Development of a Robotics-based Satellites Docking Simulator

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Development of a Robotics-based Satellites

Docking Simulator

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Development of a Robotics-based

Satellites Docking Simulator

PROEFSCHRIFT

ter verkrijging van de graad van doctor aan de Technische Universiteit Delft,

op gezag van de Rector Magnificus Prof. Ir. K.Ch.A.M. Luyben, voorzitter van het College voor Promoties,

in het openbaar te verdedigen op maandag 17 maart 2014 om 10:00 uur

door

Melak ZEBENAY

Master of Science in Space Science and Technology Helsinki University of Technology

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Dit proefschrift is goedgekeurd door de promotor: Prof. dr. E. K. A Gill

Copromotor: Dr. D. Choukroun

Samenstelling promotiecommissie:

Rector Magnificus, voorzitter Technische Universiteit Delft, NL

Prof. dr. E. K. A Gill Technische Universiteit Delft, NL, promotor Dr. D. Choukroun Technische Universiteit Delft, NL,copromotor Prof.dr. I. Sharf McGill University, CA

Prof.dr. K. Yoshida Tohoku University, JP

Prof.dr.ir. P.P Jonker Technische Universiteit Delft, NL

Dr. T. Boge German Aerospace Center, DE

Dr. A. Schiele European Space Agency/ESTEC, NL

Prof.dr.ir. J.A. Mulder Technische Universiteit Delft, NL, reservelid

ISBN 978-94-6186-276-1

All rights reserved. No part of this publication may be reproduced, stored in a retrieval sys-tem, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without permission of the author.

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To my late grandmother Tye, my mom Endaysh, my Dad Mekonen and my wife Metshet. To my children Dagem and Eklesia- your growth provides a

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Development of a Robotics-based

Satellites Docking Simulator

Melak Zebenay

Abstract

The European Proximity Operation Simulator (EPOS) is a hardware-in-the-loop (HIL) system aiming, among other objectives, at emulating on-orbit docking of spacecraft for verification and validation of the docking phase. This HIL docking simulator set-up essentially consists of docking interfaces, simulating the servic-ing satellite called chaser satellite, the serviced satellite called target satellite, a sensor of the forces and torques during contact, and two industrial robots that hold the docking interfaces, and control satellites motion relative position and at-titude. Furthermore, the EPOS includes a real-time controller interface linked to a computer-based numerical simulator of satellites orbital and attitude dynamics. A key feature of this set-up is the feedback loop that is closed on the real force sensed at the docking interfaces during contact. That feedback force is used as driving input to satellites dynamics numerical simulation.

This HIL docking simulation concept has the unique advantage of using the mea-sured contact forces and torques, but it presents significant challenges. The high stiffness of the industrial robots and the docking interfaces yields a high band-width contact dynamics at impact and, thus, very short contact time durations. These times might be shorter than the inherent time delay of the robot controllers. This leads to physical inconsistency in the docking dynamics and measured vari-ables. This also causes a stability issue in the force feedback HIL system during contact and may cause catastrophic damages to the robots. Additional problems that need to be addressed are the characterization of the stability domain of oper-ation, the compensation of the non-contact forces and torques, such as the mea-sured forces and torques due to gravity effect. Finally, this thesis addresses the task of identifying the dynamic behavior of the robot end-effectors.

This thesis addresses the above mentioned challenges and problems and presents solutions towards a stable and safe docking simulation operation of the EPOS fa-cility. First, in order to mitigate the high stiffness and time delay problem, the thesis introduces a novel idea of simulating contact based on a concept called hy-brid contact dynamics model. The method, developed in this thesis, is based on a combination of a passive compliance control introduced at the end-effector of the robot and a virtual contact model. The virtual contact model allows the

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ator to vary the contact parameters which can also be used as a control gain. The method also allows to solve the stability problem coming from the combination of time delay of the robot controller and high stiffness of the robot end-effector. For the passive compliance control, a new device is designed that has fairly known stiffness properties which are softer than the robot and docking interface stiff-ness. Second, the thesis presents a stability analysis of the proposed method via the adaptation of the pole location method to dead-time systems. The analysis is based on a linearized design model of the dynamics; linearization is performed around the docking geometrical configuration. This work first presents an anal-ysis for the single dimensional case, which is then extended to two dimensions. The highlight of the stability analysis is the development of physically intuitive state-space model that easily unveil the modes of the contact dynamics. The ap-plication of the pole location method to the resulting second-order characteristics polynomial is straight forward. The contribution of this analysis is a closed-form relationship, and associated plots, among the system’s parameter, i.e., the satel-lite’s masses, the stiffness and damping coefficient of the contact parameters, the delay, and the geometry. In addition, the stability analysis is supported using the passivity method which is valid for three dimensions. Third, a model of the force-torque sensor is presented , and the classical weighted least-squares estimation technique is suggested for the identification and compensation of the non-contact forces and torques from the contact force and the torque measurement. Finally, it is proposed to utilize a LEICA laser tracker, a positioning measurement system, in order to identify the robot end-effectors dynamics behaviors such as the natural frequency and damping ratio.

This hybrid contact dynamics model and the accompanying analysis is envisioned as a tool for safe and flexible EPOS operations. This tool shall allow emulation of the desired impact dynamics for any stiffness and damping characteristics within the stability region without recurring to a modification of the hardware. The perimental results of the robotics based hybrid docking simulator comply with ex-perimental data from an air-bearing testbed that was independently performed by this author at the Space Robotics Laboratory of Tohoku University. It demonstrates the validity of the novel EPOS concept of operations and increases the confidence of using this approach for future on-orbit docking/contact algorithm validation, at the EPOS facility.

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Abstract

De European Proximity Operation Simulator (Europese Nabijheidsoperatie Sim-ulator, EPOS) is een hardware-in-the-loop (HIL, hardware-in-de-loep) systeem onder meer bedoeld voor het emuleren van het in-orbit aankoppelen van ruimtevaartuigen voor verificatie en validatie van de koppelfase. Deze HIL aankoppelsimulator-installatie bestaat in feite uit een aankoppel-inferface, het simuleren van de bedienende satelliet genaamd chasersatelliet, het simuleren van de bediende satelliet genaamd doelsatelliet, een sensor voor de krachten en kop-pels tijdens het kontakt, en twee industriÃ ´nle robots die het aankoppel-interface, en dus hun relatieve posities, controleren. Bovendien heeft de simulator een real-time controlend interface verbonden aan een computer-gebaseerde numerieke simulator voor [free-floating dynamics/zwevende dynamica]. Een belangrijk ken-merk van deze configuratie is een terugkoppelingslus die is gesloten om de werke-lijke kracht op het aanmeer-interface tijdens het kontakt. Deze terugkoppel-ingskracht wordt gebruikt om in de dynamische numerieke simulatie de [free-floating dynamics/zwevende dynamica] aan te drijven.

