of optimal and model predictive controllers for the scenarios con-sidered, and improves the efficacy of the control strategies developed. Inspirationto derive such models is garnered from the aerospace domain, where analytical andsemi-empirical (ASE) high angle-of-attackmodels have been used for simulating air-craft, missiles and spacecraft in critical scenarios. Computational techniques suchas computational fluid dynamics (CFD) offer increasingly accurate flow predictionsalbeit at a high computational cost [42]. If data from field tests are available, sys-tem identification techniques may enable us to learn the dynamics from the data[43]. These varied techniques are threaded together to generate a nonlinear simula-

16

CHAPTER 3. FLIGHT DYNAMICS 17

Figure 3.1: Flow separation around a slender body at high angles of attack. Symmet-ric vortex shedding is seen here, which would transition to asymmetric vortices andturbulent wakes at higher angles (from [40], reprinted in [41], used with permissionfrom Elsevier).

tion model that is capable of real-time simulations over the full 0 − 360◦ angle ofattack envelope [15]. Two main innovations have been made to enable fast and effi-cient simulations without sacrificing accuracy – a generalized component build-upworkflow (to improve efficiency) and a multi-fidelity lookup table (LUT) concept (toimprove accuracy).

3.1 Component buildup workflowA key idea is to treat individual components of the AUV (including the hull, wings,fins, actuators etc.) separately with their own dynamic properties (e.g. mass, inertia,damping) and to combine them in the full simulation model. This enables easy as-sembly of simulation models for complex systems, and through the composition ofsimple sub-components allows for very fast simulations. Such a component build-upapproach is straightforward, general and flexible; and enables almost any robot to besimulated if the dynamics are well defined. Such flexibility is desirable if differentrobots and actuator configurations need to be tested. Using this approach, differ-ent models of varying fidelity levels can be combined and superposed. However, atrade-off is that interaction effects between components (such as shadowing of con-trol surfaces) are difficult to capture in such an approach.

18 CHAPTER 3. FLIGHT DYNAMICS

Figure 3.2: A real-time AUV simulator can reflect real world-flight dynamics behav-ior and subsystems for generating hydrobatic maneuvers and tuning control strate-gies. Here, the SAM AUV is depicted, both in the simulation environment of thehydrobatics simulator and in reality during field tests. Actuator subsystems on theAUV such as the LCG and VBS are also modeled in simulation [15].


Figure 3.3: A schematic illustration of look-up tables capturing the nonlinear hydro-dynamic forces for each sub-component over the full envelope.The dynamics of allcomponents are combined to obtain the full AUV dynamics model [15].

3.2 Multi-fidelity look-up tables (LUTs)The component buildup approach offers speed, but its accuracy depends on the in-dividual component models. Therefore, look-up tables (LUTs) are used to representthe hydrodynamic forces and moments due to the different AUV components (seeFigure 3.3). These LUTs are populated using the best available data for the compo-nents over the full 0 − 360◦ angle of attack envelope and over an operating rangeof velocities (Reynolds’ numbers). A multi-fidelity paradigm is used to populate thelookup tables. High fidelity data might be available readily at low angles of attack(e.g. for a wing in laminar flow), while the available data might be less accurate athigh incidence angles (e.g for a slender body post-stall). The best available datasetsat each flight condition are assembled to obtain a full envelope database and capturenonlinear dynamic behavior qualitatively.

To compose such a multi-fidelity database, any appropriate dataset may be used.Here, a variety of input sources are exemplified including:

1. Jorgensen’s analytical formulation for slender bodies at high angles of attack,which was originally intended for rockets and spacecraft [44];

2. DATCOM, a comprehensive compendium of aerodynamic calculation meth-ods for fighter aircraft; which also includes methods for slender bodies at highangles of attack [45];

3. Computational Fluid Dynamics (CFD) calculations [46]; and


4. Analytical and numerical methods for airfoils and wings including lifting linetheory and solvers such as XFOIL [47].

These methods have different fidelity levels and can be combined to ensure thatthe best available data is used to capture the full flight envelope. Rapid methods suchas DATCOM and lifting line theory can be used to populate the database over theentire operating region, while slower but higher fidelity methods such as CFD canbe used to provide more accurate snapshots in critical flow regions. Furthermore, ifdata from experiments from field tests are available, such data can also be importedto the database. It is to be noted that the different methods need to be dimensional-ized appropriately when applied to AUVs.

