From 2D to 3D Mixed Reality Human-Robot Interface in Hazardous

Robotic Interventions with the Use of Redundant Mobile Manipulator

Krzysztof Adam Szczurek

1,2 a

, Raul Marin Prades

2 b

, Eloise Matheson

1 c

, Hugo Perier

Luca Rosario Buonocore

1 d

and Mario Di Castro

1 e

European Organization for Nuclear Research, Switzerland

Jaume I University of Castellon, Spain

Keywords:

Human-Robot Interface, Robotics, Teleoperation, Virtual Reality, Mixed Reality, Operator Workload,

Galvanic Skin Response.

Abstract:

3D Mixed Reality (MR) Human-Robot Interfaces (HRI) show promise for robotic operators to complete tasks

more quickly, safely and with less training. The objective of this study is to assess the use of 3D MR HRI

environment in comparison with a standard 2D Graphical User Interface (GUI) in order to control a redundant

mobile manipulator. The experimental data was taken during operation with a 9 DOF manipulator mounted in

a robotized train, CERN Train Inspection Monorail (TIM), used for the Beam Loss Monitor robotic measure-

ment task in a complex hazardous intervention scenario at CERN. The efﬁciency and workload of an operator

were compared with the use of both types of interfaces with NASA TLX method. The usage of heart rate

and Galvanic Skin Response parameters for operator condition and stress monitoring was tested. The results

show that teleoperation with 3D MR HRI mitigates cognitive fatigue and stress by improving the operators

understanding of both the robot’s pose and the surrounding environment or scene.

1 INTRODUCTION

In the environment where there are risks for human

operators such as presence of radiation or magnetic

ﬁelds, or lack of oxygen in underground/underwater

areas, the robotic operations can be indispensable.

Nowadays, the most commonly used interfaces to

control robots in radioactive scenarios are 2D GUIs

with keyboard/joystick inputs and camera feedback.

However, thanks to the development of sensors, com-

munication, electronics and faster computation, 3D

interfaces, which show the world as we see with our

eyes, can bring multiple advantages in terms of addi-

tional information available to the operator and mak-

ing the operation more efﬁcient.

To describe the purpose of the research on the

Mixed Reality Human-Robot Interface at CERN, ﬁrst

a speciﬁc project and the teleoperation task have to

be explained. One of the tasks executed with the aid

of robots at CERN is to verify Beam Loss Monitors

(BLMs) signal condition with a radioactive source ap-

https://orcid.org/0000-0002-2440-5956

https://orcid.org/0000-0002-2340-4126

https://orcid.org/0000-0002-1294-2076

https://orcid.org/0000-0001-5396-2519

https://orcid.org/0000-0002-2513-967X

proached to the monitors in the Large Hadron Col-

lider (LHC) tunnels. Its complexity lies in the need

of full perception of the conﬁguration of the robot,

robot’s position in the surrounding area, approach to

the device and collision avoidance, which all are cru-

cial during the intervention.

1.1 Motivation

At CERN, an ongoing project is to verify that each

of 4500 BLMs located in the LHC is functioning

correctly. Currently, these measurements are per-

formed manually by an expert who brings a radioac-

tive source, held via a tool that they hold, close to

the monitor (BLM), and checks that the device is

responding correctly. This methodology has a few

drawbacks. It is time-consuming due to the number

of devices, and due to the difﬁculty of the approach.

Some of the BLMs are located next to a pathway

(Figure 1), but others can be hidden behind the mag-

nets or equipment. Another disadvantage is that the

expert operator will absorb some radiation. There-

fore, it was proposed to replace the manual measure-

ments via a robotic system. Speciﬁcally, the project is

to use the Train Inspection Monorail (TIM) (Di Cas-

tro et al., 2018b) as a platform to transport a robotic

manipulator which can reach most the the BLMs in

388

Szczurek, K., Prades, R., Matheson, E., Perier, H., Buonocore, L. and Di Castro, M.

From 2D to 3D Mixed Reality Human-Robot Interface in Hazardous Robotic Interventions with the Use of Redundant Mobile Manipulator.

DOI: 10.5220/0010528503880395

In Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2021), pages 388-395

ISBN: 978-989-758-522-7

the Large Hadron Collider (LHC) tunnels. An il-

lustration of the robotic task is shown in Figure 1,

where the BLM robotic manipulator end effector ap-

proaches a BLM installed on the magnet dipole in the

LHC. The manipulator is attached to the TIM (Fig-

ure 4) platform which travels on a rail mounted on

the tunnel ceiling. In order to have enough range and

approach ﬂexibility, a redundant robotic manipulator

with 9 degrees of freedom has been designed (Figure

1). The operation is ultimately planned to be executed

either semi-autonomously via teleoperation, or fully

autonomously. However, the use of the robot presents

multiple challenges, including:

1. Collisions and self-collisions due to limited per-

ception of the environment and robot’s pose;

2. Manipulator redundancy and singularities;

3. Limited bandwidth and coverage of connectivity;

4. Targets (BLMs) not being visible at the beginning

of the approach and not precisely located in the

design model.