Dit HIL-aankoppelsimulatieconcept heeft het unieke voordeel dat het de geme-ten kontaktkracht- en koppelgegevens gebruikt, maar het stelt ook significante uitdagingen. De grote stijfheid van de industriÃ ´nle robots en van het aankoppel-interface leidt tot grote kontakt-dynamica bij de botsing, en dus tot een zeer ko-rtdurend kontaktmoment. Deze tijden zijn mogelijk korter dan de inherente ver-traging van de robot-controllers. Dit leidt tot fysieke inconsistenties in de aankop-peldynamica en de gemeten variabelen. Dit veroorzaakt tijdens het kontakt tevens een stabiliteitsprobleem in het HIL-systeem voor kracht-terugkoppeling, en kan tot rampzalige schade aan de robots leiden. Aanvullende problemen die moeten worden behandeld zijn het karakteriseren van het stabiliteitsdomein voor de oper-atie en het compenseren voor niet-kontakt krachten en koppels zoals de gemeten krachten en koppels door het effect van de zwaartekracht. Ten slotte behandelt deze scriptie een identificatiemethode voor het dynamische gedrag van de robot eind-effectors

Deze scriptie behandelt de boven-genoemde uitdagingen en problemen en pre-senteert oplossingen voor een stabiele en veilige aankoppelsimulatie met de EPOS-faciliteit. Ten eerste, om het probleem van de hoge stijfheid en het ver-tragingsprobleem te behandelen, introduceert deze scriptie een innovatief idee voor het simuleren van het kontakt-moment gebaseerd op een concept genaamd hybrid kontakt dynamics model (hybride kontakt-dynamica model). Het virtuele kontaktmodel stelt de operator in staat om de kontakt-parameters te variÃ ´nren, iets dat ook gebruikt kan worden om de versterking te beÃ´rnvloeden. Deze

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ode biedt bovendien een oplossing voor het stabiliteitsprobleem, dat komt door een combinatie van de vertraging van de robot-controller en de hoge stijfheid van de robot eind-effector. Voor de passieve meegaandheid-controle is een nieuw ap-paraat ontworpen, dat een vrij goed bekende stijfheid heeft, zachter dan die van de robot of die van het aankoppelinterface. Ten tweede behandelt deze scrip-tie een stabiliteitsanalyse van de voorgestelde methode, voor het aanpassen van pool-localisatie-methoden voor dode-tijd systemen voor één en twee dimensies. Bovendien wordt de stabiliteitsanalyse ondersteund door de passiviteitsmethode, die geldig is voor alle dimensies. Ten derde wordt een model voor de kracht-koppel sensor gepresenteerd, en de klassieke gewogen kleinste-kwadraten schat-tingstechniek wordt gesuggereerd voor het identificeren en compenseren voor niet-kontakt krachten en koppels van de kontakt-kracht en de koppel-meting. Ten slotte stelt de scriptie voor om een LEICA laser-tracker te gebruiken, een positie-bepalingssysteem, om zo het dynamische gedrag van de robot eind-effector te identificeren, zoals de natuurlijke frequentie en dempingsgraad. Deze hybrid kontakt dynamics model (hybride kontakt-dynamica model) en de bijvolgende analyse is voorzien om te zorgen voor een veilig en flexibel aankoppelsimulator-hulpmiddel. Dit hulpmiddel zal het reproduceren van de gewenste botsingsdy-namica toelaten, voor een willekeurige stijfheid en dempingsgraadskarakteristiek binnen de stabiliteitsregio. De experimentele resultaten van de robot-gebaseerde hybride aankoppelsimulator komen overeen met de experimentele gegevens van een lucht-dragend testbed. Het demonstreert de geldigheid van de simulator en doet het vertrouwen in de simulator toenemen voor toekomstige validaties van in-orbit aankoppel/kontakt-algorithmes.

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Acknowledgments

I wish to express sincere appreciation to my supervisors, Dr. Daniel Chourkorun and Dr. Toralf Boge, for their guidance and assistance throughout this thesis work, to my promoter, Prof. Dr. Eberhard Gill, for giving me the opportunity to perform research in his group and for his valuable feedbacks.

Many thanks to all people in the On-orbit Servicing team of German Aerospace center for their support on various occasions, in various capacities and to various extents especially, Mr. Thomas Rupp, Ms. Karin Klier, Ms. Heike Benninghoff, Mr. Tilman Wimmer and Mr. Florian Rems.

Many thanks to interns Mr. Thilo Ernst and Mr. Elie Ahmare for the good and dedicated work that underpin this research.

I would like to express my appreciation to colleagues at the Robotics and Mecha-tronics Center of German Aerospace Center. I am truly thankful to Mr. Rainer Krenn and Mr. Roberto Lamparello for their generous advice, help and discus-sions concerning this research work. I am grateful to Dr. Alin Albu-Schäffer and Dr.rer.nat. Bernd Schäfer for their valuable consultation concerning this research work.

I want to acknowledge many fruitful discussions with Prof. Kazuya Yoshida and Dr. Naohiro Uyama from Tohoku University of Japan and their kind support during my stay in Japan.

I thank the Lord my God for his boundless provisions both physical and spiritual. Last but not the least, I cannot thank my family and in-laws enough for their gen-erous support and blessings. Special thanks to my mother, father and brothers, who have cared for me my whole life. Another gift I’ve gotten along the way is my own little family - Methset, Dagem and Eklesia. My wife, Metshet, always has kept me smiling and happy. Words cannot express my gratitude to her, for I could never have reached this point without her. My kids, Dagem and Eklesia, have made my life in German happier and richer.

Melak Zebenay Delft, The Netherlands, March 2014

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List of Tables

4.1 Test results for varying values of the damping parameter b. The mass is 63.2 kg, the delay is 16 ms, and the stiffness is around 1066

N/m . . . 44

4.2 Test results for two values of the mass and of the damping parame-ters. The delay is 16 ms and the stiffness is around 1066 N/m . . . . 45

4.3 Emulation of the air-floating table test using EPOS . . . 53

5.1 Comparison of the linear and the nonlinear stability indices in 2D bc=50 N/m. . . 74

6.1 Initial and final values of part I of the estimated parameter vector for the biases . . . 98

6.2 Initial and final values of part II the estimated parameter vector . . . 98

6.3 Force and torque residuals in three axes. Standard deviation and maximum values. Single run experiment. . . 99

6.4 Force and torque residuals in three axes. Standard deviation and maximum values. Single run experiment. . . 100

7.1 Extracted parameters from (7.5) plot . . . 111

7.7 Dynamic responses (natural frequency, fn, damping ratio,ξ, and period, Td) of a KR204 robot in linear axes for step inputs in X, Y and Z-axis . . . 119

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List of Figures

1.1 The EPOS facility: two robots holding a satellite mock-up and dock-ing interfaces, the target simulator robot is mounted on a linear rail 3

1.2 Hybrid simulators for orbital operations experiments. . . 8

2.1 The early EPOS: the fixed part of the testbed (a) and the mobile part (b) . . . 13

2.2 The new EPOS concept: two robots, one holding a satellite mock-up, the other mounted on a rail, and their associated controllers . . 14

2.3 The new EPOS facility: robotics-based testbed (left) and the opera-tion staopera-tion (right) . . . 14

2.4 EPOS set up for SMART-OLEV . . . 15

2.5 Capture of a target satellite in the DEOS mission . . . 16

2.6 The EPOS facility Coordinate systems . . . 17

2.7 EPOS facility control system . . . 19

2.8 Concept of Operations rendezvous simulation usin EPOS . . . 20

2.9 Concept of operations of the proposed docking simulator at the EPOS 21 3.1 Architectures of robotics-based docking simulators with various types force feedback: pure hardware simulator (top), pure software (middle), and hybrid (bottom). Software and hardware elements appear in blue and orange, respectively. . . 24

