TECHNICAL CONTRIBUTION

Autonomous Navigation for On-Orbit Servicing

Heike Benninghoff • Toralf Boge • Florian Rems

Received: 11 September 2013 / Accepted: 27 February 2014 / Published online: 19 March 2014

� Springer-Verlag Berlin Heidelberg 2014

Abstract On-orbit servicing missions induce challenges

for the rendezvous and docking system since a typical

target satellite is not specially prepared for such a mission,

can be partly damaged or even freely tumbling with lost

attitude control. In contrast to manned spaceflight or for-

mation flying missions, new sensors and algorithms have to

be designed for relative navigation. Dependent on the

distance to the target, optical sensors such as mono and

stereo cameras as well as 3D sensors like laser scanners can

be employed as rendezvous sensors. Navigation methods

for far and close range and different verification methods

are discussed.

Keywords On-orbit servicing � Visual navigation �Autonomous rendezvous � Image processing � Non-

cooperative target � Pose estimation

1 Introduction

Future on-orbit servicing missions aim at satellite lifetime

extension or safe end-of-life de-orbiting of a non-cooper-

ative, passive target [8, 19, 25, 31]. A typical target

satellite can be affected by some damage, can have a non-

operational orbit and attitude control or can be out of fuel.

The idea of on-orbit servicing can be tracked back to 1987,

when TV-Sat 1 was launched and a failure of one of the

solar panels terminated the mission since all attempts to

repair the satellite from ground had failed. By approaching

and docking to the target satellite with a servicing satellite

and by performing service tasks like refueling, deployment

of solar arrays or the take over of orbit and attitude control,

the lifetime of a partly damaged satellite could be extended

in the future.

At the end of a satellite’s lifetime, it is necessary to de-

orbit it, i.e. to perform a re-entry in the Earth’s atmosphere

or to bring the satellite to a graveyard orbit. If the satellite

is damaged, if there is no control of its orbit and attitude or

if the communication with the satellite fails, a de-orbiting

can be performed using a small service satellite. The ser-

vicer approaches the target satellite in its orbit, captures it

with a docking tool or a robotic arm, rigidly connects with

the target, and takes over its orbit and attitude control

followed by a de-orbiting in the coupled configuration.

Alternative strategies for on-orbit servicing and active

space debris removal are among others the concept of

deploying a net to capture an in-operative satellite or the

Ion Beam Shepherd concept [5], where a target spacecraft

should be de-orbited without physical contact between the

two satellites.

On-orbit servicing therefore contributes to space debris

removal since non-operational, eventually free-tumbling

target satellites in possibly strategic low Earth orbits or in

the geostationary orbit are risks for other active and oper-

ational satellites and for manned spaceflight. As space

debris is a critical issue, concepts for active debris removal

have to be developed [19].

While rendezvous and docking are state of the art in

manned spaceflight (e.g. docking to the ISS with supply

vehicles), new challenges arise in the on-orbit servicing

context. First, a typical target satellite is not specially

prepared for rendezvous and docking in contrast to the ISS.

There are no reflectors or markers which can be used for

navigation and there is no docking or capturing equipment.

Second, the target can have lost its orbit and attitude

H. Benninghoff (&) � T. Boge � F. Rems

German Space Operations Center, German Aerospace Center

(DLR), 82234 Weßling, Germany

e-mail: Heike.Benninghoff@dlr.de

123

Kunstl Intell (2014) 28:77–83

DOI 10.1007/s13218-014-0297-0

control. A freely tumbling target impedes a safe approach

and docking since its movement is not easy to predict.

Third, there is no communication between the two satel-

lites and no precise knowledge of the target’s position and

orientation in contrast to formation flying missions. The

mentioned properties define the term non-cooperative

target.

In our work, we focus on autonomous rendezvous with

no or limited ground intervention. Data of sensors mounted

on the service satellite should be processed on-board and

the entire guidance, navigation and control algorithm has to

be computed by the on-board computer of the servicer. The

autonomy requirement therefore poses a further challenge

on the rendezvous control system.

As a consequence, new concepts and methods compared

to manned spaceflight and formation flying missions have

to be developed for safe rendezvous and docking to the

target in an on-orbit servicing mission. Several studies and

developments have been proposed in the literature. In [14],

a concept called SMART-OLEV is presented for lifetime

extension for geostationary spacecrafts. A space debris

removal method using a small satellite is proposed in [19].