Nonlinear behavior and post-stall effects can be captured with these LUTs. TheLUTs are pre-computed before the simulation is run. During the simulation, theforces are interpolated from the LUTs at each flight condition (Reynolds’ number,angle of attack) of the AUV. System identification techniques may be used to learnnew dynamics models to improve the LUTs in areas of low accuracy.

3.3 Results of hydrobatics simulationsThe two ideas presented are combined using software from Mathworks 1 (Matlaband Simulink) for real-time flight simulation of hydrobatic maneuvers [15]. TheSAM AUV introduced previously (with the actuator configuration presented in Fig-ure 2.1) is used as a case study. The hydrodynamic LUTs are pre-computed fromthe computer aided design (CAD) geometries of the different components, and thecomponents and actuator models are assembled in Simulink (Figure 3.4).

Simulations are performed by time-stepping the assembled dynamics model us-ing a Runge-Kutta solver, and different actuator inputs are tested to obtain hydrobaticmaneuvers (unit quaternions are used to represent orientations so that singularitiesdue to Euler angles at 90◦ pitch angle can be avoided). Studied hydrobatic maneuversinclude :

1. Tight looping, where the AUV performs a vertical flip by asynchronouslycycling the propellers and thrust-vectoring system through maximum values(Figure 3.5);

2. Turbo-turning, where the AUV turns with a very small turning radius, using asimilar sequence as above (Figure 3.6);

1https://se.mathworks.com/


Figure 3.4: The simulation framework in Simulink including component models withlookup tables, actuator models, sensors, state estimators, links to external systems,and plots. On the right example lookup tables over the full envelope for the lift anddrag coefficients of the hull are presented.

3. Helical inspection, where the AUV follows a helical trajectory in a confinedenvironment (Figure 3.7); and

4. Stationary LCG sweep, where the AUV pitches up to ±90◦ using only theLongitudinal Center of Gravity subsystem thus enabling inspections withoutthe use of the propellers (Figure 3.8).

Each of these maneuvers can be rapidly simulated, and the aim is to use the con-trol sequences developed in the actual AUV. These real-time flight simulations canshow great value in designing intelligent controllers.

The turbo-turn sequence has also been verified in the field, and the simulationmodel has shown qualitatively similar behavior to real world tests (see Figures 3.9and 3.10). Such maneuvers can be used in real-world scenarios – for example theturbo-turn can enable maneuvering and inspections in very confined areas, while theLCG sweep can make the AUV extremely agile and enable sensor aiming at specifictargets.

To further augment the simulation models used, it can be beneficial to learn fromthe response of the real system in field tests, and thereby update the dynamics pa-rameters and LUTs.


Figure 3.5: Trajectory of a simulated tight looping maneuver (unit: m). The maneu-ver takes the AUV over 0− 360◦ in pitch [15].

Figure 3.6: Trajectory of a turbo-turn maneuver (unit: m). The AUV goes through0− 360◦ in yaw [15].

3.4 For the interested readerA closer look at the equations involved can help the reader appreciate the nuancesinvolved inmodeling hydrobatic AUVs. The dynamics of anAUVcan be represented


Figure 3.7: Trajectory of a helix maneuver (unit: m). Such a maneuver can enableinspection of targets in confined spaces [15].

Figure 3.8: Trajectory of a stationary LCG sweepmaneuver (unit: m). The maneuverenables static pitch control [15].


Figure 3.9: Comparison of yaw angle and rate for a turbo-turn maneuver betweena simulation run(red) and field test(blue). Qualitiatively similar behavior is seenbetween simulation and reality [15].

in a vectorial form [38] as

MRBν̇ + CRB(ν)ν +MAν̇ + CA(ν)ν+

D(ν)ν + g(η) = τC , (3.1)

where η refers to the position vector, ν is the velocity vector, (Mν̇ + CRBν) cap-tures the forces due to rigid body dynamics with the mass and inertia matrix Mand the matrix containing Coriolis and centripetal forces CRB . g(η) representsthe hydrostatic terms while (MAν̇ + CAν +D(ν)ν) captures the hydrodynamicforces due to added mass (MA, CA) and damping (D(ν)). Populating these ma-trices, and performing time integration of these equations of motion enables flightsimulation. While computing the rigid body and hydrostatic terms is straightfor-ward, there is a great deal of complexity and nuance in representing the hydrody-namic terms. For most AUVs at low angles of attack, constant values are used forthe coefficients in these matrices since they do not change sharply over the operatingregion. However, the nonlinear hydrodynamic behavior needs to be captured in thecase of hydrobatic AUVs, and LUTs can be beneficial. Detailed information on themethods, validation and maneuvering results from real-time flight simulation overthe full envelope can be found in Paper B .