3.2 Concept of operation of the hybrid docking simulation . . . 26

4.1 Spring-dashpot modeling of the contact dynamics . . . 29

4.2 Block-diagram of the docking simulator . . . 31

4.3 Block-diagram of the hybrid docking simulator for 1D analysis . . . 32

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4.4 Stability domains for operational point m=60 kg, k=1000 N/m, b=50

Ns/m . . . 35

4.5 Stability domains variations for an operational point m=60 kg, k=1000 N/m, b=50 Ns/m. . . 37

4.6 Root-loci using Eqs. (4.28) and (4.29) . . . 39

4.7 Root-loci using Eqs. (4.30) and (4.31) . . . 41

4.8 Experimental setup of the EPOS docking simulator . . . 42

4.9 A velocity profile of the commanded,vr, and the measured, v vs. time 43 4.10 Experimental validation of the stability analysis: System is stable above solid line and unstable below. Curves b vs h for various masses. The delay is 16 ms. The plot is the zoomed version of Fig 5.4 for different masses . . . 47

4.11 Computation of damping coefficient using the observed energy . . . 49

4.12 The system is active both for b = 0 and b = 20 Ns/m. . . 50

4.13 The system is approximately neutrally stable for b = 40 Ns/m and is passive at b = 70 Ns/m. . . 51

4.14 Air-floating test bed setup at Tohoku University . . . 52

4.15 Comparing experiments of the air-floating table test with EPOS test. Contact force and velocities profiles are similar. The velocity of EPOS test is shifted from 0.02m/s to zero for visualization purpose. Both experiments showed 0.035m/s relative velocity. . . 54

5.1 Concept of operations of the hybrid docking simulator . . . 58

5.2 Block-diagram of two satellites in contact. . . 59

5.3 Free-body diagram of two bodies in contact in 2D. . . 63

5.4 Stability domains variations for an operational point ma=15.6kg, k=1000 N/m, b=50 Ns/m. . . 73

5.5 Stability analysis validation. ma=15.6 kg, h = 16 msec, k = 3000 N/m 75 5.6 Schematics of the compliance device . . . 77

5.7 Passive compliance device mounted on the chaser robot . . . 78

5.8 3D test setup on the DLR EPOS hybrid simulator . . . 82

5.9 Time histories of the force and torque components in the Nozzle frame (upper graphs). Time histories of the relative velocity and position in the Global frame (lower graphs) . . . 83

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5.10 Visualization of the probe tip trajectory as seen from the Nozzle frame N . Dotted line: no damping. Red plot: damping of the z-axis torque. The solid line trajectory is less affected by the first

shock than the dotted line trajectory. . . 84

5.11 The observed energy without virtual damping in all axes (top plot) and with (bottom plot) virtual damping in the longitudinal contact direction . . . 86

6.1 Schematics of chaser robot, F/T sensor and docking interface . . . . 90

6.2 Force and torque residuals in all three axes as a function of the an-gleθk. . . 101

6.3 Force and torque residuals in all three axes as a function of time without changingθk. . . 102

7.1 Overshoot percentage versus damping ratio for a step input to a second order system . . . 108

7.2 Underdamped response . . . 108

7.3 Measurement setup of KR240 robot and LEICA Laser tracker . . . 110

7.4 Step response position measurements for X-axis, Y-axis and Z-axis . 111 7.5 Step response of the robot X-axis . . . 112

7.6 Step response of Y-axis and Z-axis for X-axis step input . . . 113

7.7 Step response of the robot Y-axis . . . 114

7.8 Step response of the robot X-axis and Z-axis . . . 115

7.9 Negative Z-axis step input response of the robot in Z-axis . . . 116

7.10 Step response of the X- axis and Y-axis for Z-axis step input . . . 117

A.1 Unity feedback system with time-delay . . . 125

A.2 Nyquist plots of Gr(s) =_s+13 e−hs . . . 127

A.3 Nyquist plots of _s2_+0.1s+10.4 e−hs . . . 128

D.1 Two passive systems connected in a feedback configuration . . . 134

D.2 Two passive systems connected in parallel . . . 134

D.3 Geometric interpretation of passivity of linear systems . . . 136

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List of Symbols and Acronyms

Symbols

Cartesian frames

G Global coordinate system.

B Chaser Body frame.

S Sensor frame.

S0 Nominal sensor frame.

N Nozzle frame .

H Tool frame.

Matrices

D_BA Rotation matrix from A to B frame. ˆ

Z(k) Estimate of the measurement matrix Z

H Observation matrix.

I3 3 × 3 Identity matrix.

O3 3 × 3 Null matrix.

J Inertial tensor.

K Stiffness tensor.

[a×] Skew-symmetric matrix of vector a.

Vectors

rA Vector w.r.t frame A.

g Gravity vector .

z Measurement vector.

ρ Inertial position vector.

c Bias vector.

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x State vector.

h Non-linear measurement vector.

z Measurement matrix.

δm Differential displacement.

f Force vector.

τ Torque vector.

b

n Unit-norm normal vector.

v Linear velocity vector.

ω Angular velocity vector. bli Unit length vector.

vm Measured velocity vector.

vr Commanded velocity vector.

fm Measured force and torque vector.

fr Commanded force and torque vector.

Scalars mT Target mass. mC Chaser mass. m, ma Reduced mass. k Stiffness. b Damping. kv Virtual stiffness. bv Virtual damping. k_ϕ Device stiffness. bϕ Device damping. e Coefficient of restitution. v+ Average final speed. v− Average initial speed.

N Number of samples.

vm Measured speed.

vr Commanded speed.

fm Measured force.

fr Commanded force.

∆E Observed energy.

∆t Sampling time.

h Time delay.

d Penetration depth.

f Contact force.

xT Target inertial position.

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xC Chaser inertial position.

x Relative position.

xr Required relative position.

Others ξ Damping ratio. fn Natural frequency. Acronyms 1D One-dimension 2D Two-dimension 3D Three-dimension

ATV Automated Transfer Vehicle

CoM Center Of Mass

DEOS Deutsch Orbital Servicing mission

DLR German Aerospace Center

DOF Degree of freedom

ESA European Space Agency

EPOS European Proximity Operation Simulator

F/T Force/Torque sensor

GNC Guidance Navigation and Control

HIL Hardware-in-the-Loop

HTV H-II Transfer Vehicle

ISS International Space Station

LS Least Square

LLT Leica Laser Tracker

MSFC Marshall space Flight Center

NASA National Aeronautics and Space Administration

OOS On-Orbit Servicing

OLEV Orbital Life Extension Vehicle

RvD Rendezvous and Docking

SPDM Special Purpose Dexterous Manipulator STVF Special Task Verification Facility

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1

Introduction

1.1 Motivation

Rendezvous and docking (RvD) are key operational technologies involving more than one spacecraft required for different missions such as exchange of the crew in orbital stations, repair of spacecraft in orbit, and space debris removal [Fehse 2003, Bonnal et al. 2013]. Spacecraft RvD enables novel space capabilities like on-orbit servicing of satellites. Many researche works were conducted on on-on-orbit servicing technologies [Coleshill et al. 2009, Friend 2008, Polites 1998, Stoll et al. 2009, Yoshida 2001]. Yet, only a few organizations worldwide accomplished such missions [Polites 1998]. The German Orbital Servicing Mission (DEOS) is an ex-ample, proposed by the German Aerospace Center (DLR) on-orbit servicing fu-ture mission concept. The main objectives of this mission are the capfu-ture of a non-cooperative and tumbling target satellite and the controlled deorbiting of the mated configuration [Team 2009]. Another planned on-orbit servicing mission is the Orbital Life Extension Vehicle (OLEV) designed for service and lifetime exten-sion of geostationary communication satellites suffering from propellant deple-tion [Viscor 2007]. Critical steps in a satellite on-orbit service mission are the ren-dezvous, the docking, and/or the capture of the target satellite. Performing these tasks autonomously for a non-cooperative target increases the technical challenge and thus the risk. Technologies of servicing spacecraft must therefore be thor-oughly tested before launch in simulated micro-gravity environments.