Figure 1: Approach with the BLM robotic manipulator in

the LHC tunnel.

1.2 State of the Art

The CERNTAURO robotic framework developed at

CERN for autonomous and supervised teleoperations

in harsh environments is presented in (Di Castro et al.,

2018a). The system was tested in tens of real inter-

ventions and demonstrated efﬁciency and ﬂexibility

in the executed tasks. The extension of the system

with cooperative teleoperation and multi-robot inter-

face is explained in (Veiga Almagro et al., 2020). The

cooperative behaviour was tested with the CERN-

TAURO project at CERN and with the TWINBOT

project in an underwater scenario and proved that

one operator can handle an intervention with mul-

tiple robots in a safe way. Furthermore, a prelim-

inary study of Virtual Reality Interface for guiding

underwater robots in the TWINBOT project (de la

Cruz et al., 2020) was carried out and and feasibil-

ity and usability tests of the VR HRI module were

done. A depth estimation vision system based on

tracking metallic objects was tested with the CERN

HRI is described in (Almagro et al., 2019). It presents

an operator-supervised method with AR elements to

facilitate teleoperated manipulation tasks. The dif-

ﬁculty of dealing with reﬂections from metallic sur-

faces was solved.

The subject of hyper-redundant robots is dis-

cussed in (Andr

es Mart

ın Barrio, 2020) where the

author discusses the problems with modelling and

control when teleoperating such robots. Different

problematic aspects are discussed, including those re-

lated to redundancy, such as inverse kinematics so-

lutions, pose awareness and human-robot interfaces.

They found that immersive human-robot interfaces

provided signiﬁcantly better efﬁciency, situational

awareness and visual feedback. The impact of the

use of a set of ”interaction tools” was also measured.

They concluded that the use of a physical VR con-

troller had a relatively high efﬁciency, however the

use of gestures and voice commands had better per-

formance.

Another study (Chacko and Kapila, 2019), which

focused on AR solutions, presented how visual feed-

back from a camera can be merged with additional

information from object recognition algorithms, de-

creasing the workload of the operator. The small mea-

sured workload and simplicity indicated potential ad-

vantages that could be applied in the experiments pre-

sented in this paper.

The telepresence can be affected by many factors

such as latency, frame of reference, ﬁeld of view and

frame rate. These parameters can lower the perfor-

mance and create issues. How vision and interaction

ﬁdelity affected spatial cognition and workload in im-

mersive interfaces is studied in (Almeida et al., 2020).

The results give recommendations how telepresence

improvement techniques impact the operator’s effort.

1.3 Paper Structure

Section 2 presents the overall architecture of the

CERN robotic systems and the ”2D” and ”3D”

Human-Robot Interfaces. Section 3 describes the ex-

periments undertaken with the interfaces and operator

monitoring sensors. Finally, Section 4 summarizes

the work, results from the experiments and presents

a road-map for future developments.

From 2D to 3D Mixed Reality Human-Robot Interface in Hazardous Robotic Interventions with the Use of Redundant Mobile Manipulator

389

2 SYSTEM DESCRIPTION

The overall architecture of the robot’s control system

is presented in Figure 2. The system is composed of

two subsystems. The ﬁrst is a robot with its conﬁgura-

tion of physical devices connected to a compact com-

puter with a Linux operating system. The computer

runs the CERN Robotic Framework, which is a C++

set of programs that operate devices and communi-

cates with the external world, including the processes

responsible for real-time control of motors, arms, sen-

sors and cameras. The second sub-system is the oper-

ator’s side, composed of the Human-Robot Interface

in the form of either the 2D CERN Robotic GUI (de-

veloped with WPF technology in C#) or the 3D Mixed

Reality GUI (developed in Unity, in C#), both running

on a Windows machine. In the diagram in Figure 2,

the part of the system which has been newly devel-

oped for this research is highlighted in bold.

Figure 2: Architecture diagram of the robot control.

