Conference PaperPDF Available
UAV Guidance with Robotic Total Station for
Architectural Fabrication Processes
Artyom Maxim | Otto Lerke | Marshall Prado | Moritz Dörstelmann |
Achim Menges | Volker Schwieger
1 Introduction
ere is a dramatic change in the conception of architectural production. De-
veloping technologies allow for a higher degree of structural and material ef-
ciency in building construction. rough the integration of novel fabrication
techniques, more emphasis is placed on dierentiated material arrangements
and tailored structural performance. Lightweight, high performance materials
for architectural fabrication empower this change of production and allow for an
increase of functional integration and design possibilities.
Advances in computational design and robotic fabrication techniques have
enabled more automation in the building process, which has recently increased
in importance on construction sites (Meyer 2003). e developments in auto-
mation yield benets regarding the reduction of expenses and the increase of
eciency (Heikkilä and Jaakkola 2003, Gläser etal. 2008). Robotic fabrication
provides more control and adaptability during construction, which has led to the
development of novel production processes.
Lightweight, high performance architectural systems are no longer con-
strained by traditional construction processes, which rely on assemblages of
parts. ese systems can be made onsite at full scale with continuous material
application, eliminating the need for joints that can weaken the otherwise e-
cient structural system. is requires an equally advanced building system capa-
ble of such possibilities as integrated material constraints, fabrication limitations
and structural performance. is paper will discuss some of the developments
required for an aerial fabrication system of a lightweight ber composite struc-
ture. e focus of the paper is the guidance and control of a UAV for ber wind-
ing, utilizing a robotic total station. is enables the fabrication of long span
brous structures in an onsite construction environment.
Previous research at the University of Stuttgart’s Institute for Computation-
al Design (ICD) and Institute for Building Structures and Structural Design
(ITKE) has focused on developing novel fabrication techniques for ber com-
posite building construction. In the ICD/ITKE Research Pavilion 2012 a coreless
winding technique was developed for an onsite fabrication setup (Reichert etal.
2014). An external turntable was used in cooperation with the six axis industrial
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robot to expand the working space of the robotic arm. rough this on-site set-
up, a large scale, continuous ber composite shell was fabricated. e ICD/ITKE
Research Pavilion 2014-15 maximized the reach of the robot by fabricating a
composite shell, in situ, from the inside under low tension (Schieber etal. 2015).
In both examples the highly performative material system was built up incre-
mentally by laying continuous lightweight ber rovings. e interaction of the
bers and the self-forming nature of the composite surface allows for high tol-
erances in production processes. e high strength and precision of the robotic
arms were useful where the tension of the ber and accuracy of winding needs
to be controlled but their limited range of motion and scale constrained the ma-
terial system to relatively small scale architectural demonstrators.
Aerial vehicles provide one possible solution to the limitations of industri-
al robotics in architectural production of ber composite structures. ey are
untethered and have a less constrained range of motion. Aerial vehicles have
already been used in the construction industry for lming and surveying tasks
(Burger etal. 2016, Rose 2016), but have had limited use in construction pro-
cesses for material and environmental interface. ere is a growing eort to
change this through developing architectural research. Several projective pro-
jects at the ETH Zurich (Mirjan etal. 2013), University of Pennsylvania (Maho-
ny etal. 2012) and the Architectural Association in London have utilized aerial
vehicles in novel construction systems. ey have shown that aerial vehicles can
pick, place and pull objects having a limited payload within a controlled pro-
duction environment. Materials such as rope have been used to fabricate tensile
structures, showcasing the potential use of aerial vehicles for ber composite
construction. ese examples demonstrate the inherent exibility and agility of
micro aerial vehicles in interacting with environment and set course towards the
new, exciting applications in architectural design and construction. Although
these examples made quite remarkable developments in UAV navigation within
a controlled fabrication environment and showed the interaction with objects
and materials, it is still to be demonstrated how these processes can transition
from the controlled lab-like environment to a geometrically complex, onsite or
in situ construction environment.
In order to transition from the controlled lab environment to the onsite fab-
rication setup, dierent localization strategies may be required. e positioning
systems employed for the afore mentioned for the control of the aerial vehicles
can be unsuitable for onsite construction environments. Motion capture setups,
enhanced GPS tracking (RTK-GNSS) and onboard SLAM algorithms, common-
ly used setups for localization of aerial vehicles, are useful in some construction
environments but may encounter problems with signal shadowing, reectance,
magnetic interference, light pollution or feature recognition when applied to
onsite construction.