Several technologies are available for testing and verification in a simulated

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2 CHAPTER1. INTRODUCTION

gravity environment. An Air-bearing tables [Nakanishi 2010,Menon et al. 2007] are limited to planar motion. A Spherical air-bearing simulators are limited to small angular displacements and incurs approximate equilibrium set-ups. A Free-fall methods [Sawada et al. 2004, Menon et al. 2007] provide micro-gravity in a three-dimensional environment, but is limited to 20-30s only and constrained to typi-cally very limited cargo space. The neutral Buoyancy method [Menon et al. 2007] has been extensively used for astronauts training, but is not suitable for hardware testing, in particular because of the water-induced drag that alters the dynam-ics characteristdynam-ics of the tested system. The suspended systems methods [Fehse 2003, Sato et al. 1991] are effectively used to simulate micro-gravity in three di-mensions, but exhibits difficulty in compensating for the kinetic friction within the tension control system. On the other hand, robotics-based hardware-in-the-loop simulators implement effective active gravity compensation, can accommodate complex systems for the RvD simulation, and enable full translation and rotational motions. Furthermore, it provides unlimited time to perform the simulations. There are several examples of a robotics-based hardware-in-the-loop (HIL) simu-lators for space systems RvD simulation. DLR developed an early European Prox-imity Operation Simulator (EPOS) two decades ago [Rupp et al. 2009]. This EPOS facility hosted test campaigns for rendezvous sensors of the autonomous space-craft ATV and HTV. The NASA/MSFC developed a HIL docking simulator using 6-degrees of freedom (DOF) Stewart platform for simulating the Space Shuttle berthing to the International Space Station (ISS) [Friend 2008, Ananthakrishnan et al. 1996]. The Canadian Space Agency built an SPDM (Special Purpose Dexter-ous Manipulator) Task Verification Facility (STVF) using a giant 6-DOF hydraulic robot to simulate the manipulator performance of ISS maintenance tasks [Pied-boeuf et al. 1999, Ma 1997]. The US Naval Research Laboratory used two 6-DOF robotic arms to simulate satellite rendezvous for HIL testing rendezvous sen-sors [Bell et al. 2003].

DLR has upgraded its EPOS facility as shown in Fig. 1. The new facility has been used for verification and validation of different projects. The unique features of this new facility [Boge et al. 2010], in comparison with the previously described simulators, are the two heavy-duty industrial robots. These robots can handle payloads up to 200 kg. In addition, the facility allows relative motion between the robots with a range up to 25 m. The new EPOS facility is aimed at providing test and verification capabilities for complete RvD procedures of on-orbit servic-ing missions. The detail description about EPOS simulator is presented in chapter 2.

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1.2. PROBLEMSTATEMENT 3

Target satellite simulator

Chaser satellite simulator Control room

Figure 1.1: The EPOS facility: two robots holding a satellite mock-up and docking

inter-faces, the target simulator robot is mounted on a linear rail

1.2 Problem Statement

The proposed EPOS based docking simulator set-up [Ma et al. 2011] consists of docking interfaces, simulating the servicing satellite and the target satellite, a sen-sor of the forces and torques (F/T) during contact, two industrial robots that hold the docking interfaces, and thus control their relative position. A key feature of this set-up is a feedback loop that is closed on the real force sensed at the docking interfaces during contact. That feedback force is used in order to drive the free-floating dynamics numerical simulation

Using industrial robots as the key robotic components of such an important HIL simulation facility is a highly challenging approach because these robots are de-signed as accurate positioning machines. As such, they are typically very stiff and do not naturally comply with the particular contact dynamics that satellite bound-aries experience during contact. In addition, initially designed for typical indus-trial applications, like automotive assembly, their speed of response may be too slow. For example, due to communication channels delays, the EPOS robots con-trol system shows an average delay of 16 ms between the positioning command signal and the actual position signal, and an average delay of 16 ms between the actual position signal and the measured position signal. Thus, a closed-loop con-troller using positioning command and measurement of the robots experiences an average delay of 32 ms. This value is relatively high compared to the smaller

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characteristic times of contact dynamics for high stiffness contact case such as the stiffness of the given robots end-effectors. Industrial robots have been used for rendezvous simulation purposes but rarely for docking simulation. Existing tech-nologies provide incomplete and unreliable solutions to the challenges of good compliance and quick response. In order to properly simulate docking ( or con-tact dynamics), the robots must exhibit an adequate compliance and they should response adaptively to the contact dynamics during the docking action. The pro-posed research is organized around the tasks of addressing the mentioned issues, and of providing adequate solutions for HIL docking simulation based on the EPOS system.

1.3 Research Objectives

This research stems from the development project of a docking simulator capa-bility for the new EPOS facility. The simulator shall be used for the final docking phase verification of future on-orbit servicing missions.

The general research objective of this thesis is to develop a docking simulator con-cept based on the EPOS facility under the constraint of the utilization of industrial robots, and to validate the proposed concept via experiments. In particular, the following research objectives were formulated:

• to mitigate the combined effects of the robots high stiffness and response delay on the closed-loop contact.

• to analyze and characterize the stability domain of operations of the pro-posed docking simulator system.

• to develop a methodology for efficient non-contact force and torque com-pensation such as the gravitational force.

• to experimentally identify the dynamics behavior of the robot end-effector. • to perform experiments in order to validate the proposed docking simulator

concept.

1.4 Research Questions

The research questions (RQ) addressed in this thesis are derived from the objec-tives. They are defined as:

RQ1 How to mitigate the effect of the robots stiffness and time delay of the EPOS

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1.5. LITERATURESURVEY 5

RQ2 What are the main driving parameters impacting the stability of the

pro-posed docking simulator concept?

RQ3 How to compensate the non-contact forces and torques such as the

gravi-tational force, the errors, and the disturbances that affect the force-torque sensor measurements?

RQ4 How to identify the robot end-effector dynamics behavior ? RQ5 How to validate the proposed docking simulator concept?

1.5 Literature Survey

An introductory literature survey is presented below. More specific references, re-lated to the chapter’s specific topics, are given in the forthcoming chapters.

1.5.1 Rendezvous and Docking Simulator Technologies

Several technologies are available for testing and verification of a RvD mission in a simulated micro-gravity environment. This section provides an overview of the most commonly used technologies.

