Robotics and Computer Integrated Manufacturing 67 (2021) 102034

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Robotics and Computer Integrated Manufacturing
journal homepage: www.elsevier.com/locate/rcim

Full length Article

Considerations of potential runaway motion and physical interaction forspeed and separation monitoring

T⁎

Eugene Kim , Yoji Yamada, Shogo Okamoto, Mikihiro Sennin, Hiroki Kito
Department of Mechanical Systems Engineering, Nagoya University, Furo-Cho, Chikusa-ku, Nagoya, 464–8603, JapanA R T I C LE I N FO

A B S T R A C T

Keywords:
Human-robot collaboration
Speed and separation monitoring
Safety-related sensor
3D Human sensing
Unstructured manufacturing

In the ﬁeld of human-robot collaboration (HRC), the speed and separation monitoring (SSM) collaboration haveattracted much attention owing to its non-contact safety strategy. 3D sensing applications are currently of interest for industrial automation technology and are considered to be a promising method for maximizing theeﬃciency of SSM. However, little attention has been given to the runaway space of the robot or the potential
contact due to the foreseeable misuse of the operator. In this study, experiments are conducted using a radarsystem as an example of a 3D safety-related sensor, and battery assembly scenarios are carried out for thecomparison. In the experiment, two diﬀerent orientations of the robot are tested, considering the potential
runaway motion of the robot. Also, the maximum permissible speed of the robot is calculated by geometricaltransfer energy, which is based on eﬀective mass and the velocity of the manipulator and human injury criteria.
From the experimental results, it is evident that it is better to avoid placing the vertical articulated robot in frontof the operator from the perspective of minimizing the eﬀect of runaway motion into the safety distance. Finally,
the proposed framework of speed limitation is thought to be an eﬀective method to link SSM and power andforce limiting safety function.

1. Introduction
Robots have undoubtedly become a critical resource in the manufacturing sector. Repetitive and simple tasks, or tasks that are diﬃcultand dangerous for humans, can be performed eﬃciently and safely by
robots. However, humans have higher productivity than robots inparticular tasks, such as connecting wires and cables. For this reason,
human-robot collaboration (HRC) plays an important role in bringing
productivity and eﬃciency one step higher [1,2]. In order to realize thedemands for HRC, it is necessary to reduce the risk to an acceptablelevel, which can only be achieved through suﬃcient risk assessment[3,4]. Here, the speed and separation monitoring (SSM) function can beutilized in the scenario that uses a non-contact policy for securing thesafety of humans while allowing a protective separation distance (PSD)
between humans and robots [5,6].
There has been discussion and investigation regarding how to takeadvantage of the SSM function. Szabo et al. applied the SSM prototype,which was one of the earliest forms that utilize laser scanner-type ofsafety-related sensors [7]. Furthermore, Marvel et al. evaluated thesafety and productivity of the SSM functionality, and several studies
were undertaken to investigate the inﬂuence of the essential parameters⁎that supplement the PSD function [8–11]. Another approach was taken
by Lacevic et al., which expands the SSM function to entire joints of therobot using a danger ﬁeld algorithm for calculating avoidance motion[12,13]. In addition, Byner et al. maximized the eﬃciency of SSM bygeometrically computing the distance of the robot to the source of
danger [14]. A number of studies have investigated the parameters that
supplement the PSD, such as basic parameters [15], a psychologicalapproach [16], and the likelihood of intrusion [17].
However, commercialized robots that are designed for collaboration
are not generally operated with the SSM function. Some studies haveclaimed that the reason for this is the absence of a suitable safety-related sensor [18,19]. On the other hand, the recently launched ISO/TS
62,998 standard, which aims to extend and integrate these safety-related sensors, does not limit the detection principle to a speciﬁc
medium [20]. These recent trends have led to proposals of 3D detection
techniques and to feasibility studies of human presence sensing[21–24]. The high-quality data obtained by these 3D sensors is expected
to drastically reduce the distance between humans and SSM implemented robots [18]. Moreover, recent advancements in the framework of collision-free HRC has been reported with various concept
using 3D sensors such as machine learning techniques and pose

Corresponding author.
E-mail address: kim.eugene@a.mbox.nagoya-u.ac.jp (E. Kim).
URL: http://www.mech.nagoya-u.ac.jp/asi/en/ (E. Kim).

https://doi.org/10.1016/j.rcim.2020.102034
Received 10 September 2019; Received in revised form 13 July 2020; Accepted 13 July 2020
Available online 22 July 20200736-5845/ © 2020 Elsevier Ltd. All rights reserved.