(a) Right Turbo-Turn, Top View, Experiment

(b) Right Turbo-Turn, Top View, Simulation

Figure 3.10: The behavior of the AUV in field tests during the Turbo-Turn maneuverin (a) is qualitatively similar to the simulated motion in (b). This enables the useof the simulator as a development tool, as maneuvers and control strategies can bepre-tested in simulation [15].

Chapter 4

Cyber-physical Systems

Translating novel capabilities and control strategies derived in virtual simulation en-vironments to real-world scenarios is essential. However, such translation is alsotremendously difficult, since additional disturbances such as waves and currents,wakes from other vehicles and objects, uncertainties in sensing and communication,actuator delays and saturation, couplings caused due to unmodelled effects and en-vironmental factors such as temperature, salinity or density could render the resultsof simulations to be inaccurate or even invalid. Therefore, there is a need for a struc-tured approach to reduce the risks and uncertainties associated with this transition.Close intertwining between virtual and physical worlds can be crucial for successfulapplication cases, and this motivates tight integration of simulation environmentswith the full robotic system. This means that a network of sensors and actuators istightly coupled with software, intelligence and simulations across platforms, lengthand time scales. Such intertwined systems are called cyber-physical systems (CPS),and they can provide a streamlined pathway to translate new ideas to reality withreduced risk.

This concept is illustrated in Figure 4.1. The software and hardware subsystemsare all part of a common sensing and computing network. The SAM AUV is at thecenter of the cyber-physical system, and utilizes the Robot Operating System (ROS)1 as its common network communication interface between software components.The actuators and sensors within SAM utilize a common communication protocolwith the Controller Area Network bus (CAN bus) 2. To blur the distinctions betweenhardware and software with minimal delay and high robustness, a translation linkexists between the CAN-bus and ROS 3. The AUV system is coupled with virtual

1https://www.ros.org/2https://uavcan.org/3https://github.com/smarc-project/uavcan_ros_bridge

26

https://www.ros.org/https://uavcan.org/https://github.com/smarc-project/uavcan_ros_bridge

CHAPTER 4. CYBER-PHYSICAL SYSTEMS 27

SAM SW

Status

Action

BehaviorTree

Motion Planner

Sensor Data

Sensor Data

Control SignalsCAN-ROSBridge

Pose

PoseDead Reckoning

SAM Hardware

Plan

IMC-ROSBridge

ControllersControllersControllers

Stonefish Simulator

Simulink Simulator

Control SignalsSet Points

Operator

WebInterface

Topics

NeptusPlan

Status

Figure 4.1: The SAM cyber-physical system architecture integrating a user interfacefor mission planning, control, autonomy and localization software, AUV hardware,and simulation tools. Two simulation environments are integrated – the Stonefishsimulator to rehearse missions and integrate software, and the hydrobatics simulatorto simulate advanced maneuvering [17].

validation of subsystems, controllers, maneuvers and missions. Therefore simula-tions of varying time and length scales are used to model and validate various vehicleand mission scenarios. Simulator environments (such as the hydrobatics simulatorpresented in Chapter 3) can publish and subscribe to topics within the ROS networkwith the same interface as the real AUV, enabling rapid validation in the loop. Inthis way the hardware, onboard software, and external evaluation environments aretightly coupled. This system can also be scaled to include multiple robots and furtherexternal tools for mission planning and payload post-processing.

