SAMPE Europe Conference 2021 Baden/Zürich - Switzerland
SYSTEM CONCEPT FOR HUMAN-ROBOTCOLLABORATIVE DRAPING
Christian Eitzinger
Profactor GmbHIm Stadtgut D1
AT-4407 SteyrGleink

Christoph Frommel
DLR
Am Technologiezentrum 4
DE-86159 Augsburg

Stefano Ghidoni
DEI – Universityof Padova
via Gradenigo 6/B
IT-35131 Padova

Enrico Villagrossi
CNR-STIIMA
Via Alfonso Corti 12
IT-20133 Milano

ABSTRACT
Draping is a technically complicated process as it involves the placing of a flat piece of carbon
fiber fabric in a 3D mold, without creating wrinkles, maintaining an accurate position andensuring correct fiber orientation. Currently, this process is either done fully manually or – insimpler cases – fully automatically through robots or application-specific handling devices. In
this paper a system concept is proposed that allows human-robot collaborative draping. Basedon typical use cases from the aerospace, boat-building and automotive industry various
interaction modes are identified, including the joint transport of large patches of material, theplacing of the material and the draping of areas of low and high curvature. The analysis ispresented in the form of a prototypical robotic work-cell including the sensor systems as wellas the real-time software architecture to allow a smooth and efficient interaction between thehuman and the robot.

1. INTRODUCTION
1.1 Industrial draping and human robot collaboration
Draping is the process of placing soft and flexible patches of textile material (in the applications
considered in this papers it is carbon or glass fiber fabric) on a 3D shape during the
manufacturing of carbon fiber composite parts. Draping remains very important as analternative to automated fiber placement and tape laying due to its flexibility and its
compatibility with a wide variety of fabric materials. As a result, 30% of aerospace composite
parts [1] are still produced through draping, while many automotive and almost all marine
structures are made this way. At present, most draping is done manually, with only 5% of
aerospace composite parts using some automated draping process.
The automation of draping processes often uses specifically designed handling systems thatpick up a patch of material and either place it directly on the mold or in a frame that holds thepatch in place while it is lowered on the mold [2]. Sometimes the edges of these frames are
modified to change friction and to better control the draping process. Robotic draping has been
investigated in [3]. The main challenge is to deform the flat material into a 3D shape that
resembles the shape of the mold. Various kinematic structures with a varying degree of
flexibility have been introduced [4], but there are often limitations in terms of curvature thatcan be achieved. The draping of tight corners, flanges or U-shaped structures is difficult withsuch flexible gripper systems, unless application- and geometry-specific designs are made.
In order to obtain a higher degree of automation, a combination of human and robotic
capabilities will be a solution that combines the dexterity of humans, when draping small, highcurvature areas and the precision and higher reach of robots when draping the larger, less1

SAMPE Europe Conference 2021 Baden/Zürich - Switzerland
curved areas. To enable an efficient collaboration process, human and robotic capabilities need
to be tightly and seamlessly integrated. This is a substantial extension over situations, where ahuman and a robot are just operating within the same workspace, but on independent tasks.
Also, trust [5] needs to be investigated, as – due to the typical size of the parts – the robot will
inevitably have a significant size and payload.
1.2 Typical requirements in boat-building, automotive and aerospace applications
The degree of automation varies in the different industries, and directly correlates with the
manufacturing volume. In the automotive industry RTM (Resin Transfer Molding) processes
are most common and parts are of medium size with up to 2m along the major dimension. The
range of materials is increasing, often dry material in combination with infusion processes isused. For large production numbers the draping process is automated, with specificallydesigned equipment. For smaller and medium size batches, such as in sports car, the draping isdone manually and also includes e.g. prepreg material. In the aerospace industry manual
draping is used for medium size parts, especially for spars, ribs and similar parts with highlocal curvature. Manual work is often supported by projection systems to help with thepositioning of the single patches. In boat-building the draping is a purely manual process, with
two or three persons involved when draping larger patches of material, e.g. during theconstruction of a boat hull. Materials include glass fiber for the leisure craft and carbon fiber
for racing equipment.
Independent of the particular materials and processes used, requirements for draping processes
always include the accurate placement of the material and controlling the distortion and reorientation of the fibers when the material adapts to the 3D shape. The main defects that arefound during draping include wrinkles, bridging and foreign objects.
1.3 Challenges for human-robot collaboration
Combining the skills of humans and robots in draping processes creates several challenges thatneed to be addressed when designing such systems.
The robots used for draping will be full scale, noncompliant robots due to their size and due to the
payload required to carry the - often heavy - grippers.
Collaboration can thus be ‘scary’ for the humanoperator and aside from setting up appropriate safety
devices also a certain level of trust is needed to enable
the collaboration. The main challenge is to find abalance between efficiency and safety.
Fig. 1 Collaborative handling of a
Collaborative draping also includes situations, where patch of material.
the robot and the human are in direct collaboration andneed to jointly complete the task, such as transporting a large patch of material (Fig. 1). This
leads to hard real-time constraints as the robot needs to react immediately to the actions of thehuman. Also, there must be a common, high-level understanding of the task to enable aseamless interaction. Using specific gestures of the human to signal e.g. the completion of aprocess step to the robot is not an option as this would substantially reduce the efficiency.