2.1 2D CERN Robotic GUI

Currently, the operational control of the robots at

CERN is performed with the CERN Robotic GUI,

which is an application connecting directly to the

robot via WiFi, using CERN’s internal network or via

a VPN running of the 4G mobile network. The user

has the ability to:

• Customize the robot’s conﬁguration for a mission;

• Control the movement of a robot’s base (i.e.

the omnidirectional platform shown in Figure 3,

monorail train shown in Figure 4);

• Control a robotic manipulator in joint and Carte-

sian coordinate systems (base and tool refer-

ences);

• See video streams from different types of cameras

(RGB, depth, thermal) and record them;

• Control tools (i.e. gripper) and have sensor feed-

back (i.e. force sensor);

• Perform semi-autonomous tasks like unfolding

the manipulator, approaching a deﬁned/recorded

position;

• Control the robot with different input devices

(keyboard, gamepad, space mouse).

The architecture of the core of the CERN Robotic

GUI has been fully described in the Section IV in

(Lunghi et al., 2019).

Figure 3: Omnidrectional robotic platform with an installed

arm.

Figure 4: Train Inspection Monorail which can haul spe-

cialized wagons i.e. with a manipulator.

A control view with the robot’s control inputs and

views from the cameras is shown in Figure 5. On the

left vertical ribbon, there are tabs to switch between

different views to control the robot’s base, arm, cam-

eras, state machine and change the settings of the ap-

plication. For the arm control, in the upper right cor-

ner, the two control modes (joint and Cartesian) can

be toggled. In the lower right corner, different input

devices (keyboard, space mouse or gamepad) can be

selected. In the area on the bottom, the current in-

put device panel is presented. In the upper left corner,

there is the cameras selection tool, which streams are

placed in the upper and center parts of the window.

2.2 3D Mixed Reality GUI

The Mixed Reality Human-Robot Interface for con-

trolling robots with real-time feedback has been de-

veloped and experimentally tested. At CERN, oper-

ators are using mouse and keyboard control input on

weekly basis for robotic interventions. Whilst these

inputs devices are not novel, they are very reliable,

well-understood and give the operator full trust and

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390

Figure 5: CERN Robotic GUI control view with an arm’s

control and cameras view, keyboard control.

high-resolution control options. Other input devices

such as gesture recognition and voice control and very

interesting to further study to understand their appli-

cability in real-case scenarios, but are not the subject

of this research paper. The main functionalities that

have been already implemented are:

• Visualization of the robot’s pose in relation to a

modelled environment visible from any perspec-

tive;

• Planning of the next position and sending a com-

mand to move when it’s ready;

• Real-time control of the robot and its manipulator;

• Camera streaming in the 3D scene.

In the Figure 6 a planning mode is used to ﬁrst

position the arm (the transparent model) for the best

approach, and then launch the movement in joint con-

trol. In Figure 7, real-time control is used with imme-

diate commands and feedback reading. The operator

can select which joint to move.

The second possible mode is real-time control

where the command is send immediately to the robot

to execute and the current positions are read con-

stantly from the robot. In Figure 7 the operator can

select a joint marked in red and change the angle to

approach the target.

Apart from joint coordinate system control, the

end effector can be steered in the Cartesian world or

tool coordinate systems. In Figure 8 there are 3 illus-

trations, the ﬁrst depicts a gizmo that can be moved

and rotated by dragging it with a mouse, the second

shows a gizmo that controls only the rotation and the

third shows translation motion only. As the manipu-

lator consists of 9 joints (a redundant system), it was

concluded that only the inverse kinematics (IK) con-

Figure 6: VR control of the arm in the planning mode.

Figure 7: VR control of the arm in the real-time control

mode, the active joint is marked in red.

trol of the last 6 joints is needed, while the ﬁrst 3 joints

can be controlled in joint space. This limits the com-

plexity and decreases the computational time of the

IK algorithm, while still allowing the operator enough

workspace to complete the task.

The arm can be controlled with the use of the in-

teractive markers presented in the scene (Figure 9),

presenting the direction of the joint movement, its an-

gle and clickable controls.

3 EXPERIMENTS

An experiment was conducted to evaluate the use of

the 3D environment and its effect on the operator’s

workload and efﬁciency compared to the use of the

standard GUI with the 2D interface. In the 3D envi-

ronment, the operator could see not only the streams

from cameras but also the real-time model of the

the robot and the ofﬂine environment that was pre-

pared before the intervention. The model of the robot

was placed in the environment according to measure-

ments of the setup. In this experiment, there was no

From 2D to 3D Mixed Reality Human-Robot Interface in Hazardous Robotic Interventions with the Use of Redundant Mobile Manipulator

391

Figure 8: Inverse kinematics control with a gizmo that can

be dragged with a mouse.