4.1 Controller validation with SimulinkA tight link to virtual validation means that one can fail several times in simulationuntil a fault-tolerant and robust system is achieved. The cyber-physical link enablesthe best solutions to be looped between reality and simulation with high confidence.With a focus on hydrobatics, control strategies designed within the real-time flightsimulator can be readily translated to the real AUV hardware (see Figure 4.2). Highrisk maneuvers such as the turbo-turn sequence can be optimized in simulation be-fore field testing. Furthermore controllers for depth, pitch and heading control canbe tuned and then deployed to hardware. Similarly, if model predictive controllers,or reinforcement learning policies are developed in simulation, these can also betranslated to hardware thanks to the system structure.

28 CHAPTER 4. CYBER-PHYSICAL SYSTEMS

Figure 4.2: Controllers can be evaluated first in simulation models in Simulink, andlater deployed to the ROS environment for system integration and hardware deploy-ment.

4.2 Software validation with StonefishTo further expand on the cyber-physical system; a second simulation environment 4based on the open-source Stonefish software package [48] provides a virtual worldto test the entire software system on the AUV. The environment can be representedincluding specific bathymetry, submerged objects and water conditions, albeit withless numerical and physical accuracy than the flight simulator (due to processingconstraints). The control systems linked to hydrobatics also interact closely with theautonomy, perception and localization modules. The focus of the Stonefish simula-tor is to enable the evaluation of planning, autonomy and perception packages. Inthis way, full system runs can be performed for early fault detection. Entire missions(e.g. to search for a submerged target or to survey an area) can be rehearsed, andhigh risk activities such as the multi-agent operations can be explored (see Figure4.3).

The two simulator environments complement each other, and ensure that whennew autonomy software is deployed to the actual AUV, it is pre-validated. Thereare challenges with translating controllers and localization systems to reality, sinceseveral disturbances and nonlinearities can necessitate fine-tuning. However, a basicfoundation has been constructed as a springboard for better fine-tuning on board onthe real environment.

4https://github.com/smarc-project/sam_stonefish_sim

https://github.com/smarc-project/sam_stonefish_sim


Figure 4.3: Mission rehearsal with multiple SAM AUVs in the Stonefish simulationenvironment (left) and in the Neptus mission planning interface (right). SAM-1 isexecuting a mission while SAM-2 follows SAM-1. A sunken target (car) can be seenin the background [17].

4.3 Results of field testsTaking the CPS concept in Figure 4.1 to reality, an inspection scenario has been re-alized with the SAM AUV in the Baltic Sea at the island of Askö in field operatingconditions (details can be found in [17]). SAMwas used to autonomously search forand detect a submerged passenger car (a Mini Cooper) using its payload cameras.To realize such a scenario, a search mission was planned as a sequence of waypointsusing the Neptus mission planning interface 5. A behavior tree enabled autonomousmission execution, sending targets to specific actions. A motion planner read thesetargets and generated setpoints for controllers. A set of flight and trim controllersenabled the AUV to reach the target waypoints, acting on feedback from the local-ization module. A perception module processed the feed from the payload cameras,and fed back information on target detection to the behavior tree. Furthermore, SAMwas capable of re-planning in the case of detection, and could react to emergencysituations. This entire system was thus validated in a real-world use case (see Figure4.4 for the results of the search mission overlaid with a satellite image, and Figure 4.5for the 3D trajectory of SAM computed using dead reckoning during the mission).Additionally, hydrobatic capabilities such as the turbo-turn sequence and static pitchand depth control were also demonstrated in the field to improve mission execution,showing the translation of these capabilities to the real world.

The field test demonstrated that a streamlined sequence exists for translatingideas from simulation to reality. The elements of the cyber-physical system enableus to appreciate and exploit the nuances of the use case scenarios. For example, the

5https://www.lsts.pt/toolchain/neptus/

https://www.lsts.pt/toolchain/neptus/

30 CHAPTER 4. CYBER-PHYSICAL SYSTEMS

Figure 4.4: The spiral search mission in Askö in which the car is found. The plot-ted points are from the localization module. Colour indicates time, blue(start) tored(end). The red marker shows where the car is, known from a prior multibeamechosounder survey. Green lines show the planned path. Green marker shows theestimated location of the car from the on-board localization unit [17].

use of the simulation tools enabled fine-tuning of the interaction between the higherlevel planners and the lower level controllers and reduced the chance of conflictingobjectives. While traditional flight control strategies using cascaded feedback con-trollers have been used for maneuvering at this stage, the overall system architectureoffers a strong framework for the development of further novel control and motionplanning strategies. This offers inspiration for further work in more advanced con-trol strategies for challenging use cases such as commercial algae farm operationsor under-ice monitoring. Such cyber-physical systems can link closely to the revo-lution in Industry 4.0 (with a focus on interconnection between systems, autonomyand information transparency [49, 50]), and can enable rapid development of newcapabilities for challenging and novel use cases.