The German Space Operations Center (DLR-GSOC) has

executed a far range rendezvous experiment in the exten-

ded phase of the PRISMA mission [6]. Additionally, sev-

eral works on satellite tracking, guidance, navigation and

control algorithms, as well as coordinated plan and control

of the space robot are conducted by various authors, cf. [7,

18, 21, 22, 24, 27, 30] to mention a few.

In this work, only the rendezvous phase is considered.

The objective of this paper is to provide an overview on

technologies and concepts for autonomous relative navi-

gation developed at the German Space Operations Center.

2 Methods

2.1 Sensors for Autonomous Navigation

For an on-orbit servicing mission, sensors are needed to

estimate the relative position and orientation (pose)

between service and target satellite. Optical sensors like i.a.

mono and stereo camera systems or 3D sensors such as

LIDARs and RADARs (LIght/ RAdio Detection And

Ranging) are suitable sensors for pose estimation during

the rendezvous to a non-cooperative target spacecraft. Also

PMD (photonic mixing device) cameras are theoretical

candidates; however, to the authors’ knowledge, PMD

sensors have never been used in space yet.

We employ monocular cameras for far range rendez-

vous [2, 6]. While tracking a satellite from far range

camera images, we already know a rough direction to the

target. A camera with a high resolution (e.g. 1,024 9 1,024

pixels) and a small field of view (e.g. 10� 9 10�) has to be

chosen such that the number of pixels per degree is large.

This is important to dissolve the target’s projection in the

image plane. Otherwise, the image of the target at several

kilometers distance would cover only a very small fraction

of a pixel and the target would not be visible.

Further attention has to be drawn concerning the sensi-

tivity of the camera. The magnitude of sunlight reflected by

the target satellite decreases quadratically with the distance

to the target. The light of the stars can be even some

magnitudes smaller. The visibility of stars will be impor-

tant if the direction to the target in an inertial reference

system is estimated using data of a star catalog, see

Sect. 2.2.1. Therefore, the aperture diameter and the

exposure time need to be chosen or set large enough

dependent on the operational range such that visibility of

target and stars is ensured. During far range rendezvous,

not the full 6D pose (relative position and orientation)

needs to be estimated. We compute the direction to the

target, denoted as line-of-sight, from sensor data, cf. [2, 6].

At mid and close range, mono cameras with a larger

field of view can be used. At close range, stereo cameras

can be employed resulting in a more accurate pose esti-

mation. In the VIBANASS project [18], a camera system

was developed for far, mid and close range rendezvous. For

ranges [5 m, a monocular camera was used, whereas a

stereo camera was chosen for the last five meters prior to

docking. Both cameras are based on a CMOS chip. 3D

sensors like i.a. LIDARs or PMD cameras generate a 3D

point cloud with distance information and are possible

alternatives to standard cameras at mid and close range.

The full 6D information about the relative position and

orientation needs to be computed at mid and close range.

Standard 2D cameras (CCD/CMOS camera) and 3D sen-

sors (PMD camera, LIDAR, RADAR) serve as appropriate

sensors. Naturally, 3D laser scanners result in a high

accurate distance determination. Lateral motions can be

tracked with high accuracy if a visual camera with high

resolution is employed. Further, visual cameras allow to

detect and track edges of the spacecraft, textured surfaces

or LEDs. The performance of all optical sensors strongly

depends on illumination conditions. Cameras require suf-

ficient brightness which depends on factors such as the

surface material of the target, the incident angle of the Sun

and the observation angle, the size of the target and the

range between the two spacecrafts. The performance of

LIDAR sensors, for example, will severely degrade if high

reflective surfaces (like MLI surfaces) are involved.

The methods described in the following sections are

based on our research on camera navigation. Visual cam-

eras are attractive solutions regarding low weight and low

power-consumption. Further, many spacecrafts are already

equipped with star trackers. Dependent on their location

78 Kunstl Intell (2014) 28:77–83

123

and orientation w.r.t. the spacecraft, they can be addition-

ally employed as far range rendezvous sensor.

2.2 Navigation Methods and Algorithm Design

In this section, we propose methods for relative navigation

which are based on research conducted at DLR-GSOC [1–

3]. We distinguish between far range rendezvous and mid/

close range rendezvous since different objectives and tasks

exist and thus different methods and algorithms are chosen

and developed.