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is paper provides an alternative method for aerial vehicle localization and
control utilizing standard construction equipment suitable for onsite fabrication.
Robotic total stations are portable, robust machines capable of accurate position
estimation in various indoor and outdoor conditions over large distances. ey
are useful for site survey-
ing, digital scanning and
motion tracking. Heavy
machinery such as grad-
ers and pavers have used
robotic total stations for
the localization and con-
trol on construction sites
(Fig.1 and Fig.2).
Robotic total stations
have been used for track-
ing of aerial vehicles (Fitz
2013), though guidance
of UAVs by tachymetry
is not common. Never-
theless tachymetric guidance of UAVs may become more prominent if demands
on accuracy rise and operational scenarios become complex, as with onsite con-
struction of ber composite structures. Furthermore this positioning method is
reliable for operation even in challenging environmental, weather and lighting
conditions, where other methods might not be suitable. e given architectur-
al fabrication task requires an optimal performance during ight operations in
exible environments. Regarding the technical implementation of tachymeter
guidance, the existing machine guidance methods used for construction equip-
ment can be transferred to UAV applications with slight modications, which
will be shown in the following chapters.
Fig. 1:
3D Control, Exam-
ple of a Mainline
Paver
Stempfhuber 2008
Fig. 2: Road Constructions (Certus Verlag AG, Contribution
by Terradata AG, last access on 13.09.2016)
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2 UAV – Guidance System
2.1 Description of the UAV
A custom aerial vehicle was built using both o the shelf electronic and propul-
sion components and custom made frame parts. e brushless motors, motor
controllers, battery and propellers, used for the propulsion system, are standard
for self-built quadrotors. e car-
bon bre plates and aluminium
proles were used for the frame
(Fig.3). Electronic setup includes
an autopilot board, onboard com-
puter, several radio and power
modules, USB-serial converters.
e autonomous vehicle op-
erates using two main hardware
components – the autopilot and
the onboard computer. A “Pix-
hawk” autopilot platform, with
“PX4” rmware (Fig.4), was cho-
sen as the core of the system, as
it has a growing developer community, a substantial online documentation and
has a completely open source hardware and soware system. e Pixhawk is
an embedded computer system with integrated MEMS based inertial, magnetic
and barometric sensors and hardware interfaces for controlling an autonomous
aerial vehicle. e PX4 is “a node-based multithreaded open source robotics
framework for deeply embedded platforms” (Meier etal. 2015). Pixhawk is used
in conjunction with the onboard computer “Odroid XU4”, which is an ARM
single board computer that is running Ubuntu, with Robot Operating System
(ROS). ROS is framework for programming and controlling robots, machines
and systems of any kind (ROS 2016). It provides all necessary tools for creating
complex and reliable computerized systems. In this project, ROS is used as a
high level control mechanism for the vehicle. It is able to run navigation algo-
rithms, control packages with advanced logic and external interfaces and serves
as a bridge to the autopilot.
e power system includes a 5Ah battery, two power modules, a 5V5A power
supply for onboard computing and a Pixhawk power module, which not only
provides power to the autopilot, but also data about the current voltage and cur-
rent state of the battery. An onboard WiFi adapter is also included for ground
computer connection, accessing the terminal of the onboard computer, upload-
ing code and ight programs and observing autopilot state. Additionally two
Fig. 3: UAV
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radio modules – FrSky radio receiver for manual remote control and Laird
RM024 2.4 Ghz with ½ wave 2dBi antenna are installed. A pair of RM024 radio
modules were used to robustly transfer localization data from the ground com-
puter, connected to the total station, to the onboard system of the aerial vehicle.
e ight range is only limited by the radio receivers. e operating distance
is around 1 km. e ight time on one battery charge is 10to 15minutes. e
payload is 500grams.