Robotics and Computer Integrated Manufacturing 67 (2021) 102034

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estimation of human [25,26]. In this respect, it is important that supplemental considerations especially related to safety criteria for SSM
when introducing 3D sensors are urgently discussed.
The aforementioned SSM criteria can be applied only to situations
when the robot is normally activated and has the capability of abortingwhen necessary [27]. However, possible faults such as an erratic sensor
at the joint level may generate erroneous signals which cause an instant
runaway state of the robot [28]. In this respect, some studies have attempted to show how much the runaway volume on the robot manipulator should be estimated at an instant in 3D space. Meanwhile, therunaway motion space may diﬀer with the relative orientation of therobot and the operator since the runaway distance is generally represented as an ellipsoid volume. Based on the pioneering work of
Yamada, we will discuss whether the runaway distance can be reduced
according to the relative orientation of the human and the robot [29].
For the case of using properly designed SSM, no contact can occur
except for intentional or misuse by the human because of the nature of
the non-contact oriented collaborative system. However, some of thepotential intrusion may be conditionally allowed if the estimated risk is
acceptable which will make the application more eﬃcient than solely
used SSM. In this case, more attention should be given to the severity of
the undesired contact, originating from human error or the ergonomic
aspect of risk assessment for the human operators. This may be realized
by considering transfer energy between the human and the robot which
also indispensable from the viewpoint of a probabilistically worst-casescenario. Thus, the approach of estimating the expected transfer energyof potential contact in the dynamic state will be discussed in this study.

Fig. 1. Illustration of protective separation distance (blue colored area), where
vh and vr denote the relative speed of the operator and the robot, respectively,
deﬁned according to the closest distance plane; Tr denote the global reaction
time which includes protective equipment, logical control device, and drive
apparatus; and Ts represent the stopping time to make the speed of the robot
zero. (For interpretation of the references to colour in this ﬁgure legend, thereader is referred to the web version of this article.)is given by
1.1. Contributions

Sp (t0) = Sh + Sr + Ss + C + Zd + Zr .
This study presents two approaches for investigating marginal
considerations for SSM collaborative method with the 3D sensor usage.
One approach is to investigate the eﬀect of the sudden acceleration of
the robot due to the runaway motion of the robot. Another approachshows the maximum permissible speed of the robot calculated bygeometrical transfer energy. First, an experiment was made to compare
the SSM implementation when using a radio wave sensor as an example
of a 3D sensor and a conventional minimum distance approach whenusing a 2D laser scanner. In the experiment, human and robot battery
assembly tasks were used to demonstrate a realistic HRC scenario, andexperiments have conducted by the participants who have been engaged in the production line for more than two years. Then, the obtained relative motions of the robot and the human are used to calculate
the eﬀect of a potential runaway motion of robots based on two relativeorientations of an operator and a robot: frontal orientation A, andperpendicular orientation B. Based on eﬀective mass and the relative
velocity of the manipulator and the operator obtained in the experiment, the speed limitation of the robot method is discussed.

(1)

where Sh is the distance that the human can move from the time thestop signal is issued to the robot until the time that the robot is required
to stop completely; and Sr represents the idle distance of the robot until
the stop signal is issued by the controller. The reaction time generally
includes the response time of the safety-related equipment, the robot
controller, motor drive, and communication latency. Furthermore, Ss isthe braking distance of the robot until it stops completely, which ishighly dependent on the braking performance of the robot, the present
speed of the robot when the stop is issued, and the weight of the payload. In addition, C is an intrusion distance through which a part of the
body (usually a hand) can move past the detected zone toward thehazard zone prior to actuation of the safety functions [31]. For instance,
all possible intrusion of the human hands from the undetected zone
should be considered if the robot system can only monitor a 2D plane.
Based on consideration of the ISO standards, shifting from a 2D sensor
to a 3D sensor can reduce the intrusion distance. When using a 2D laser
scanner, a distance less than or equal to the 850-mm intrusion distancemust be considered in the SSM function as C. Therefore, it is required toseparate the human and the robot more than is necessary, which consequently limits the eﬃciency of the collaboration. On the other hand,when using a 3D sensor as the safety-related sensor, C can be neglectedby only considering the measurement performance of the sensor.
Finally, Zd and Zr are the measurement uncertainties of the positionsof the human and that of the robot. However, as some research shows,
how to derive these parameters regarding uncertainties still remains notclear [11,18]. In this study, we follow the framework of IEC/TS 62,998
to use coverage interval as the measurement uncertainties. The coverage interval indicates the conﬁdence level of the Gaussian distributed
measurement error that meets the required safety integrity level [20].
In accordance with the required safety integrity level of PLr = d, themeasurement uncertainties are calculated as Zd = 5.16 σ , where σ denotes the standard deviation of the measurement error.
Assuming that the robot decelerates with a constant braking performance as, (1) can be rewritten as follows:

2. Methodology for non-contact policy between humans androbotsThe SSM collaborative operation method secures the operator fromcollision by continuously monitoring the movements of humans androbots. This method calculates the minimum allowable distance between a human and a robot based on the physical relationship using
information from a safety-related sensor. At the moment when theoperator is encroaching in terms of the PSD, the robot system designedwith SSM method initiate a protective stop and safety-related functions
such as turning oﬀ any hazardous tools [30]. In this section, potential
runaway motion in task space (PRAM-t) is introduced based on the
dynamic manipulability ellipsoid (DME).
2.1. Protective separation distanceFig 1 shows a schematic diagram of the PSD. The formula of the PSD
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E. Kim, et al.

Sp (t0) = vh (t0)(Tr + Ts ) + vr (t0) Tr +vr (t0)2+ C + Zd + Zr
asv (q) = J (q) q˙(2)

where J is the Jacobian matrix (J ) and q˙ = d q/ dt .
Let M (q) is the inertia matrix; the acceleration of the robot endeﬀector can be computed as follows:

where Tr is the reaction time of the robot system; and Ts is the time
elapsed from the issuing of the stop signal until the robot stops completely. In addition, vr and vs represent the relative speed in the mannerthat it decreases the closest distance the most. Finally, the robot that
violates the PSD should execute the protective stop function as follows[32]:

Sp (t0) ≤ S (t0).

(5)

˜ −1 (q) τ˜ (q)v˙ (q) = J (q) M(6)where τ̃ is the normalized control force of jointsτ˜ = [˜τ1, τ˜2, …, τ˜n], τ˜i = τi/ τimax ,

(3)

(7)

and M̃ denotes the normalized M :2.2. Potential runaway motion volume in task space

˜ = Tτ M, Tτ = diag ⎛ 1 , 1 , …, 1 ⎞.
Mτmmax ⎠
⎝ τ1max τ2max⎜

Despite this eﬀort, the safety distance between the robot and thehuman may be insuﬃcient with the presented PSD alone. For instance,
popular permanent magnet servomotors, which are used to produce thedriving force to move the joints of industrial robots, can generate faults
for electrical, mechanical, and other external reasons [33,34]. It shouldbe also noted that even though ISO/TS 15,066 requires to consider theacceleration capability of the robot, few studies have attempted to include the runaway motion of the robot [6].
In this study, PRAM-t is used to estimate the worst-case acceleration
volume that the robot can move during the state of the runaway, as
shown in Fig. 2 [29]. In the case where a robot makes a fault at time t0,
it can be interpreted that the end-eﬀector of the robot moves along adiﬀerent path from that of the original one that has been planned innormal operation. For industrial robots that are designed for the manufacturing sites, it is required to have the capability of detection or
tolerance to a single fault [5]. Therefore, it can be assumed that theserobots have the capability to execute the protective stop function after acertain period of time, i.e., the reaction time or the communication
time. Especially, in general, the motor apparatus also have independently capable of self-diagnosis function in case of failure. In thisstudy, we assume the reaction time of an activation of a protective
safety function is equivalent to the maximum reaction time of the motor
drive.
Suppose r = (r1, r2, …, rm)T ∈ m is a set of vectors that represents the
position and orientation of the end-eﬀector deﬁned from a reference
coordinate
system
ﬁxed
to
the
robot
system,andq = (q1, q2, …, qn )T ∈ n is the joint angle vector. The ﬁrst-order relationship between two vectors, q and r, can be written as follows:r = f (q).