Air-bearing simulator An Air-bearing based simulators offer a nearly

friction-free environment [Schwartz et al. 2003]. Simulators based on air-bearing are in-tended to enable payloads to experience some level of translation and rotational freedom. A planar air-bearing based simulator provides two translational and one rotational DoF. For example the Space Lab of Tohoku University [Yoshida et al. 2004, Nakanishi 2010] uses a planar air-bearing table for a rendezvous and dock-ing simulation. Air-beardock-ings are also used in spherical shape called spherical air-bearing simulators. They are commonly used in spacecraft attitude dynamics re-search since they provide unconstrained rotational motion if the center of mass is coincident with the bearing’s center of rotation [Schwartz et al. 2003]. However, spherical air-bearing simulators are limited to small angular displacements and incur approximate equilibrium set-ups [Aghili 2005]. The most advanced and use-ful setup is the combination of planar and rotative motion air-bearing system that provides complete freedom in all six axis degrees. NASA-MSFC provides the only combined simulator of the planar and spherical air-bearing table to one 6-DOF simulator to the author knowledge [Schwartz et al. 2003].

Free-fall simulator The free-fall and/or parabolic flight based simulator [Sawada

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air-6 CHAPTER1. INTRODUCTION

plane flying along a parabolic trajectory and a free-falling capsule at the micro-gravity center. These methods enable micro-micro-gravity in a three dimensional envi-ronment. However, they have limitations: short experiment durations (20-30s), very limited cargo space and weight, cost as well as complexity of flight tests and experiments [Aghili 2005, Menon et al. 2007].

Neutral Buoyancy simulator Neutral Buoyancy methods [Menon et al. 2007]

have been extensively used for astronauts training. NASA has utilized natural Buoyancy simulation for on-orbit tasks since 1968 [Frontera 2002]. The use of this method has the potential advantage that experiments can last for unlimited amount of time. However, it is not suitable for hardware testing - in particular because of the water-induced drag that alters the dynamics characteristics of the tested system. Furthermore, space qualified sensor or systems cannot generally be tested directly under water as they can be damaged.

Suspension simulator This method are generated via a sytsem of cables in order

to compensate for the gravity of the suspended masses. Many attempts have been made to build a test facility for docking dynamics using the suspended system method. However, there are many challenging requirements [Fehse 2003] such as implementing a six-degree motion capability, identifying the satellites masses, inertias and center of mass position as well as compensation for the effects of grav-ity. A suspended docking simulator of Russia’s space agency has been presented in [Fehse 2003]. The facility has five DOF which was used for Apollo-Soyuz dock-ing mechanism verification.

Hybrid or hardware-in-the-loop Method The hybrid methods, which are also

referred to as a HIL, uses both a mathematical model and a hardware which are in-teracting each other through sensors and actuators. Compared to pure hardware simulators, they present several advantages. In some cases the pure hardware tests are challenging such as simulation of two satellites in contact in ground. Pure soft-ware simulator on the other hand have limitations inherent to modeling errors. Several among other concepts hybrid simulators for RvD were developed. Ref-erence [Akima et al. 1999] reports a hybrid simulator based on a Stewart platform robot. Reference [Agrawal et al. 1996] presents a hybrid simulator using two robots to simulate the relative motion in proximity to a free-flying target. DLR and ESA developed a hybrid simulator called the European Proximity Operation Simulator (EPOS) more than two decades ago [Rupp et al. 2009]. The aim of this simulator was relative navigation for the simulation of spacecraft rendezvous, including ver-ification of sensors and systems. The EPOS hosted test campaigns for rendezvous sensors of the autonomous spacecraft maneuvers for the final few critical meters

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of the final rendezvous phase (excluding contact). Since 2008 DLR has upgraded the EPOS based on two robots as shown in Fig. 1.2-a [Boge et al. 2010] extending its capabilities to contact and docking simulation. NASA/MSFC developed a HIL docking simulator using a 6-degrees of freedom (DOF) Stewart platform that sim-ulates the Space Shuttle berthing to the International Space Station (ISS) [Friend 2008, Ananthakrishnan et al. 1996]. The Canadian Space Agency built an SPDM (Special Purpose Dexterous Manipulator) Task Verification Facility (STVF) using a giant 6-DOF hydraulic robot to simulate the manipulator performance of ISS maintenance tasks [Piedboeuf et al. 1999, Ma 1997]. The US Naval Research Lab-oratory used two 6-DOF robotic arms to simulate satellite rendezvous and to test navigation sensors [Bell et al. 2003]. Reference [Kohei et al. 2009] reports on a hybrid simulator based on the combination of a parallel robot and two robots, see Fig 1.2-b.

The present doctoral work is an outgrowth of a research and development project at the DLR-GSOC institute in Oberpfaffenhofen on the EPOS. The purpose is to develop the capabilities of the EPOS to operate during the docking phase. The focus therefore is on the contact phenomenon i.e. modeling of its dynamics, and analysis of the stability of the hybrid simulator.

1.5.2 Contact Dynamics

In general, the contact between two bodies is an extremely difficult and highly non-linear phenomenon. In literature, contact modeling follows two approaches that are a discrete force approach and a continuous force approach [Gilard and Sharf 2002]. The discrete method assumes that the contact between the bodies occurs instantaneously. Furthermore, the dynamic analysis is separated to be-fore and after contact. When the initial state of the system is known, principles of impulse-momentum method are applied to express the post-contact states. The continuous force method on the other hand analyzes the contact geometric con-straints and the corresponding contact forces, and incorporates them into the dy-namic equations. Both types of contact dydy-namics models are reviewed below.

Discrete Contact Model

This model describes the impact between two rigid objects over a short period of time and assumes neglectable changes in the objects configuration [Gilard and Sharf 2002, Faik and Witteman 2000]. This model uses the so-called coefficient of restitution and the impulse ratio to describe the energy transfer and dissipation before, during and after the impact. The coefficient of restitution is defined as a relationship between the normal components of the velocities before and after impact at the contact point.

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(a) The new EPOS hybrid simulator facility robots

(b) Overview of the Tohoku University hybrid simulator [Ko-hei et al. 2009]

Figure 1.2: Hybrid simulators for orbital operations experiments.

The drawbacks of the discrete approach are its complex and the difficulty to extend it on generic multi-body systems [Gilard and Sharf 2002].

Continuous Contact Dynamics Models

The continuous contact force model does not assume of instantaneous contact. It explicitly allows for the computation of the contact forces. In contrast to the discrete model it considers the compliance, damping and friction between ob-jects [Gilard and Sharf 2002]. The general expression for the continuous contact

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normal force, f , is as follows [Nakanishi 2010, Gilard and Sharf 2002]:

f = kdn+ by(d) ˙dq (1.1)

where d is the relative position or penetration depth between the bodies, b is the damping coefficient along the normal of the contacting surfaces, k is the stiffness along the normal to the contacting surfaces, n and q are real numbers the values depend on the shape of the contact objects and y(d ) is as a function of penetration depth.

The following summarizes the most common continuous contact models.

Hertz model The Hertz model [Gilard and Sharf 2002] describes a relationship

between the normal contact force and the local contact penetration depth. This is a non-linear model but is limited to impacts with elastic deformation and in its original form does not include damping in this model, the contact force is defined as:

f = kdn (1.2)

where k and n are constants depending on material and geometric properties. For example in the case two spheres in central impact, the coefficent n is equal to 1.5 and k is defined in terms of Poisson ratios, Young’s moduli and radii of the two spheres. This model can be used only for low impact speed and high stiffness materials [Gilard and Sharf 2002].

Hunt-Crossley model The Hunt-Crossley model [Hunt and Crossley 1975,Gilard

and Sharf 2002] is physically more meaningful than a linear spring-dashpot model and retains the advantage of the Hertz model. The force model has a non-linear damping term and is expressed as follows:

f = kdn+ bdpd˙q (1.3)

where k and b are stiffness and damping coefficients and n, p and q are positive real numbers describing the non-linearity and hysteresis behavior. This model might yield negative non-physical sticking forces. However, there is no force dis-continuity during the transition between contact and no-contact.