Figure 9: Interactive joint position markers and controls in

the scene.

live positioning, 3D point cloud, force feedback nor

other collision detection feedback. The experiment

also tested the user’s behaviour, to see if he or she

would be focused more on the camera feedback or 3D

model, and what would be the response to small dis-

crepancies between the real robot’s position and the

position in the Virtual Reality related to the external

environment. In this paper, we focus on the use of

standard inputs such as a keyboard and a mouse to

interact with the Virtual Reality environment, as they

are readily available and universal. In both 2D and 3D

systems, the time delays are minimal and much lower

than the limit of 300 ms where the operator would

change the control strategy. This behaviour was in-

vestigated in the section ”Navigation task with time-

delay” of (Lunghi et al., 2019).

During the experiment the heart rate and the gal-

vanic skin response (GSR) of the operator were mea-

sured and recorded to see how the volatility can be

correlated with the events that occur during the op-

eration. It could be a collision, malfunction of soft-

ware or hardware, or network delays. A more difﬁcult

position of the arm, collision vicinity or other stress

sources could also cause increased stress.

3.1 Setup

A mock-up of the LHC tunnel (Figure 10) was used

to represent a real scenario where the end effector had

to approach the BLM device (yellow cylinder). The

manipulator is installed on the TIM robot moving on

a rail attached to the ceiling. At the end of the effector

there is a camera.

Figure 10: Experiment scene.

In the experiment, the goal was to approach the

BLM in 3 different positions of the effector shown in

Figure 11 within a speciﬁed tolerance of angles with

an unrestricted pose of the rest of the manipulator.

Figure 11: Position goals (from the side, from behind and

from above).

The person used a laptop with the GUI in a sepa-

rate room isolated from the experiment scene (Figure

12). The full cycle of familiarisation training with the

GUIs, operating the arm, measurements and feedback

was performed by 4 (n = 4) people. It was a pilot

project with the small number of subjects to under-

stand if indicative results show promise in expanding

this area of research. The results can be considered as

indicative/early-stage. The subjects were aged 20-35,

they were not operating the robots before but they had

at least basic knowledge in robotics.

3.2 Workload Measurement

The user experience was assessed with the aid of the

user’s responses to the NASA Task Load Index (TLX)

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Figure 12: Operator’s local computer with the open Mixed

Reality GUI. The hearbeat and GSR sensors are worn on

hands.

workload questionnaires (Hart, 2006) and via open

feedback written at the end of the experiment. Ac-

cording to the responses, in the chart shown in Figure

13, there is a weight distribution of the elements con-

tributing to the workload. The Physical Demand has

the lowest weight as the control does not require any

other activity other than keyboard and mouse opera-

tion. The highest weight was given to Mental Demand

and its derivatives, as they played a signiﬁcant role re-

garding performance and effort - which includes ad-

ditional Mental Demand.

Figure 13: Averaged weights in NASA-TLX question-

naires.

In Figures 14 and 15 the ratings of the workload

subscales with marked minimum, maximum and av-

erage values from both tests - 2D and 3D GUIs are

shown. From these values, it can be deducted that

Frustration varied signiﬁcantly depending on the per-

son and the Physical Demand was low, however the

other subscales averaged around the value of 60-70.

The biggest difference between 2D and 3D GUIs was

in the Mental Demand subscale. It is possible that the

mental demand may only have been lower as the users

could choose their viewpoint in the scene, but this in-

dicates that the use of a 3D simulated environment

with ﬂexibility of viewpoints provides a big advan-

tage to ease of teleoperation.

Figure 14: Rating responses for 2D GUI.

Figure 15: Rating responses for 3D GUI.

Overall, the workload difference between the

GUIs was 14% (61 for 3D to 71 for 2D).

Figure 16: 2D and 3D GUI ratings.

Valuable points can also be drawn from the written

feedback:

From 2D to 3D Mixed Reality Human-Robot Interface in Hazardous Robotic Interventions with the Use of Redundant Mobile Manipulator

393

• In the operation with the 2D GUI, the views from

the cameras played a big role. Because the cam-

era axis was tilted to have a view from above, it

was not easy to understand the orientation of the

end effector with respect to the stream. There was

a problem when the arm went out of the camera

frame so that the pose of the manipulator was not

visible, which sometimes made the operation very

difﬁcult if the operator did not remember the pre-

vious joint conﬁguration.