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In situations where the robot is holding the material in place, while the human is draping nearby
areas of high curvature, the human will be very close to the robot. This might also happen
during the placement operation while the robot is still in motion. At that point the robot cannot
take any ‘avoiding action’ when it is operating close to a human. More intelligent modes ofinteraction are required than just keeping a certain minimum distance or blocking certain areas
around the robot.
In the following section a concept for human-robot collaborative draping is described that takes
the requirements of different domains into account, by analyzing human-robot interaction
modes and proposing a concept at hardware and software level. In section 3 a particularinstance of this concept is provided.

2. SYSTEM CONCEPT FOR COLLABORATIVE DRAPING
2.1 Human-robot interaction modes
Based on the analysis of typical application scenarios in the aerospace, boat-building andautomotive domain, a set of human-robot interaction modes have been identified. It has beenfound that in addition to human-robot collaborative tasks, there are also robot-only tasks, which
will be included in the discussion below, and possibly human-only tasks, which are notincluded. The following interactions have been identified:(1) Two Robots transport a large patch to the mold and pre-drape it into the initial shape.
This is a robot-only operation mode that is suitable when the patches are large and the area on
which they are to be draped does not have high curvature. This requires the coordinatedplanning of the robot trajectories, including a good model of the behavior of the material thatis handled between the robots, but the task as such is fully predictable.
(2) The robot and the human transport a large patch from the delivery table to the mold.
This is the most difficult interaction scenario, because it requires the robot to follow the humanin order to avoid over-tensioning the sensitive material, but on the other hand it needs to beplaced in the correct position. The robot obviously has a higher capability of locating theposition with high accuracy and thus needs to guide the human to this location.
(3) The robot transports a patch and drapes it.

This operating mode is a simplified variant of mode (1). In this case the patch of material issmall enough to be handled by a single robot and the curvature of the part allows fully
automatic draping. Standard motion planning can be used to generate a trajectory and – due tothe absence of human interaction – there is not much variability involved.
(4) The robot does initial draping and holds the patch in place, while the human is draping.
This interaction methods poses challenges in terms of trust. The human possibly needs to work
close to the robot. Sensor systems need to provide accurate feedback to avoid that the robot
unexpectedly starts to move in the wrong direction.
(5) The robot work-cell assists the human by delivering material or projecting information.

In these interaction modes the robot provides assistance, e.g. by delivering auxiliary materials
that need to be placed or by projecting position information during manual draping. The maindifference to other collaborative scenarios is that the task is completed by the human and the3

SAMPE Europe Conference 2021 Baden/Zürich - Switzerland
robot does not directly react to human. The scenario, however, requires at least anunderstanding about the state of the process.
2.2 Hardware level and collaborative draping process
Human robot interaction will be realized in industrial robot cells including one or more robots.
Large cut pieces and molds make it necessary to use an additional linear axis for the robots.
Due to the weight of the grippers, robots with respectively high payload capacities are needed.
The robots also have to provide the infrastructure for the features of the gripper such ascompressed air, electricity and network technology. The gripper uses several features to handle
the collaborative draping process. First the gripper needs modules to pick up the material andhold them in place but also needs adjustable gripping force to allow slipping when required.
Generating defined slippage is realized by friction-control modules which support duringdraping but also during holding of material on the tooling. The fixation of draped plies is carriedout by tacking modules which melt thermoplastic binder in the material. To automatically drape
certain cut pieces the gripper needs to be flexible and deformable to the desired geometry. Witha deformable gripper the position of the cut piece on the gripper needs to be detected by position
measurement modules to ensure precise placement. Finally, sensor modules which detect fiber
angles and movement of cut pieces finish the requirements needed to perform the precise
draping of fabrics in a human robot collaborative scenario. The cut pieces are usually provided
by industrial cutting machines. If the cutter is not located in the robot cell the cut pieces aredelivered to the robot on material carriers which have to be calibrated to the cell. To precisely
grip the cut pieces computer vision will be used to detect the location on the material carrier.
Since the worker is within the reach of multiple robots, special safety features have to be
implemented for worker detection. Combinations of laser systems and camera-based detection
will be used. To enable communication between the worker and the robot cell augmented
reality devices like a HoloLens are an option. This enables voice and gesture-based interaction
between worker and robots. The overall task execution will be controlled by a high-level
Manufacturing Execution System (MES). The MES needs to communicate with every
participant in the system via several network technologies. Processes like cutting are definedtasks which only need triggering when needed. However, the robots need to react in real-time
to worker movements which are provided by external systems. Therefore, the robot controls
need to be accessible by the MES to change robot paths on the fly.
2.3 Software structure for real-time interaction
The control architecture of a workcell for collaborative draping needs to handle two different
working modes: robot-only tasks where the robot(s) work(s) without interaction with theoperator and human-robot collaborative tasks.
A standard control architecture for industrial robotic cells is shown in Figure 2. The core of thesystem is the PLC that acts as a central node able to run a predefined finite state machine, tocommunicate with the factory automation through a fieldbus, to control the workcell actuatorsand to acquire workcell sensory data. The robot starts and stops the execution of predefinedprograms under the supervision of the PLC. This architecture is simple and effective but is not
flexible enough for a collaborative scenario.