Empirical model If the contact geometry are not axisymmetric, it is hard to find

explicit expressions for k and n. In these cases empirical models are used [Ma , Luo and Nahon 2006]. The empirical model relates the contact force between the objects, the penetration depth and contact area A as follows:

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where k(A) is a stiffness coefficient that is function of square root of A, the surface material parameters the contact geometry.

Finite-Element method Many methods were developed using finite

ele-ments [Cook et al. 2002], such as the Lagrange multiplier methods and the penalty method [Kim 1999]. Similar to the discrete method, the finite element method is complex and computationally costly in particular for multi-body system contact dynamics modeling [Kim 1999].

Spring-dashpot model This model relates the normal force between objects

lin-early with the penetration depth, d and the penetration rate, ˙d . The contact force is expressed as follows [Gilard and Sharf 2002]

f = kd + b ˙d . (1.5)

Because of its simplicity, this model has been widely used [Gilard and Sharf 2002, Verscheure 2009, Nakanishi 2010] in most works on contact control of ma-nipulator systems. However, the model has weakness [Gilard and Sharf 2002] such as the linear damping term gives rise to discontinuities in the contact force during transitions between contact and no contact, and negative sticking value of f may occur for large negative penetration rates.

In this thesis, the spring-dashpot model was chosen to model the contact dy-namics. of contacting bodies. Thanks to this model, linear stability analysis tool can be employed. The coefficient of restitution has been redefined in [Nakanishi 2010]that includes the effect of the damping and stiffness of the contacting bodies. That definition of the coefficient of restitution was applied in this work. This al-lows investigating the post-contact behavior of the contacting bodies while using a continuous contact model.

1.6 Scientific Contributions

This thesis is concerned with the development of docking capability of the robotics based HIL simulator EPOS. The contributions made during the course of this the-sis range from hardware design and to software development. In particular a novel implementation of contact dynamics modeling [Zebenay et al. 2013b, Zebenay et al. 2013c, Zebenay et al. 2013a] is introduced. It is called hybrid contact model as it incorporates physical (hardware) and virtual (software) elements in the con-tact parameterization. The proposed hybrid approach is implemented, tested and validated using EPOS under several contact conditions. In order to implement the proposed approach a passive compliance device is designed, built, assembled,

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1.7. THESISOUTLINE 11

and used during tests [Zebenay 2012b]. In addition, the stability of the hybrid sim-ulator system is analyzed. The results presented in [Zebenay et al. 2013b] are in linear motion or single dimension(1D) , and the results presented in [Zebenay et al. 2013a] are extensions to two and three dimensions (2D/3D). The analysis will help the operator in estimating the operational bounds of the docking simulator as a function of critical parameters such as the mass of the simulated satellites, the robot controller bandwidth limit and the contact parameters. Moreover, connect-ing theoretical and experimental domains, the proposed hybrid simulator concept is validated through experiments. The experiments carried at the EPOS and on the air-floating test-bed of Tohoku university provide proofs-of-concept, as well as helped in understanding implementation issues.

1.7 Thesis Outline

This thesis consists of eight chapters. The remainder of the thesis is outlined next. In chapter 2, EPOS facility is briefly described. The chapter begins with an overview of the early EPOS and the new EPOS. Then an overview of the EPOS sys-tems and the operations control system is presented. The chapter closes by intro-ducing a RvD simulator concept based on EPOS.

Chapter 3 clarifies the docking simulator requirements and control architecture. The implementation challenges of the docking simulator are underlined, and so-lutions are proposed.

Chapter 4 presents the 1D hybrid docking simulator concept. It first presents the model, then the stability analysis and concludes with experimental results for val-idation of the method.

Chapter 5 starts with the 3D nonlinear mathematical modeling of the hybrid dock-ing simulator, followed by a 2D linearized model. A stability analysis is performed on the 2D linear system. In addition, the design of a passive compliance device is presented, together with the development of an analytical expression for its effec-tive stiffness tensor. Finally, experimental results are presented.

Chapter 6 address the compensation of the non-contact forces from the contact force measurement data. This comprises the mathematical modeling of the sen-sor, the calibration method, and test results.

Chapter 7 describes a procedure for preliminary identification of the EPOS robot end-effector dynamics. Test results are analyzed.

Finally, a summary of achievements and recommendations for future work are proposed in Chapter 8.

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2

EPOS-European Proximity Operation

Simulator

This chapter provides a brief introduction on the hardware-in-the-loop simulator EPOS.

Section 2.1 presents the early and the new version of the EPOS with their respective capabilities. Section 2.2 describes the facility monitoring and operation control system. The last section provides some insights on the main application areas of the EPOS.

2.1 Introduction

The European Proximity Operations Simulator (EPOS) is a ground-based, hardware-in-the-loop simulation facility that was developed to test and validate algorithms and sensors required during satellites rendezvous and docking opera-tions.

DLR has more than two decades experience in simulating RvD maneuvers. The early EPOS had been developed jointly by DLR and ESA for laboratory simulation of rendezvous from 1985 to 1998. The facility is located at the DLR German Space Operations Center(GSOC) in Oberpfaffenhofen, Germany. In the original imple-mentation had three main subsystems (see Fig 2.1): (1) a dynamic motion sub-system providing six DOF of motion capability for the chaser satellite, (2) a target mount that provides three rotational DOF to the target motion, and (3) a sun

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2.1. INTRODUCTION 13

(a) Top view (b) Bottom view

Figure 2.1: The early EPOS: the fixed part of the testbed (a) and the mobile part (b)

mination simulator.

The early EPOS was designed for real-time simulation of satellites rendezvous ma-neuvers, i.e. for the last few critical meters of the rendezvous phase. That facility had been used for many projects such as the ATV( RvD sensors and GNC system) verification and the Japanese HTV (RvD sensors testing) [Fehse 2003] [Boge et al. 2010].

The verification and validation requirements were continuously amended mainly due to changing application scenarios. On-orbit servicing missions require a prox-imity operation simulator facility that provides several testing capabilities. First, the facility shall prove six DOF relative dynamic motion of two satellites in the fi-nal approaching phase. Second, it shall provide six DOF contact dynamic behavior during the entire docking process. Third, it shall provide the space-representative lighting and background conditions such as sun simulator and projection of the earth image. The early EPOS obviously cannot satisfy all these requirements, in particular the contact dynamics simulation. Thus, it was replaced entirely by a new system (see the schematics in Fig 2.2).

The design and construction work of the new facility began in 2008. Figure 2.3 shows the pictures of the new facility that aims at providing test and verification capabilities for complete RvD phases of on-orbit servicing missions. Furthermore, the new EPOS has several advantages compared to early version. The measure-ment accuracy is improved compared to the former EPOS. In addition, it allows for high command rates and for the implementation of the F/T at the robot end-effector for docking simulation. Moreover, it benefits from an improved the sun-light illumination simulation. Finally, the utilization of two robots yields a system with 13DOF, 6-DOF from each robot and a linear movement on the target robot on the rail system, allowing a high flexibility for different application scenarios.