2.2 Guidance with Robot Total Station
In this work a Leica MS60 robotic total station has been used as a positioning
sensor. MS60 is a state of the art robotic total station. It provides position accu-
racies of 3to 5 mm in kinematic mode (Leica 2016) and thus complies with high
accuracy demands. e MS60 can be dened as a multi-sensor system. Besides
including several sensors to measure temperature, pressure, humidity, the to-
tal station also includes relevant sensors for position determination, horizontal
and vertical angle measurement, as
well as electronic distance measure-
ment, the EDM. e most impor-
tant features for this investigation
is the Automatic Target Recogni-
tion (ATR) sensor which allows to
recognize and track the target. For
each measurement the station pro-
vides X, Y, Z-coordinates. In this
investigation the total station has
been used in combination with a
360° prism Leica GRZ101 (Fig.5).
e instrument provides several
useful tools for kinematic tracking
Fig. 4:
Pixhawk
Auto pilot
(left), Odroid
XU4 (right)
Fig. 5: Multi Station Leica MS60 (left), Leica
360° prism GRZ 101 (right)
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including: Dynamic Lock and Power Search functions (Leica 2016), good target
holding quality, high tracking rate of 20 Hz, low rate of data gaps, tolerance to
target occlusion and an enhanced target prediction phase. e limitations are the
tracking distance, the general problem of retention of the line of sight, valid for
all tachymeter applications and unfavourable atmospheric conditions.
2.2.1 Transfering Estimated Positions to the PX4 Autopilot
e guidance methods are implemented using the robot operating system (ROS)
platform programming framework. e system consists of several ROS nodes,
running on both, ground and onboard computers and connected by a radio link.
e “Tracking” node communicates with the tachymeter using a serial connec-
tion to receive the X,Y, Z-coordinates of the measured prism position, mounted
on the vehicle. e implementation of this node is based on an existing open
source Python library for communicating with Leica total station instruments
using the “GeoCom, an ASCII based request-response protocol, which makes
it compatible with many computer platforms, e. g. microcontroller, smartphone
or a PC. Aer receiving coordinates from the “Tracking” node, a “Sender” node,
passes the data via serial connection to the RM024 radio module, congured
as a “Server” to transmit position information to the onboard computer of the
UAV. e data gets received by the “Client” RM024 module, read by the ROS
“Receiver” node and shared for other onboard ROS system nodes, for use. e
coordinates can then be accessed by a main ROS “Control” node, which handles
safety checks for data consistency and controls the execution of custom ight
instructions. e “Control” node passes measured real-time position and com-
mands to the “Mavros” node – a ROS package, which provides simple means of
communication and control of the aerial vehicle. Using Mavros, the “Control
node can obtain and utilize any data from the autopilot, including its current at-
titude, state, mode, battery charge level and multiple others. Mavros then passes
commands to PX4 autopilot over a serial connection, which in turn controls the
ight behaviour of the vehicle. Additionally, the “Rosbridge” node on the ground
computer passes measured coordinates to a visual scripting interface, integrated
with a common architectural CAD soware, which is able to interactively dis-
play, store and evaluate trajectories of the ight path (Maxim 2016). e main
function of this soware within this project is the real-time monitoring of ight
operations on the ground computer.
2.2.2 Integration of Position Data into Autopilot
PX4 Autopilot is able to use externally measured position data for localization, in
relation to its initial origin, which resets to zero at startup. In the developed set-
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up, a local position estimator (LPE) soware module of autopilot is used, which
is responsible for processing the position data from internal or external sensors.
LPE is based on an extended Kalman lter algorithm, oen used for nonlinear
discrete systems. e Kalman lter is a set of mathematical equations that pro-
vides an ecient computational (recursive) means to estimate the state of a pro-
cess, in a way that minimizes the mean of the squared error. e lter is powerful
in several aspects: it supports estimations of past, present, and even future states
by prediction, and it can do so even when the precise nature of the modeled
system is unknown (Welch and Bishop 2004). LPE is capable of fusing multiple
position sources, such as GPS, Motion Capture or Computer Vision, potentially
improving the precision of the resulting estimate, however, in the presented set-
up, only the robotic MS60 Multi Station has been used, as it alone provides posi-
tion estimates for moving targets with millimeter precision. From LPE estimator,
the position data gets passed on to another soware module of PX4 autopilot,
the “Position Controller”. It is based on a proportional-integral-derivative (PID)
controller method and is responsible for calculating the amount and direction
of force that the vehicle needs to produce in order to reach the destination tar-
get setpoint. e position controller has multiple, separate closed-loop systems,
controlled by PID controllers, for dierent aspects of the motion of the vehicle,
e. g. vertical and horizontal movements as well as velocities. ese controllers
need to be tuned properly, for achieving acceptable motion accuracy of the ve-
hicle. If the controllers are untuned, the vehicle tends to oscillate around a target
setpoint, never actually reaching it. In worst cases, the amplitude of oscillations
will increase and the vehicle becomes completely unstable. In this project, the
PID controllers were tuned using an heuristic approach, which has proven to be
sucient for eliminating oscillations. Detailed description on the PID controller
theory and tuning can be found in Mann etal. (2005) or Busch (2012). e out-
put from the position controller is passed to the “Attitude Controller, a soware
module, which gets the attitude information (roll, pitch yaw) from an onboard
IMU and is as well based on a PID controlled closed-loop system. e attitude
information is then passed to the “motor mixer” and the “motor driver” soware
modules, which control the speed of the rotors.