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SAMPE Europe Conference 2021 Baden/Zürich - Switzerland
To improve flexibility
and to enable thecollaboration
thescheme presented in
Figure 3 needs to be
evolved with the
integration of smart
sensors able to track
the presence of thehuman operator(s) in
the robot workspace,and possibly providing
information at a higher
Fig. 2. Standard control architecture for robotic work-cells.
level about humanactions and intentions.
In case of a cooperative task, the goal is to use sensory information to adjust online the robot
nominal (i.e. planned offline) trajectory to deal with operator feedback (i.e. operator experiencein draping process) and operator movements (i.e. uncertainties of the movements). The
trajectory adjustments should prevent damage to the manipulated materials but at the same timethey should be constrained within predefined ranges. Tracking the operator in combination
with safety sensors, is also fundamental for safety monitoring to prevent dangerous working
conditions.
An intelligent perception system is a key component of a modern workcell [6], [7]. Therefore,
the draping cell is equipped with a perception system that exploits multiple technologies suchas 3D cameras, which provide RGB and depth images, and inertial sensors attached to thehuman workers operating in the cell, that are the main target of the perception system. Indeed,rich information about humans (as in [8]) is essential to achieve an efficient human-robot
collaboration. The vision sensors are organized in a camera network in order to provide an
overall map of the workcell. An additional force/torque sensor is integrated between the robot
flange and the robot gripper to measure the force/torque exchanged between human and robot
during the collaborative material manipulation.
The measurements provided by the different sensors are fused together to generate uniqueinformation able to provide feedback on the operator position and movements. The fusion
module is capable of providing reliable and stable information, and to overcome the limitationsof a single sensor: for example, a camera network with a proper sensor placement can overcome
occlusions that are very likely to occur in a human-robot cooperative application.
The signals provided by the sensor fusion module feed a Model Predictive Controller (MPC)
able to work as an active filter to generate a deformation of the nominal trajectory executed by
the robot if the operator movements and intentions differ from the expected one. As a primarytask the MPC acts as a target tracker, while as a secondary task manages the exploitation offunctional redundancies (when available), and scaling the velocity according to the human levelof interaction and the robot constraints. The MPC, therefore, is a module that, based on theactual scene information (humans, robots and sensors), modifies the pre-planned trajectory.
The generation of the deformation is based on a bounded model of the integration ofimpedance-based algorithms in MPC as an extension of [9] for dynamic applications with timevarying boundaries. The MPC, therefore, extends the approach to the control of a trajectory,5

SAMPE Europe Conference 2021 Baden/Zürich - Switzerland
and at the same time allows the integration of dynamic filter – such as impedance-filters – tomodify smoothly the trajectory according to the human intention. The methodology allows tomodify the velocities in order to fulfill ISO/TS 15066:2016, the dynamics of the robots, andexploiting the information coming from the camera to enable a safe online continuous replanning.

Fig. 3 Control scheme of work-cell for collaborative draping.
The MPC runs on an Industrial PC (IPC) and can be developed through soft PLC with the IEC61131-3 automation languages, to keep the application as close as possible to industrial practice(see also [10]). The MPC is triggered only during the collaboration, in all the other cases therobotics workcell acts as a standard application with the execution of predefined programs. The
adjusted trajectory is sent from the IPC to the robot controller through the so-called real-time
open channel, provided as a software option by several robot manufacturers.