⎟(8)

Hence, all the sets of acceleration of the end-eﬀector that can beobtained using the joint driving force become ellipsoids in the m-dimensional Euclidean space (i.e., DME) [35]. The main axis of the ellipsoid σd1 ud1, σd2 ud2, …, σdm udm can be determined by calculating the−1
singular value decomposition of (JM̃ ):˜ −1 (q) = Ud Σd VTd .
J (q) M

(9)

In accordance with (9), PRAM-t when the robot runs at time t0 can
be calculated under the assumption that the robot will follow the acceleration calculated by the DME during the reaction time of internal
motor drive Tro.
Finally, the PSD considering the PRAM-t RSp is expressed by thefollowing conditional equation that is dependent on the speed of therobot during the reaction time of the motor drive Tro, which is shown in
Fig. 3. What is to note is the PRAM-t is generally represented as anellipsoid, and the acceleration value is dependent on the relative distance and direction between the robot and the human. Therefore, as
shown in Fig. 2, the RSp is calculated on the Mahalanobis distanceconsidering the geometrical relationship between the ellipsoid and thesphere of the human body part distance.

(4)

Further, velocity of the end-eﬀector v ∈  can be expressed as

Fig. 3. Illustration of protective separation distance (blue colored area) andpotential runaway motion in task space (orange-colored area), where vh and vr
denote the relative speed of the operator and the robot, respectively, deﬁned
according to the closest distance plane; Tri, Trl, and Tro denote the reaction timeof the protective equipment, logical control device, and motor drive respectively. Ts, and ΔTs represent the stopping time to make the speed of the robot
zero and an increase in the stopping time due to the acceleration of the robot
system respectively. Finally, ar(a) and ar(b) denote acceleration and deceleration
performance of the robot, respectively. (For interpretation of the references tocolour in this ﬁgure legend, the reader is referred to the web version of thisarticle.)Fig. 2. Illustration of protective separation distance considering potential
runaway motion in task space ellipsoid RSp(t0) during [t0, t0 + Δt ) . The typicalcase where an industrial manipulator is operated along a preliminary taught
trajectory from A to B is considered and is calculated along the Mahalanobis
distance.
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RSp (t0) = Sp (t0) +ar(a) Tro2 + ar(b) ΔTs2
+ (vh + vr )ΔTs
2

(10)

where ar(a) and ar(b) represent acceleration calculated by the DME, anddeceleration originating from braking performance of the robot, respectively. It should be noted that Tri, Trl, and Tro denote the reaction
time of the protective equipment, logical control device, and motor
drive respectively. In addition, ΔTs represents an increase in the stopping time due to the acceleration of the robot system. Consequently,
RSp should meet the following condition in addition to (3):Sp (t0) ≤ RSp (t0) ≤ S (t0).

(11)

2.3. Maximum speed control and energy transfer during contact
The SSM method is a protective feature for securing PSD between
the operator and the robot. In general, risk of contact can be deﬁned as
a combination of the probability of occurrence of harm and the severityof that harm [3]. In this respect, it should be noted that minor contact
can be accepted for the case where the risk of contact between thehuman and the robot is negligible. In other words, the robot may
maintain its tasks when the PSD is violated as far as the severity of thecontact risk is acceptable. However, little guidelines or agreements
have been made regarding the severity of the risk of contact, which is tobe deﬁned in terms of the probabilistic regulation. In this study, thepermissible velocity limit is discussed based on the expected transfer
energy using the biomechanical limits in ISO/TS 15066.
The kinetic energy matrix associated with the operational spaceΛ (q) for non-redundant robots can be calculated by [36,37]

Λ (q) = (J (q) M−1JT (q))−1.

Fig. 4. Battery assembly task: Human-robot collaboration scenario used in theexperiment. An example of the robot is equipped with a radio wave sensor
system using a speed and separation monitoring function.

to investigate the eﬀect of the potential acceleration of the robot in caseof runaway by using actual information of the human and the robot.
Two diﬀerent orientations were demonstrated in the experiment because the eﬀect of the potential runaway motion of the robot diﬀers
according to the posture of the robot and the relative distance vector
between the human and the robot. The third is to investigate expectedtransferred energy assuming potential contact based on the dynamic
properties of the robot and the biomechanical limit of human injury.
Assuming foreseeable misuse of the human side, the simulation was
carried out by using the instant velocity of the human and the robot.
Our experiments were performed with the permission of the institutional review board of Nagoya University (approval no. 18-16).