4.4 For the interested readerThe cyber physical system concept is explored in Papers C and D. Detailed descrip-tions of hardware and software subsystems, as well as simulation environments areprovided, and results and challenges are further highlighted. Paper C introducesthe idea and focuses on the ROS-CAN bus link, while Paper D focuses on systemintegration for field testing.


Figure 4.5: The trajectory of SAM in 3D (blue to red gradient in time). The desiredwaypoints are shown in green. Note the different scales of the horizontal and verticalaxes.The AUV could hold depth with an accuracy of 0.5m [17].

Chapter 5

Conclusions and outlook

The reader has been introduced to the concept of hydrobatics in the course of thiswork. Hydrobatic AUVs have been defined, and impact areas in environmental sens-ing, ocean production and security have been delineated. A first key challenge inunderactuated control has been introduced, and solution strategies have been pro-posed. A second key challenge in flight dynamics modeling has been addressed, andreal-time simulations of hydrobatic maneuvers have been demonstrated. Such simu-lations have been translated to real-world use cases through a cyber-physical system;which encompasses not just hydrobatic control strategies, but also autonomy pack-ages and hardware. Through this sequence, hydrobatics has been taken from conceptto reality.

Revisiting the research question, the boundaries of underactuated AUVs havebeen demarcated by identifying the technical challenges in underactuated controland flight dynamics modeling (Paper A). A novel framework for handling advancedhydrodynamic modelling is a key contribution of this thesis (Paper B). Further, thehydrodynamic models together with the cyber-physical system concept have beendeveloped and verified in both simulations and experiments (Papers C and D) toenable us to further push the boundaries for new strategies in underactuated control.This can enable us to realize AUVs that are both efficient in speed and range, as wellas agile in maneuvering.

The current focus is therefore to further explore the challenge of underactua-tion, by designing a lightweight model-predictive controller to control hydrobaticmaneuvers in field experiments. Moving forward, further intelligence to handle un-certainties and adapt to changing conditions will be added to the system. This willenable efficient and adaptive underactuated control strategies using new ideas andtechniques in motion planning, behavior trees, reinforcement learning and system

32

CHAPTER 5. CONCLUSIONS AND OUTLOOK 33

identification. Further use case scenarios and capabilities of the system will also beprioritized, such as precision maneuvering, docking and swarming. Upcoming pub-lications will therefore focus on both new control strategies and application cases.

5.1 Real-time nonlinear MPCIn order to address the underactuated control problem, an efficient and simple non-linear model predictive controller that can run in real-time on the AUV hardware isproposed. The prediction model used combines the 6 degree-of-freedom rigid bodykinematics (including quaternion rotations to enable 0 − 360◦ maneuvering in alldirections) and a vectorial nonlinear flight dynamics model (previously presented inequation (3.1) in Chapter 3). This flight dynamics model can be augmented withthe multi-fidelity LUTs to improve accuracy at high angles of attack. The predictionmodel is given by

η̇ = Jq(η)ν ,

ν̇ = M−1eff (τC − Ceff(ν) − g(η)) ,(5.1)

where η contains the positions and orientations and ν comprises the linear and an-gular velocities. Meff represents the combined mass and inertia matrix includingadded mass terms, Ceff represents the combined velocity dependent terms includ-ing Coriolis forces, centripetal forces and hydrodynamic damping, g(η) representshydrostatics, and τC contains the control forces and moments from the propulsionand thrust vectoring subsystems. If necessary, trim actuators such as the LCG orVBS subsystems can be modeled by adding the mass and c.g. positions as addi-tional states.