2.2.1 Far Range Rendezvous

The navigation part at ranges[500 m between servicer and

target can be divided in two main steps: (1) Far range

image processing and (2) estimation of the relative orbital

elements by a navigation filter. For the latter, the method of

angles-only navigation [29] can be applied, see also [6].

We do not recall this concept, but concentrate on the image

processing part to measure the line-of-sight to the target

from monocular camera images which is an important

input to the navigation filter.

An image captured with a far range camera consists of a

black background and small bright spots. These clusters of

bright pixels can be stars, the target illuminated by sun-

light, or camera noise or hot spots, see Fig. 1. Hot spots are

pixels which look much brighter because they do not react

proportional to incoming light. The pixels are called hot

because the effect is increasing at high temperature.

Considering one single camera image, one can hardly

distinguish between stars, target or hot spots. In [2], we

developed an algorithm for far range image processing.

The idea of our method is to identify clusters in a given

image, to link clusters between two successive images and

to track them over a sequence of images. Considering the

velocity of the clusters in the 2D image, the target is

identified by motion segmentation: Its motion differs from

the motion of static hot spots and distant stars. In the image

domain, the star clusters have approximately the same

velocity vector, which depends on the attitude change of

the servicer. The motion segmentation is combined with a

star catalog. From the measured 2D position of the clusters

and the attitude of the camera with respect to an inertial

reference system (e.g. Equator J2000), the 3D direction

vector in the inertial reference system is computed and

compared to the true direction of stars given by the star

catalog. Thus, some of the clusters are assigned as stars and

the number of possible target candidates is thus decreased.

Inputs to the far range image processing are the current

image and the current attitude of the servicer on assump-

tion that a rough estimate of the orientation of the camera

with respect to the servicer’s body frame is known. An on-

line calibration resulting in a more accurate attitude esti-

mation of the camera is performed by the developed image

processing software which applies the q-method of

Wertz [28]. If the velocity of the servicer is additionally

available, the estimation of the line-of-sight vector to the

target is improved by performing an aberration correction.

An overview on the single functions and their relation,

inputs and outputs of the system is presented in Fig. 2. For

details on the algorithm, resulting performance and accu-

racy, we refer to [2].

2.2.2 Mid and Close Range Rendezvous

The objective during mid and close range rendezvous is to

compute and track the pose of the target spacecraft.

Example images of a target captured at close range with a

monocular CCD camera (Prosilica GC-655) at different

distances are given in Fig. 3.

Fig. 1 Example of an image of a far range camera (Advanced Stellar

Compass [11]) captured during the PRISMA mission [2, 6]

Servicer Attitude Images

Cluster Detection and Centroiding

Cluster Linking

Hot Spot Detection

Star Detection

Target Detection

Line of Sight Computation

Camera Attitude Estimation

Target XY /LOS Camera Attitude

Servicer Velocity

Aberration correction

Fig. 2 Overview on the algorithm for far range image processing

Kunstl Intell (2014) 28:77–83 79

123

In a first step, the target has to be identified in the given

image. Typically, edge tracking algorithms, which track

the outer edges of the target or inner edges like the nozzle

boundary, are applied. For this task, images from mono and

stereo cameras are employed [3, 20, 26], where the detailed

algorithm depends on the sensor type. Also 3D sensors can

be used [3, 10, 23].

After identification of the target the full 6D pose has to

be estimated. We assume that information about the

geometry and sizes of the target is available, to relate

measurements in the sensor coordinate system with 3D/6D

quantities in a world coordinate system. The transformation

from the 6D pose vector to image/sensor coordinates is

described by a camera model or in general by a sensor

model.

In principle, there are two ways of re-computing the full

6D pose information: One way is to solve an inverse

problem. This can be combined with a least squares fit to

estimate the optimal pose such that mean square error

between real measurements and mappings of the pose via

the sensor model is minimized. Since non-linear models are

typically involved, the Levenberg–Marquardt algo-

rithm [16, 17] can be applied [26]. A second way is to use

an Extended Kalman filter or Unscented Kalman filter [12,

13, 15, 32], which incorporates the measurements in the

filter update/ correction step. We pursued this approach

in [1], where also more details on the vision system and on

the navigation and control method are given. Using Kal-

man filter techniques, the resulting pose estimation is much

smoother compared to pure sensor measurements, see

Sect. 3.1. Further, also quantities like the relative velocity

or angular rate of the target can be estimated simulta-

neously with the pose by a Kalman filter.