(12)

Further, (12) can be decomposed into

¯ vω (q) ⎤Λ−v 1 (q) ΛΛ−1 = ⎡.
⎢Λ¯ Tωv (q) Λ−ω1 (q) ⎥
⎣
⎦

(13)

Thus, a scalar value of the eﬀective mass of the end-eﬀector can be
expressed as follows:

mu = [uT Λ−v 1 (q) u]−1 ,3.1. HRC scenarios
Fig. 4 illustrates the battery assembly task performed by the participants and the robot system. Also, the task process of battery assemblytask scenario for conducting the collaboration between the robot issummarized in Table 1. In the experiment, a six-axis robot arm (VS-060,
Denso Wave, Japan) and a compressed air dispenser were used for
applying the adhesive material. Experimental tools and layout of thehuman side are explained in Fig. 5. The robot performs the repetitiveand simple task of applying the adhesive to the battery case, and thehuman operator performs a quality inspection and inserts the battery
cell into the battery case after the task by the robot is completed. Two
subjects who participated in the experiment were experts who were
engaged for many years in the production line. The experiment was
conducted until the operator completed four batteries after several
trials of assembling the test batteries. A motion capture system (Venus
3D) was used to measure the relative distance between the three-dimensional position of the operator, and the end-eﬀector of the robot. In
this study, human hand contact between the robot was considered as
the most hazardous event since the preliminary taught motion of therobot is demonstrated only around the working table. Therefore, theposition of the participant’s hands was measured, and the position of
the chest was additionally measured as the central position of theparticipant. In case of emergency, the emergency stop button is locatednear the human work area and the experiment manager.

(14)

and

iu = [uT Λ−ω1 (q) u]−1 ,(15)where mu is the reﬂected robot inertia; and iu is the reﬂected rotational
robot inertia toward the u direction. In this study, only mu is treated as
the eﬀective mass of the end-eﬀector of the robot due to the diﬃculty of
estimating iu in practice, and most of the trajectories performed by the
robot in the experiment were translational movements. Finally, considering the dynamic contact between the human and the robot, expected transfer energy E when a simple two-body model is used can be
written as follows:1
μvrel 22(16)μ−1 = mr−1 + mh−1

(17)

E=

where μ is the reduced mass of the human and the robot; vrel is therelative speed between the human and the robot; mh represents theeﬀective mass of the human; and mr denotes the eﬀective mass of therobot in the u direction.
3. Experimental design
The purpose of experiments presented in this section is ﬁrstly tocompare the separation distances between when using the 2D and 3D
sensor as a safety-related sensor. For the comparison, battery assembly
scenarios between the human and the robot were carried out. Second is

3.2. Protective equipment and test scenarios
Fig. 6 shows a block diagram of the control structure using two
diﬀerent types of sensors. Both the laser scanner (SE2L-H05P, IDEC,
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Table 1
Summary of battery assembly task scenario process .
Type of task (Subject of action)

H1 - Bottom case task (Human)

R1 - Bottom case adhesive
injection (Robot)

H2 - Upper case task (Human)
R2 - Upper case adhesive
injection (Robot)

H3 - Battery cell assembly task
(Human)H4 - Switching task (Human)

Process/ content

Classiﬁcation

Bottom case procurement
Bottom case set
Tag seal
Robot activation
Adhesive injection

MovingOperation
Inspection
Operation
Injection

Upper case procurement
Upper case set
Bottom case collection
Robot activation
Adhesive injection

MovingOperation
MovingOperation
Injection

Battery procurement
Quality check1: electricalinterference
Screwing1: middle case
Quality check2: position
check
Bottom case procurement
Tag seal
Bottom case set
Upper case insertion
Screwing2: bottom case
Quality check3: ﬁnal test
Transportation of assembled
battery

MovingInspection
Screwing
Inspection

Fig. 7. Two diﬀerent orientations of the robot. Orientation A demonstrates
frontal interaction concept. Orientation B demonstrates perpendicular interaction concept.