2.2.3 Local Reference Frame Orientation
An important point is achieving correct reference frame orientation of the total
station. is coordinate frame can be established by the “free stationing” tech-
nique. e autopilot utilizes an internal compass sensor, for determining its ori-
entation heading in space. e autopilot navigation frame is North-East-Down
(NED), which means its X, Y axes are pointing along the magnetic North, East
directions respectively and the Z axis points perpendicular to the other axes,
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downwards. By convention, ROS operates in another coordinate frame, the
ENU (East-North-Up). erefore the coordinates form the instrument should
be converted to ENU format. e Mavros node (Fig.6) is responsible for con-
verting ENU coordinates to the NED convention and passing them to the PX4
Autopilot. e information about the vehicle’s orientation heading is provided
by an onboard magnetometer and is the main source of the heading informa-
tion. is sensor, which measures the magnetic eld of the earth to dene the
orientation of the platform, is easily aected by various magnetic disturbances,
caused by onboard power cables, motors and even by large metallic objects in
the surrounding environment. In this case, the onboard compass heading could
be suciently distorted during the ight operation and become unreliable or
imprecise. e resulting eect is a misorientation during the ights causing de-
viations from the desired trajectory. In this context the problem of an insucient
heading determination of the UAV might be present in some cases, but wasn’t
addressed in this investigation, as the performed ights have shown that slight
misalignment of the frames by few degrees is not critical for the given applica-
tion. During the performed experimental ights only small deviations from the
expected trajectory during the vehicle’s movement could be detected. e solu-
tion for this circumstance would be an additional orientation estimation source,
e. g. a computer vision system, use of the reference trajectory or the use of the
eective trajectory sections according to Beetz (2012).
2.2.4 Design and Execution of Custom Flight Trajectories
e direct control of vehicle behaviour and functionality is achieved using a cus-
tom “Control” node as described previously (Fig.6). is node is a functional
Fig. 6: Guidance System Architecture
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core” of the system, allowing for the execution of ight programs in an auto-
mated mode, stored onboard. It serves as a communication hub as well, feeding
commands and position measurements to the autopilot and reading vehicle sta-
tus and passing the information on to the user. e ight trajectories are pro-
cessed by the visual scripting interface, to generate a custom-formatted code,
consisting of an index, position and orientation information for every point
along the ight path. e ight code generation is tightly connected to the de-
sign workow, where the ight paths can either be drawn manually in a CAD
soware, or produced automatically from the computationally dened lament
winding order. is ight program is then sent and stored in the onboard “Con-
trol” node and is later executed line-by-line during the ight. At startup there is
a discrepancy between the internal local position, dened as zero by autopilot
rmware, and its real-world position, dened by the measurement in the coor-
dinate frame of the total station. erefore, one of the “Control” node tasks is to
oset both, measured position and trajectory targets, by the initial startup world
position oset, which allows to coordinate everything to the same superordinate
reference frame. e oset must be considered for a 3 dimensional case. e
target setpoints, the measured prism position, as well as the architectural fab-
rication model inside CAD soware can now be represented in the ENU world
frame, making the system easy to use by avoiding unnecessary frame rotations.
3 Test Results
e guidance system was tested in context of a full-scale lament winding exper-
iment. It is based on a freestanding conguration of four winding frames, with
one 4 m high in the center and three 1.5 m high, equally spaced around, to form
a triangle with a side of 7 m (Fig.7). Aer arranging the frames, the MS60 Total
Station was used to scan the environment to nd both, the surrounding bound-
aries of the fabrication space and the precise position of the frames themselves.
is allowed the construction of the ight trajectories around measured objects
in the CAD soware. e resulting ight path is then converted into custom-for-
matted instructions for the vehicle and uploaded to the onboard system. Aer
the total station guidance has been set up and the vehicle was receiving its meas-
ured position, the user activated the automatic ight mode for the quadrotor to
start executing the program.