3. IMPLEMENTATION EXAMPLEA CFRP frame is designed by general design guidelines for fiber reinforced layups [3,11] andwould be used in the door surrounding of a single-aisle airplane. Features of the frame are thelength of approximately 4 meters, local reinforcements which are generating slopes, double
curvature in the radius of web to flange and the resulting 90 degrees between draping planesof web to flange. The cut piece spectrum of the lay-up consists of several plies, all of them
have approximately 400mm in width but differ highly in length. This spectrum of cut pieces
requires several combinations of human-robot collaboration modes as stated in section 2.1.
An industrial robot cell with two robots on a linear axis will be used for the preforming process.
Also located in the robot cell will be the tooling on a fixed position as well as material carriers.
For the patches and short cut pieces an autonomous driving table can be used. The long cut
pieces are delivered to the robot cell and are placed on a long table. Both carriers are calibrated
to the robot cell for possible cut piece detection via machine vision. Finally, a worker is locatedin the robot cell which makes it necessary to enhance the cell with additional safety features
for safe human robot collaboration. In conventional robot cells, guards like safety fences arecommon, which basically prevent access to the cell while robots are moving automatically. To
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facilitate the presence of the worker in the cell, a hierarchical concept is proposed. In order togenerally control the entry into the cell while maintaining accessibility, light curtains are used
(Fig. 4, red border). This allows traditional safety features to be implemented like ensuring asafe position of the robots or triggering an emergency stop on entry.

Fig. 4 Collaborative draping cell with safety features: light curtains (red border), interaction
zones (green area), position tracking (yellow view fields), safe tools (red circles) and skeleton
tracking (blue circle)
Additionally, within the cell, there are one or more designated “interaction zones” (green
areas), which have a fixed position and size. The safety control will be implemented in a waythat the robots are only allowed to move when the worker is within an interaction zone. Thisrequires the accurate detection of humans in the work-cell. For this purpose, the perception
system will be used, which can be enhanced by safety equipment such as laser scanners (yellow
view fields). The robot needs to be able to enter the interaction zone as well. Since this needs
to happen in a safe and controlled way, an additional package for the robot controller will be
used (e.g. “SafeRobot” for KUKA), which provides comprehensive features such as thedefinition of safety zones, safe tools (red circles) and reduced speed. To enable sophisticatedinteraction with the robot, this basic safety equipment will be extended with multi-modal
sensing such as camera-based skeleton tracking (blue circle and view field), speech recognition
and gesture detection. This will increase the intelligence of the work-cell and enable a safe andefficient collaboration.

4. CONCLUSIONSCollaborative draping poses several challenges when setting up a robotic work-cell. At thehighest level, a suitable balance needs to be found between safety and efficiency. This will
require more intelligent behavior of the robots that goes beyond just stopping or slowing down
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in the presence of a human. At the control level, the interaction needs to resolve contradictions
such a placing the material in the right location while properly reacting to the variability ofhuman motion. This requires developments in perception and real-time control. Due to the sizeof the robots also trust between the human and robot is an important factor to be taken into
account.

5. REFERENCES
1. S. Das, J. Warren, D. West, S. M. Schexnayder, Global Carbon Fiber Composites Supply
Chain Competitiveness Analysis, CEMAC technical report, May 2016
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SAMPE North America, (2017).
3. C. Frommel, M. Körber, M. Mayer, A. Schuster, M. Malecha, L. Larsen, Autonomous
preforming system: automated and sensor-aided handling of dry carbon fibre textiles. 29th
International Conference on Flexible Automation and Intelligent Manufacturing(FAIM2019), 24.-28. June 2019, Limerick, Ireland
4. T. Gerngross, D. Nieberl, Automated manufacturing of large, three-dimensional CFRP
parts from dry textiles, CEAS Aeronautical Journal, 2014
5. K. E. Schaefer, Measuring Trust in Human Robot Interactions: Development of the “Trust
Perception Scale-HRI”, pages 191–218. Springer US, Boston, MA, 2016.
6. M. Faroni, M. Beschi, N. Pedrocchi, Inverse Kinematics of Redundant Manipulators with
Dynamic Bounds on Joint Movements, IEEE Robotics and Automation Letters, (2020)
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7. S. Ghidoni, M. Munaro, A multi-viewpoint feature-based re-identification system driven
by skeleton keypoints. Robotics and Autonomous Systems, (2017) 90, pp.45-54.
8. M. Terreran, M. Antonello, S. Ghidoni, Boat hunting with semantic segmentation for
flexible and autonomous manufacturing. In 2019 European Conference on Mobile Robots(ECMR) (2019), pp. 1-8.
9. A. Dalla Libera, N. Castaman , S. Ghidoni, R. Carli, Autonomous Learning of the Robot
Kinematic Model. IEEE Transactions on Robotics (2020).
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Applications," 2020 25th IEEE International Conference on Emerging Technologies andFactory Automation (ETFA), (2020), pp. 1273-1276.
11. D. Deden, C. Frommel, R. Glück, L. Larsen, M. Malecha, A. Schuster, (2019) Towards afully automated process chain for the lay-up of large carbon dry-fibre cut pieces using
cooperating robots. SAMPE Europe 2019. SAMPE Europe Conference 2019, 17.19.09.2019, Nantes, France.

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