MovingOperation
Operation
Operation
Screwing
Inspection
MovingJapan) and radio wave sensor system (SC1211AU2, Socionext, Japan)
which uses frequency modulated continuous wave method is used tocompute the safety distance; the speed of the robot is modiﬁed by external controllers. Accordance with the calculated safety distances, thespeed of the robot is rescaled until it does not violate each safety distance. Fig. 7 shows two diﬀerent concepts depending on relative postures between the robot and the human. PRAM-t was compared according to the relative orientation between the human and the robot,
diﬀerent postures of the robot were demonstrated. Relevant safety
distances were computed based on physical parameters which are givenby the robot manufacturer. Especially, the reaction time of the logicalcontrol device Trl, and the motor drive Tro was set as 100 ms, and 50 ms
respectively. Details of diﬀerent test cases with relevance to protective
equipment have been deﬁned in the following.
3.2.1. Test case 1: layout using the laser scannerFig. 8 shows the layout of the presented Test Case 1 scenario. Safety
distance between the robot and the human was calculated based on theminimum distance criterion with sensor latency Tri of 40 ms [31]. Body
parts such as a human hand cannot be continuously detected whenusing the 2D laser scanner to monitor the movement of the operator.
Therefore, it is required to take into account an appropriate margin
distance according to the installed position of the laser scanner [31].
Accordance with the height of the Laser scanner (0.9 m), C was chosen
as 0.85 m. The safety distance between the operator and the robot wascalculated from the position of the maximum reach of the robot. The

Fig. 5. Experimental layout and tools required for battery assembly task. The
battery is complete when it is ﬁxed to the battery cell with adhesive andscrewed tightly.

Fig. 6. Block diagram of experimental hardware and software setup dependent on the safety-related sensors. The speed of the robot is modiﬁed according to therelative distance and velocity between the human and the robot.
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the SISO sensor which allows measuring only the relative distance andvelocity, simulation based on measured samples by motion capturesystem was used to investigate the eﬀect of the runaway motion of therobot. It is presumed that the runaway acceleration of the robot will
diﬀer against each orientations A and B due to the PRAM-t is represented as an ellipsoid. For the same reason, the acceleration of therobot’s runaway motion is dependent on the relative distance and direction between the robot and the human. Therefore, the runaway acceleration of the robot can diﬀer according to the relative position of
the human body parts. With respect to the eﬀect of the runway motionof the robot into the PSD, a comparison of runaway margin RSp − Sp isused for the evaluation for each body part.
In addition, the result is normalized with torque and arm length
value since the eﬀect of PRAM-t can diﬀer according to the performanceof the robot used in the experiment. However, due to the computation
payload and calculation resources, only one cycle time period of thesample from participants was used for the normalization.

experiments were conducted according to the orientations of A and B of
the robot.
3.2.2. Test case 2: layout using the radio wave sensor system
Fig. 9 shows the layout of the Test Case 2 scenario when using theradio wave sensor system. A total of three radio sensors S1, S2, and S3
were installed on the end-eﬀector and both ends of the workspace. The
speciﬁc parameters of the radio wave sensors were conﬁgured based on
their displacements and receptivities. All carrier frequency of the radio
wave sensors were set as 24.14 GHz, and sweep frequencies were set as
180 MHz. Closest distance and its velocity was calculated based on the
FM-CW method with FFT size of 2048, and the Hamming window wasused for its digital signal processing. In order to avoid radio wave interferences and ghost targets, diﬀerent sweep sampling time 1024 us,
614 us, and 409 us were used for S1, S2, and S3 respectively. Both radio
wave sensors placed on the end of the table was used to conﬁrm
whether the participant is in the workspace. The radio wave sensor
placed on the end eﬀector S1 is enabled only when the table placed S2,
or S3 gives distance less than 1 m with latency Tri of 30 ms. The PSD
was calculated based on the distance between the end-eﬀector and theclosest body parts of the operator taken by the S1. Intrusion distance C
was ignored in the PSD calculation because the relative distance between the body part of the human and the robot can be observed. The
measurement uncertainty Zd was set as 16 cm considering the measurement error of the used radio wave sensor follows the Gaussian
distribution having 3 cm of standard deviation, and coverage interval of
PLr = d requires 5.16 σ [20,21]. In the same way as in Test Case 1,
experiments were conducted for the orientations of A and B of therobot.