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14 CHAPTER2. EPOS-EUROPEANPROXIMITYOPERATIONSIMULATOR Robot 1 KR 100 HA Linear slide Robot 2 KR 240-2 Protective Fence Robot Control KRC2 for KR 240-2 Robot Control KRC2 for KR 100 HA Peripheric Control Cabinet

Figure 2.2: The new EPOS concept: two robots, one holding a satellite mock-up, the other

mounted on a rail, and their associated controllers

Figure 2.3: The new EPOS facility: robotics-based testbed (left) and the operation station

(right)

The two robots of the new EPOS are industrial robots manufactured by KUKA [Boge 2011]. The robots simulate the motions of the chaser and target satel-lites. The target, a KR100 robot, is mounted on a 25 m long linear rail system. This robot can move along the rail, add another DOF, which is used to simulate the relative approach phase of the satellites. The chaser, a KR240 robot, is fixed with respect to the laboratory ground. Each robot is equipped with a fixture tool at-tached at the end-effectors which allows mounting various devices, e.g a docking interface, sensors, a satellite mockup. In addition, the fixture tool provides differ-ent voltage power supplies as well as various data interfaces [Boge 2011]. These robots accept position command in real-time for simulations of rendezvous and docking maneuvers. Thus, the target simulator can maneuver from 25 m to 0 m. This facility shall contribute to the feasibility and safety of future missions such as

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2.1. INTRODUCTION 15

for testing of a capture tool developed at DLR and improving RvD hardware and software. As a generic RvD simulation facility, EPOS is not designed for a partic-ular mission-related application. However, potential future missions did provide the momentum for the development of the facility. Recently, several satellite pro-grams were proposed in the field of on-orbit servicing(OOS).All scenarios involve servicing satellite approaching and docking to a client satellite. Next are described two mission concepts, for which the EPOS can play a critical role in the testing and verification phases.

SMART-OLEV mission

The objective of the SMART-OLEV mission is the orbital lifetime extension of com-mercial Geostationary satellites [Viscor 2007]. The chaser shall approach a target, dock to it and control their combined attitude and orbit. Figure 2.4 shows an il-lustration of the SMART-OLEV RvD test setup. The RvD sensors will be mounted

Figure 2.4: EPOS set up for SMART-OLEV [Viscor 2007]

on the chaser robot. A typical satellite mock-up of the target satellite shall be mounted on the other robot. The RvD sensors can measure the relative position and attitude of the client satellite. On this basis, the on-board computer calcu-lates the necessary thrusters and reaction wheel commands. These are executed in a real time simulator. In the next sample time that numerical simulator updates the state vector both spacecraft positions and attitude based on all relevant envi-ronmental and control forces and torques. Then, the state vector is provided as a reference input to the robots tracking controllers. The docking scenario involves a nozzle in the target and a probe attacjed to the chaser. The chaser probe shall dock to the target [Krenn and Hirzinge 2009].

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16 CHAPTER2. EPOS-EUROPEANPROXIMITYOPERATIONSIMULATOR

In this thesis, the SMART-OLEV docking scenario was used as the main application case.

DEutsche Orbital Servicing mission

DEOS is a technology demonstration mission in low Earth orbit. The mission plans on demonstrating various technologies in the area of rendezvous, docking as well as re-entry capabilities [Sellmaier et al. 2011]. According to the Phase A study, DEOS will include two satellites, a chaser and a dedicated target, launched to-gether into an initial orbit. The primary mission goal is the capturing of the tum-bling and non-cooperative target using a manipulator on the chaser. Furthermore, the mission shall demonstrate the re-entry (de-orbit) of the coupled configuration within a pre-defined orbit corridor [Rupp et al. 2009]. To achieve the envisaged goal, dedicated experiments must be conducted with increasing complexity over the mission period. Figure 2.5 depicts the chaser with its manipulator and the tar-get satellite. Experiments are currently running at the EPOS in order to validate the rendezvous sensors and planned trajectories.

Figure 2.5: Capture of a client satellite in the DEOS mission (courtesy of DLR) [Sellmaier

et al. 2011]

2.2 EPOS Reference Coordinate Systems

Some of the coordinate systems of the EPOS facility relevant for this research are introduced here, see Fig. 2.6. The full description can be found in the EPOS

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man-2.2. EPOS REFERENCECOORDINATESYSTEMS 17

ual [Boge 2011].

• Global Laboratory Coordinate System: The axes orientations of this right-handed Cartesian coordinate frame are shown in Fig. 2.6. The origin is de-fined 1.5 m above the floor of the laboratory. This system is fixed with respect to the laboratory room, and serves as an inertial system for all practical pur-poses. It can be used to command both robots simultaneously.

• Base Coordinate Systems: For each of the two robots, these right-handed Cartesian coordinate systems have their origins in the middle of the robots bases, and the axes orientations are shown Fig. 2.6. These systems result from 3-2-1 rotations from the Global Laboratory frame with Euler angles C , B, A.

• Tool Coordinate Systems: For each of the two robots, these right-handed Cartesian coordinate systems have their origins at the middle of the tool flanges. The axes orientations are shown in Fig. 2.6. These systems result from 3-2-1 rotations from the Global Laboratory frame with Euler angles C , B, A. Notice that the same symbols are used for simplicity. Symbols will be distinguished when needed in the subsequent developments.

Figure 2.6: The EPOS facility Coordinate systems [Boge 2011]

Notice that the Euler angles appearing in Fig. 2.6 denote angles of the intermediary rotations within the 3-2-1 sequences.

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2.3 EPOS Operation Control System

Figure 2.7 shows a diagram of the EPOS monitoring and control system. The entire control system is divided into three levels. The first level is the application control system. At this level, the actual application of the dynamic system is executed. In particular, the models of the satellites dynamics and the case-specific scenarios can be implemented in a MATLAB/Simulink environment such as two satellites in contact. This means that the whole software related part of the simulation can ex-ploit a model-based design approach. According to it, MATLAB/Real-Time Work-shop can be used to accomplish the automatic code generation. Subsequently the real-time executable is downloaded to a target platform running under the Vx-Works operating system. The second level is associated with a real-time monitor-ing and control system of the robots. This system induces a relatively large delay between the time when the position command is sent to the robots and the time when they reach the desired position. The nominal value for that delay, as pro-vided by the manufacturer, is 16 msec. Then, another delay of the same value, 16 msec, is induced by the internal monitoring system when providing the current position of the robots. The third level is the local robot control (LRC) system. This system separately controls all axes of the robots. The robotics tracking system has a sub-millimeter accuracy, three sigma accuracy of 1.56 mm in position and 0.2 deg in attitude, and operates at a frequency of 250 Hz.

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2.4. RENDEZVOUS ANDDOCKINGSIMULATIONCONCEPTS 19 Windows Windows VxWorks VxWorks EtherCAT Slave Master Application Control System Facility Monitoring and Control System Local Robot Control Units RSI Ethernet RvD Sensors etc.

Figure 2.7: EPOS facility control system [Boge 2011]

2.4 Rendezvous and Docking Simulation Concepts

2.4.1 Rendezvous Simulator Concept

The main objective of the EPOS is to validate and verify an on-orbit servicing (OOS) mission phases such as rendezvous, docking algorithms and OOS sensors. The rendezvous is a critical phase in the OOS mission. Thus EPOS can be utilized for verification of the close range rendezvous phase of the mission [Tzschichholz et al. ].