Fig.8 exemplarily depicts the eective ight path and the reference ight path
between the waypoints.
In order to evaluate the control quality for the tachymetric guided UAV, the
3Ddistances between reference and eective ight path have been analysed. is
approach was inspired by 2Devaluation approaches, e. g. represented in Lerke
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und Schwieger (2015) or Beetz (2012), but has been adapted for a 3Dscenario.
e adapted method analyses the shortest distance between the measured posi-
tions at timestampi and the reference ight path. To determine this distance a
cross-product approach has been used to calculate the plumb line through the
measured position perpendicular to the reference ight path. Subsequently the
length of the plumb line has been calculated and analysed. Details on cross-prod-
uct theory can be found at e. g. Merziger and Wirth (2010).
In order to examine the control quality, minimum and maximum distances,
as well as the mean value and the root mean square (RMS) have been calculated.
e formula for the RMS is given below:
Fig. 7: Experiment Site and Construction Object
Fig. 8: 3-D Flight Path (shown exemplarily for one round)
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¦2
i
d
RMS
n
(1)
222
()()()
REF EFFECTIVE REF EFFECTIVE REF EFFECTIVE
iii ii ii
dXX YY ZZ
(2)
di – lenght of the plumb linei, nnumber of measurements,
RE
F
i
X
,
RE
F
i
Y
,
RE
F
i
Z
– coordinate triple for pointi of the reference trajectory,
EFFECTIC
E
i
X
,
EFFECTIV
E
i
Y
,
EFFECTIV
E
i
Z
– coordinate triple for pointi of the effective
trajectory.
Tab. 1 shows the summarized results on the above described evaluation method.
e histogram in Fig.9 shows the distance sample distribution for the com-
plete recorded ight path. e y-axis represents the amount of samples, the
x-axis represents the calculated distances. e histogram reveals that over 40 %
of the sampled distances are below 100 mm. Around 25 % of the samples are
between 100 mm and 200 mm and the remaining 35 % are between 200 mm and
1,000 mm. e results show a wide value range and therefore a wide range of the
resulted control quality.
Tab.1: 3D Flight Path Evaluation
Section of the Flight
Path
Min. Distance
[mm]
Max. Distance
[mm]
Average
[mm]
RMS
[mm]
1 4 967 273 371
2 6 919 195 284
3 3 906 262 352
Complete (1 + 2 + 3) 3 967 235 329
Fig. 9: Distribution of 3Ddistances to reference trajectory for complete ight path;
number on bars equal to number of samples
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To take a closer look
on the situation, the ight
movement has been sepa-
rated into a horizontal and a
vertical component. Firstly,
an evaluation of the hori-
zontally projected trajectory
has been conducted. Fig.10
depicts a ight path section
with the reference ight
path, the eective ight path
and the plumb foot points,
projected on the x-yplane.
e numerical values for
the horizontal component of
the movement are summarized in Tab.2. e values show, that the lateral devia-
tion of the eective ight path is in the range between 0 mm and 300 mm. us
the guidance quality, referred to the x-yplane, is approximately three times better
than for the 3Dcase. e histogram in Fig.11 depicts the samples distribution
Tab. 2 : 2D ight path evaluation (referenced to x-y plane)
Section of the Flight
Path
Min. Distance
[mm]
Max. Distance
[mm]
Average
[mm]
RMS
[mm]
1 1 303 96 129
2 <1 296 80 99
3 <1 186 65 82
Complete (1 + 2 + 3) <1 303 81 104
Fig. 10: 2-D Flight Path, projected to x-y plane
Fig. 11: Distribution of 2Ddistances to reference trajectory, projected to x-yplane;
number on bars equal to number of samples
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of the plane based method. e histogram shows that approximately 70 % of the
overall distances are below 90 mm.
For the completion of the analysis, the evaluation of the vertically projected
trajectory has been performed. Fig.12 shows the 2-Dight path, referenced to
the y-zplane. It is noticeable from Fig.12, that the eective ight path exhibits
large vertical deviations from the reference ight path. Tab . 3 summarizes the
numerical values for the vertical component of the ight movement.
e analysis shows, that the vertical component of the ight movement has
the greatest impact on the examined distances. us, the altitude control (along
z-axis) has the worst performance. e histogram in Fig.13 shows, that only
40 % of the calculated distances are smaller than 100 mm.