The nonlinear model predictive control problem is formulated for output refer-ence tracking, where the optimal control must minimize the deviation of the outputfrom a reference value. An output function o(η, ν) converts the orientations in thestate vector

[η ν

]T from quaternion parameters to Euler angles so state devi-ations can be tracked easily. The output state is given by

[ηout νout

]T , whilethe reference state to be tracked is given by

[ηref νref

]T . The model predictivecontroller aims to minimize the deviation from the reference trajectory, while alsominimizing the use of the control τC as well as reducing control transients τ̇C . Theobjective function J is given by

34 CHAPTER 5. CONCLUSIONS AND OUTLOOK

J =

∫ t+Tt

[(ηref(t)− ηout(t))TQ(ηref(t)− ηout(t))+

τTC (t)R1τC(t) + τ̇TC (t)R2τ̇C(t)]dt

(5.2)

where t is the time, T is the prediction horizon, and Q,R1 and R2 are weightingmatrices. In addition to the prediction model and the objective function, additionallimits on actuators as well as constraints on states can be specified. Direct methodsin nonlinear programming such as Sequential Quadratic Programming (SQP) canbe used to obtain the optimal control [24]. Linearized (Jacobian) matrices of thedynamics model, the objective function and the output function can be used to speedup the solution time for the optimization solver. This setup can be evaluated in sim-ulation (e.g. using the MPC toolbox in Matlab), and ongoing research work focuseson implementing and evaluating this controller in the AUV hardware.

Such a controller can be customized, and it is possible to easily extend it to in-clude additional constraints, objectives, and allow model updates. The solver speedcould limit use of optimal and predictive control for online real-time implementa-tions, especially if nonlinear dynamics in 6DOF is considered (as it is here). This isbecause the large state space and the nonlinear dynamic constraints make the opti-mization problem difficult to solve. Linearizing the model about specific referencestates simplifies the problem, and can enable rapid solutions (e.g. using convex op-timization or linear-quadratic regulation). Warm start techniques could also be usedfor increased speed. Additional custom constraints can be added to make the con-troller more robust.

5.2 Intelligent control and motion planningThe MPC can be augmented with a feedback motion planner to make the systemmore intelligent. Robustness can be improved by using feedback motion planning toplan safe trajectories and add funnel-like state constraints to the MPC [34]. Behaviortrees can be used to switch between different control objectives based on the missionstate, thereby acting as supervisory or event driven controllers [51]. For example, anenergy-optimal controller can be used for transit and simple maneuvers; while time-optimal control can be prioritized in emergencies or criticial scenarios. Augmentingbehavior trees with control barrier functions can enable guaranteed safety limits [52].Techniques in machine learning can be used to identify dynamics models of the sys-tem from sensor feedback [53] to enable adaptive or dual control. The optimizationsolver can be improved as well, using techniques such as deep reinforcement learn-ing to offer real-time and rapid optimal control solutions to dynamic systems in the

CHAPTER 5. CONCLUSIONS AND OUTLOOK 35

Figure 5.1: Future ideas in intelligent control for underactuated robotics.

presence of uncertainty[32]. Learnt models of the system can also be used to im-prove the real-time simulation models, thereby enabling a more accurate simulationtestbed. Data from experiment and simulation can be used to derive adaptive con-trol policies using reinforcement learning [54, 55]. These ideas are summarized inFigure 5.1.

5.3 New scenariosThe intelligent controllers thus derived will be used in real-world use case scenarios.These include:

1. Operations within an algae farm environment (see Figure 5.2, top) consider-ing maneuvering in the presence of obstacles, docking with stationary/movingtargets and cooperation with other agents for inspection or manipulation.

2. Operations in a calving glacier front (see Figure 5.2, bottom). Missions in sucha hostile, dynamic environment encompass tests of robustness, adaptability, aswell as agile launch and recovery.

In particular, multi-agent tasks, docking and near/under ice operations will be ex-plored. This increases the focus on autonomy in a hostile and challenging environ-ment. Such demonstrations will put the concepts introduced through a stress test,allowing the solutions to be optimized and industrialized.

To conclude, themethods outlined in this thesis form the foundation to create newhydrobatic capabilities for use in new applications and hostile conditions, therebypushing the frontiers of what we know today!

36 CHAPTER 5. CONCLUSIONS AND OUTLOOK

Figure 5.2: Photographs of new scenarios: kelp farms (top, picture courtesyKosterAlg) and calving glacier fronts (bottom, picture courtesy Elias Strandell Er-storp/Sebastian Thune).

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