A general concept of a rendezvous simulation is described in Boge [Boge et al. 2011]. Based on that concept, [Benninghoff et al. 2012b] describes a HIL simu-lator for close range rendezvous using the EPOS. Figure 2.7 shows a conceptual illustration of the EPOS for rendezvous.

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Facility Monitoring and Control System Satellite Dynamic Simulator Sun simulator Camera Robot 1 KUKA Control Joint motion feedback Joint motion command Robot 2 KUKA Control Joint motion feedback Joint motion command Robot 2 motion

feedback Robot 2 motion command

Robot 2 motion command Robot 1 motion feedback

Application Control System

GNC Navigation Filter Satellite Controller Image Processing Camera Images Facility motion command Facility motion feedback

Figure 2.8: Concept of Operations rendezvous simulation usin EPOS [Boge et al. 2011]

2.4.2 Docking Simulator Concept

The docking system of a servicing spacecraft has to be thoroughly tested and veri-fied before a real space mission is launched. In particular, the validation of a dock-ing process in three dimensions is very challengdock-ing.

In this thesis, a robotics-based 3D docking simulator concept is envisioned based on the EPOS. In this concept, a 3D active gravity compensation is implemented. Figure 2.9 illustrates the concept of operations of the docking simulator at the EPOS. This concept contains three essential elements. The first element is a real-time computer simulator used to predict the dynamic response of the target and chaser satellites. The model-based simulator integrates rigid multi-body dynam-ics equations of motion. The second element consists of the two robots that track in real time position signals generated by the satellites numerical simulator. The third element is a hardware mockup of the docking mechanism of the satellites, onto which a F/T sensor is attached. This sensor generates readings of the contact force and torque when the chaser probe hits the target’s nozzle. These measure-ments are used in a feedback loop as inputs into the numerical satellites simulator. The force sensor output, after calibration, is corrupted with errors of order 0.25 N

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2.4. RENDEZVOUS ANDDOCKINGSIMULATIONCONCEPTS 21

and has a sampling frequency of 1000 Hz. This force/torque feedback feature cir-cumvents the limitations and challenges of the contact dynamics mathematical modeling.

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3

Hybrid Docking Simulator Concept

This chapter presents the requirements for the docking/contact simulator. Then, three different concept architectures are presented. Finally, the selected concept is described together with the stratgey to address the questions formulated at the set of this research.

3.1 Architecture and Requirements

A critical requirement for the control of the EPOS hybrid simulator is that the 6-DOF robot in the loop has to mimic the dynamic response of the space system to be simulated during a contact operation. In other words, the dynamics of the 6-DOF robot should not alter the dynamics characteristics of the simulated satellite system as they exhibited at contact interfaces.

For such a concept to have high simulation fidelity, it is required that the simu-lated docking behavior must be the same as that from the real satellite docking operation. Using the common sense, one can easily understand that such a fun-damental requirement can be achieved under the following two conditions. First the active robots used to deliver the simulated satellite motion must be able to quickly respond to the control command. Second, when reacting to a physical contact during a docking operation, the active robots must dynamically comply like the on-orbit satellites being simulated. The first condition requires the active robots to have fast response to its control system and the second condition re-quires the robots to exhibit very similar dynamics characteristics as the satellites

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3.2. CHALLENGES ANDCONSTRAINTS 23

to be simulated. However, using industrial robots for hybrid docking simulation purposes is a highly challenging approach. Designed to be very accurate in po-sition, the robotic system typically is very stiff [Park 2006, Xu and Paul 1988], not compliant to the requirement. On the other hand, the response time is relatively long compared to the time of the contact dynamics of the contacting surfaces. In addition, the hybrid simulator relies on feed-backing the measured contact force to a numerical simulator of the satellites dynamics. That software (S/W) mod-ule calculates the satellites’ positions and feeds them as command signals to the robots tracking controller. This system is, in essence, a closed loop system where the robotics tracking system introduces a communication delay before it responds to the command [Boge 2011]. Figure 3.1 depicts the functional block diagrams of three different concepts: pure hardware with measured force feedback (top), pure software with virtual force feedback (middle), and the hybrid approach using a combination of both (bottom).

The concept of pure hardware with measured force feedback (top of Fig 3.1)is sim-ple,truthful to the actual sensed force and avoids the modeling of the contact force. The high stiffness contact combined with time delay in the loop triggers instability and this harms the system [Krenn and Schaefer 1999, Kohei et al. 2009]. The sta-bility problem causes the numerical simulation and the hardware of the simulator to interact in the wrong way.

The second concept, pure software with virtual force feedback (middle of Fig 3.1), simulates both the satellites dynamics and the contact force dynamics and sends the generated command to the robots position controller. However, the mismatch between the models and the real plant might also instability problem [Park 2006, Xu and Paul 1988].In this concept, the relevance of hardware-in-the-loop testing is diminished since no information feedback from the contact is used.

The third concept is a combination of the first two and is, therefore, called hybrid. that is the combination of pure hardware contact dynamics with force-torque sen-sor and the pure contact dynamics modeling. The choice of this concept for im-plementation in the EPOS is discussed next.

3.2 Challenges and Constraints

Existing approch provide incomplete and unreliable solutions of the problem with the challenges of good compliance and quick response. In order to simulate dock-ing (contact dynamics), the robots must have a good control of its compliance and should quickly respond to the docking action. An ideal control approach would be to apply an impedance control, see [Hogan 1985]. However, this is not possi-ble in the industrial robots used at the EPOS, as they allow for position control of the end-effectors only and their low-level control software is not open. Similarly,

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24 CHAPTER3. HYBRIDDOCKINGSIMULATORCONCEPT + + Docking I/F & F/T Sensor Satellite Dynamics Simulation Robot Control System +

Virtual Docking I/F & Virtual F/T Sensor Satellite Dynamics Simulation Robot Control System Position Estimator + + +

Virtual Docking I/F & Virtual F/T Sensor Docking I/F & F/T Sensor Satellite Dynamics Simulation Robot Control System Position Estimator + Input Input Input Net F/T Measured F/T Required position Measured position Virtual F/T Required position Measured position Measured position Required position Measured F/T Virtual F/T Net F/T Net F/T

Figure 3.1: Architectures of robotics-based docking simulators with various types force

feedback: pure hardware simulator (top), pure software (middle), and hybrid (bottom). Software and hardware elements appear in blue and orange, respec-tively.

many other advanced and proven robot control strategies, such as the computed torque control [Middleton and Goodwin 1988], cannot be implemented on indus-trial robots because it requires the knowledge of the robots dynamics as well as an access to the velocity and acceleration control of the robots. The only option is to use admittance control [Albu-Schaffer and Hirzinger 2002, Ma et al. 2011] as an outer loop on top of the original inner-loop position control system using the measured contact force and torque as feedback. In order to implement admittance controller, it is required to know the impedance model for a specific satellite ing case. Since the satellite model has been assumed available for hybrid dock-ing simulator implementation this allows to use the 6-DOF rigid-body dynamics model of the satellite as the target impedance model.

Development of a Robotics-based Satellites Docking Simulator

Development of a Robotics-based Satellites

Docking Simulator

Development of a Robotics-based

Satellites Docking Simulator

Development of a Robotics-based

Satellites Docking Simulator

Abstract

Abstract

Acknowledgments

Table of Contents

List of Tables

List of Figures

List of Symbols and Acronyms

1

Introduction

2

EPOS-European Proximity Operation

Simulator

3

Hybrid Docking Simulator Concept