3 Verification

Before an on-orbit servicing mission can be launched,

rendezvous and docking sensors and algorithms have to be

intensively tested and verified. Software and hardware

tests, as well as complete hardware-in-the-loop tests can be

conducted. Further, the methods and concepts can be

demonstrated during real flight experiments with semi-

cooperative targets. Semi-cooperative means that we have

full control over a target satellite for safety reasons during

the demonstration, but only the data and information

present on board the service satellite is used for

rendezvous.

Each verification method has intrinsic advantages and

disadvantages. A combination of the verification methods

is recommended in the preparation phase of an on-orbit

servicing mission.

3.1 Hardware-in-the-Loop Simulator EPOS 2.0

The rendezvous and docking process including real hard-

ware like rendezvous sensors and the guidance, navigation

and control system can be verified within a hardware-in-

the-loop environment like the European Proximity Opera-

tions Simulator (EPOS 2.0). It is a robotic based test

facility for physical, real-time simulations of rendezvous

and docking maneuvers [4].

The testbed consists of two industrial manipulators with

each 6 degrees of freedom such that each robot can sim-

ulate the 6D dynamic behavior of a satellite in space. One

robot is mounted on a linear slide of 25 m length which

enables a simulation of the last 25 m of the rendezvous

phase and the contact dynamics simulation of the final

docking process. Using scaled models, simulation of larger

distances is also possible. The simulation of sunlight illu-

mination generates a realistic environmental scenario.

Another feature of the facility among others is the

dynamical capability which allows a high commanding rate

of 250 Hz, a prerequisite to perform contact dynamics

simulations. Recently, an extension to two endlessly

rotating axes has been implemented such that tumbling

target motions can be completely simulated.

We performed several hardware-in-the-loop simulations

using EPOS 2.0 for test and verification of complex real-

time embedded systems like rendezvous sensors [1]. The

Fig. 3 Example of images showing an illuminated target captured with a mono, CCD camera and marked detected edges (blue dots). (Color

figure online)

80 Kunstl Intell (2014) 28:77–83

123

embedded system was connected to its numerical coun-

terpart simulating the real environment of the sensor which

cannot be represented with real hardware (e.g. orbital

dynamics).

A typical closed loop testing scenario is as follows: A

rendezvous sensor is mounted on one robot of the test

facility which simulates the translational and rotational

motion of the service satellite. The second robot carries a

typical mock-up of a target satellite and simulates the

motion of the target satellite. The rendezvous sensor

measures the relative position and orientation between the

robots. The measurements are processed in real-time by

guidance, navigation and control algorithms which will

later run on the servicer’s on-board computer. A controller

computes commands for the thrusters and reaction wheels

based on the measurements, the navigation result and the

guidance values. The commands are fed to a real-time

numerical simulator which simulates the actuators and the

environment of the satellites. For the next sample, an

update of the state vector (position and orientation) is

computed using the control forces and torques as input

vectors for kinematic and dynamic equations of motions.

The robots of the EPOS facility move according to the

computed new state. Figure 4 shows a typical setup at

EPOS 2.0 and provides an overview on the control loop

including hardware elements, on-board software and the

numerical simulator.

Controlled approaches from a distance of 20–3 m have

been performed on EPOS using a monocular CCD camera

as rendezvous sensor (recall Fig. 3), an Extended Kalman

filter for navigation and a PID controller. Figure 5 shows

the measurement and filter error observed during one

approach with a CCD mono camera. The Kalman filter

estimates significantly smooth the raw measurement data.

The numerical simulator for these tests is based on Hill’s

equations [9], Euler equation and quaternion differential

equation [28]. The equations of motion describe the orbital

and rotational kinematics and dynamics. In [1], we have

shown more details and results of hardware-in-the-loop

rendezvous simulations.

3.2 Camera Simulator

The applicability of a test facility like EPOS to verify the

far range rendezvous process is limited. To simulate ren-

dezvous phases with several kilometers distance between

service and target spacecraft, we use a software-based

camera simulator. We have generated images of a simu-

lated far range camera using the camera simulator devel-

oped by Astos Solutions GmbH. It is possible to define

physical cameras with special effects like depth of field, to

include geometry files of the spacecrafts and to generate a

motion including realistic orbit and attitude data. For a

future mission, a complete approach can be simulated and

image processing algorithms can be tested by applying

them on the generated simulated images.