Fig. 12: 2-D Flight Path, projected to y-z plane
Tab. 3 : 2D ight path evaluation (referenced to y-z plane)
Section of the Flight
Path
Min. Distance
[mm]
Max. Distance
[mm]
Average
[mm]
RMS
[mm]
1 0 966 260 361
2 0 916 181 273
3 0 901 251 344
Complete (1 + 2 + 3) 0 966 222 319
Fig. 13: Distribution of 2Ddistances to reference trajectory, projected to y-zplane;
number on bars equal to number of samples
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According to the datasheet of the tachymeter, the accuracy is 3 mm to 5mm
for positioning in kinematic mode and thus its inuence on the resulted guid-
ance accuracy is neglectable. e values from Tab. 1 , Tab.2 and Ta b. 3 comprise
frame misalignment, PID controller parameter settings, as well as ight dynami-
cal aspects and further unknown inuences. Especially the z-component, under-
lying the gravitational forces, has the largest share of the reduction of guidance
quality.
4 Conclusion and Outlook
e guidance of UAVs by robotic total stations is possible and enables the ex-
ploration of novel large scale on site automated fabrication processes for archi-
tectural structures. e conducted experiments have shown, that tachymetric
guided UAVs fulll the quality requirements for fabrication of large scale on site
construction on the example of a lament structure.
For the 3Dcontrol quality a RMS of 329 mm could be detected for the com-
plete ight path. e horizontally projected evaluation of the control quality, ref-
erenced to the x-yplane, revealed a RMS of 104 mm and the vertically projected
evaluation, referenced to the y-zplane, indicated a RMS of 319 mm. Although
these results suggest a marginal control quality, they comply with the given re-
quirements for core-less lament winding methods. is method allows for a
relatively large tolerance range, as the accuracy of the fabricated piece is deter-
mined by the winding frame and is not a direct representation of the ight path.
e reciprocal constraints of payload, drone size, battery capacity and ight
time dene boundary conditions for the presented fabrication method. Future
trajectories of development towards a specic fully functional fabrication meth-
od for large scale ber composite parts could include stationary positioning of
the ber spool, thus the spool is not part of the payload. In this scenario the
UAV is mainly utilized as a ber guidance and positioning vehicle. is would
allow for a more continuous fabrication process, as the reduced payload allows
for longer ight time and a ground supported spool could be signicantly larger,
requiring less frequent spool changes. A second aspect relevant for scaling of this
technology is the balance between slack and tension force in the ber roving.
While UAVs allow for low tension stringing of lament over large distances with
a comparatively low level of precision, a robotic arm allows for high precision
positioning of lament with a substantial amount of pre-tension within a limit-
ed operation space. Combination of industrial robots and UAVs render a very
promising future opportunity, as it combines both the UAVs benet of a large
operation space with local robotic precision and strength to pre-tension bers
over long distance.
158 DVWSCHRIFTENREIHE p Band 86/2017
Maxim et al., UAV Guidance with Robotic Total Station …
© Wißner-Verlag
Beyond the specic application for large scale fabrication of lament struc-
tures, the general control and localization method using a robotic total station
has wider elds of application in automated construction processes. For the
emerging research eld of mobile fabrication agents, a robust localization sys-
tem can be an important key technology to enable coordination of fabrication
robots oen proposed to be utilized in large quantities with semi-autonomous
group behaviour. Beyond positioning of fabrication machinery, total stations can
fulll multiple tasks, e. g. tracking of the fabrication process, thus allowing to
inform the fabrication agents behaviour as an inline control mechanism, or nal
measuring and quality control.
Sensor integrated fabrication strategies, such as total station enabled aerial ve-
hicle localization and control, set the base for adaptive modes of fabrication that
allow for both, higher accuracy and control in known processes, and exploration
of novel fabrication strategies.
Acknowledgements
Funded by Volkswagen Foundation.
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Kontakt
Artyom Maxim | Marshall Prado | Moritz Dörstelmann | Achim Menges
Institute for Computational Design, University of Stuttgart
Keplerstrasse 11, 70174 Stuttgart
artyom.maxim@gmail.com
marshall.prado@icd.uni-stuttgart.de
moritz.doerstelmann@icd.uni-stuttgart.de
achim.menges@icd.uni-stuttgart.de
Otto Lerke | Volker Schwieger
Institute of Engineering Geodesy, University of Stuttgart
Geschwister-Scholl-Strasse 24D, 70174 Stuttgart
otto.lerke@ingeo.uni-stuttgart.de
volker.schwieger@ingeo.uni-stuttgart.de
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