• Operators generally focused on the camera views

in the 2D GUI and on the model view in the 3D

GUI. The use of cameras only helped to prevent

collisions when the whole arm was visible in the

stream. However, having the model and environ-

ment in 3D made the movements much more com-

fortable, as the scene can be seen from any angle,

which according to the feedback, was the biggest

advantage of this view. On the other hand, too

much trust was put in the model, and if there were

any discrepancies between the models and the re-

ality or there was an un-modelled obstacle, there

was an increased risk of collision if the camera

streams were not checked regularly.

• The experiment focused on the use of the joint

coordinate system but, especially in approaching

the BLMs, the Cartesian coordinate system con-

trol would be more suitable and could make the

operation easier.

3.3 Galvanic Skin Response

Measurement

GSR sensors detect changes in the electrical skin con-

ductance caused by sweat gland activity. The mois-

ture changes the balance of ions in the ﬂuid, which

results in measurable changes. Two electrodes were

placed on emotionally sensitive locations with high

sweat glands density. In this experiment, the sensors

were placed on ﬁngers and the device was attached to

the wrist (Figure 17).

Figure 17: Shimmer GSR device attached on a hand.

Figure 18: Skin resistance graph.

An example skin resistance dataset recorded dur-

ing an operation is shown in the Figure 18. The

value dropped over time with a few steeper variations,

meaning that the glands activated and the conductance

increased. This response may be correlated with an

increased operator’s stress. However, it was noticed

that simply the contact with the electrodes and Vel-

cros causes minimal sweating under them. The other

parameter is the force with which the electrodes are

pressed to the skin - a higher force causes less resis-

tance. Moreover, small movements of the ﬁnger to

touch the keyboard or mouse may have provoked no-

ticeable spikes in the signals. Thus, further study is

necessary. From the observations and analysis of the

recordings, the following conclusions were drawn:

• The electrodes should be worn at least 1 hour un-

der stable conditions to stabilize sweating;

• The use of electrodes with conductive gel attached

with an adhesive to a place which does not move

may be a solution to eliminate the effects of the

motion of the ﬁngers;

• An emotional fast response is clearly visible on

graphs for a distinctive event (i.e. unexpected be-

haviour, collision, high stress) but slowly increas-

ing stress may be harder to deduct.

3.4 Heartbeat Measurement

The stress of a person can be correlated with his or

her heart rate measured using a photoplethysmograph

(PPG) optical sensor (Mohan et al., 2016). The sensor

can be clipped on an ear lobe or be integrated in a

smart watch. In the experiment the second option was

used.

An example heart rate graph recorded during an

operation is shown in Figure 19. It is visible that

the base frequency is around 70 beats per minute

(bpm), however there were moments when the fre-

quency raised up to 100 bpm. It may be interpreted

that the person was dealing with more mental load or

breathing pattern changed due to the performed task.

In a steady state, during a typical work in front of the

computer, the heart rate doesn’t vary more than a few

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394

Figure 19: Heart rate graph.

beats per minute, what was checked with the same

sensor before the experiment.

4 CONCLUSIONS AND FUTURE

WORK

This paper describes how a Mixed Reality HRI can

be used in a real hazardous scenario where a redun-

dant manipulator is used via teleoperation of an op-

erator to perform an inspection taks. It presented the

robotic system’s architecture and functionalities that

were achieved. The comparison of the standard 2D

GUI and the 3D Mixed Reality GUI was made in an

experiment where the workload and operator’s bio-

metric parameters were measured. The main con-

clusion from this experiment was that the teleoper-

ation with 3D MR HRI mitigates cognitive fatigue

and stress by improving robot’s pose and scene un-

derstanding by an operator.

To allow for Cartesian control using this robot,

the pose control problem of the redundant manipula-

tor will be studied and different solutions (Jacobian-

based, heuristic) will be tested.

To increase the trust and robustness of the simu-

lated environment, real-time point cloud and environ-

ment reconstruction is an important evolution in the

Mixed Reality control. Future work will be dedicated

to integrate these functionalities.

As the 3D environment has been rendered on a

2D screen, the immersion and experience of the user

is much better with larger screens. A bigger scene im-

mersion may be achieved with the use of a VR head-

set and/or different input devices, which could also be

controlled with gestures or VR controllers. However,

it requires more preparation and setup from the oper-

ator in a real intervention where the access time in the

area may be limited. Future work will also integrate

and evaluate the effectiveness of these devices when

performing common teleoperation tasks at CERN.

Further study and tests will be done to compare

the reading with GSR measurement, heartbeat rate

and camera recordings to select the factors that clearly

impact the operator heart beat and GSR resistance. If

the effect is immediate, the values could be communi-

cated to the operator in the GUI in order to let him or

her realize about the body reaction to the performed

task.

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