Thus a camera simulator can bridge the gap between

tests on terrestrial facilities and real flight demonstrations.

All kinds of motions and full orbits with eclipse phases,

etc. can be simulated. Although a camera simulator cannot

replace a real sensor since visual effects and noise are

difficult to simulate, it can be used to test image processing

and navigation systems in early design phases.

3.3 Flight Experiments

Terrestrial tests and software simulations provide only

limited information about the performance of a sensor and

the guidance, navigation and control system under space

conditions. Before a real on-orbit servicing mission with a

non-cooperative, partly damaged target can be started, tests

can be performed during missions where semi-cooperative

targets are involved.

DLR-GSOC has demonstrated far range rendezvous

from 30 to 3 km during the ARGON experiment

(Advanced Rendezvous demonstration using GPS and

Controller

Guidance

NavigationFilter

+

-

GNC Functions

ThrustersWheels

Actuators

Dynamics& Kinematics

Disturbances

Forces & Torques

RendezvousSensors

State (Position, Attitude)

Sensor System

Spacecrafts’Dynamics, Kinematics and Environment

= real hardware

= numerical simulator

= on-board computer

EPOS test bed(manipulators)

Fig. 4 The EPOS 2.0 robotic test bed (left) and control loop for hardware-in-the-loop rendezvous simulation [1] (right)

Kunstl Intell (2014) 28:77–83 81

123

Optical Navigation) in the extended phase of the PRISMA

mission [6]. PRISMA is a formation flying mission in low

Earth orbit involving the satellites MANGO (Main) and

TANGO (Target). The data of TANGO was only moni-

tored for safety reasons and stored for post-analysis and

error computation. The experimenters did not know the

target’s position during the entire demonstration and used

only GPS data of MANGO and angles-only measurements

(see Sect. 2.2.1) from images captured by a monocular

camera. The experiment was ground based, i.e. after each

ground contact, the telemetry data of the last orbit was

downloaded and processed. At the next ground contact,

maneuvers were commanded. Thus, there were no real-

time requirements for the navigation algorithms. In the

next step, such experiments have to be performed on board

a satellite.

With flight experiments like ARGON, experience can be

gained concerning the rendezvous under space conditions.

Real flight experiments are time- and cost-intensive. Rep-

etitions of experiments to test for example a large number

of different lightning conditions or approach strategies are

usually limited or impossible. Therefore, there is still a

need for realistic hardware and software simulations.

4 Conclusion

We have presented our work on far range and close range

rendezvous based on optical sensors, presented possible

navigation algorithms and verification methods.

Although a variety of algorithms for different tasks

exists, there is still a large research area in the field of pose

estimation and relative navigation for both far range and

close range rendezvous to a non-cooperative target.

The verification process is a non negligible part in the

preparation of an on-orbit servicing mission. Ideally, a

combination of software, hardware, hardware-in-the loop

and real flight tests can be performed to verify a sensor and

a navigation system for a safe rendezvous.

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−0.2

0

0.2

0.4

x in

[m]

5101520−0.1

0

0.1

y in

[m]

5101520−0.1

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Heike Benninghoff is a junior

researcher in the On-Orbit Ser-

vicing team at DLR-GSOC in

Oberpfaffenhofen, Germany.

She received her diploma

degree in mathematics in 2010

from the University of Regens-

burg. Her research interests

comprise visual navigation,

image processing, numerical

analysis and HiL simulation.

Toralf Boge received his Ph.D.

in electrical engineering from

Technical University Dresden in

2003. Since 2000 he works for

DLR at GSOC. He is team lea-

der of On-Orbit Servicing team

at GSOC. His areas of interest

are sensors and GNC system for

rendezvous and docking and

HiL simulation.

Florian Rems got his diploma

in aerospace engineering from

the Technical University of

Munich (TUM) in 2012. He is a

research associate in the On-

Orbit-Servicing group at the

German Space Operations Cen-

ter (GSOC). His work focuses

on LIDAR technology for

autonomous rendezvous navi-

gation and on RvD simulation.

Kunstl Intell (2014) 28:77–83 83

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