3.3.3. Velocity boundaries and transfer energy analysis
The purpose of the simulation is to investigate the expected transfer
energy to calculate the permissible velocity limit. For the case of using
properly designed SSM, no collision can occur except for intentional or
misuse by the human because of the nature of the non-contact oriented
collaborative system. While the SSM implementation allows avoiding
foreseeable contact, some of the contact during the application may beconditionally acceptable if the risk of the contact is minor. From thisinsight, this simulation includes the approach of estimating the expected transfer energy of potential contact in the current dynamic state.
First, the contact scenario is established using the reduced-mass twobody model and the eﬀective mass of the robot end-eﬀector derived by
the mechanical properties of the robot. After the contact scenario ismodeled, the threshold of the robot speed is identiﬁed based on thebiomechanical limit where the human feels pain. The mass of thehuman hand is assumed to be ﬁxed at 0.6 kg, and the biomechanicalenergy limit of the human hand was set at 0.49 J according to the ISO/TS 15,066 [6]. Referring to (14), we recall that the eﬀective mass of therobot diﬀers according to the relative position of the human and therobot. Therefore, the expected transfer energy was computed between
each part of the human body during the presented experiment.

3.3. Evaluation metric3.3.1. Productivity metric
In each trial, the protective equipment has been varied, which were
presumed to have an impact on separation distance and cycle time
mainly due to the decreased intrusion distance. The productivity of the
HRC application for the presented test scenarios when using diﬀerent
protective equipment was evaluated by the workspace occupancy andthe application time. With respect to the HRC analytical models for
productivity in the workspace, the following shop ﬂoor space (FS) isand an average separation distance was used for the evaluation [22]:FS = (x imax − x imin ) × (yimax − yimin ),4. Results(18)

In this section, the cycle times achieved with the diﬀerent types of
sensors and orientation in each of the test cases as deﬁned in
Section 3.3.1 are presented in the following. In addition, PRAM-t simulation and velocity boundary analysis were conducted based on theobtained motion capture data samples as shown in Section 3.3.2, and3.3.3.

where x imax and yimax are the maximum values of the x and y positions,
respectively, for the resources, including the operator and the robot;
and x imin and yimin are the minimum values of the x and y positions
during the ith task, respectively. Each x and y is deﬁned in accordancewith the layout of the workspace. In addition, the application cycle timerequired to complete the task TH is assessed as an additional metric:n4.1. Comparison of cycle times

TH = ∑ Tci,

(19)

i=1

Table 2 lists the total cycle time, average and maximum velocity of
the robot for each task when the application is held without any intrusion from the human operator. The comparison results of the mean
cycle times from the experiment are summarized in Fig. 10. In comparison to when using the laser scanner, the mean cycle time of themoving task was reduced from 17.5 s to 4.6 s and from 13.1 s to 4.7 s
for orientations A and B respectively in case of using the radio wave

where Tci is the time consumed during the i th task; and n represents thetask number conducted by the operator.
3.3.2. PRAM-T simulation analysis
The main goal of the analysis is to ﬁnd a distinction between thepresented orientations. Since the radio wave sensor used in this study isTable 2
Robot cycle time, average and maximum velocity for each tasks without intrusion .
Type of task (Robot)

Cycle time(s)Average velocity v̄r (mm/s)

Maximum velocity max(vr) (mm/s)

R-1 Bottom case adhesive injection
R-2 Upper case adhesive injection

45.9
50.6

88
94

509
452

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Fig. 10. Comparison of mean cycle time according to type of battery assembly
work. Waiting indicates the waiting time of the operator for the robot to ﬁnish
its task. The operation time includes battery assembly, attachment of the serial
number seal, and manipulation of the robot. The moving time includes activities of transportation of materials.

Fig. 8. Test Case 1: Layout when using the 2D laser scanner as the safety-related
sensor. A layout was chosen to minimize moving path and risk of falls of theoperator based on the risk assessment. Two diﬀerent orientations of the robot, A
and B, were tested.

sensor. In addition, the total mean cycle time had no much diﬀerence
according to the orientations in the case of using the laser scanner.
When using the radio wave sensor, the total mean cycle time of orientation A was shorter than that of B. In particular, radio B had thelongest mean cycle time for waiting of 35.1 s. As a result, the mean
cycle time was shortened when the orientation of the robot was A, andthe mean cycle time increased when the orientation was B.
4.2. Comparison of ﬂoor space
Fig. 11 and Fig. 12 shows the heat map of the end-eﬀector of therobot and the two participants during the task scenario of orientation A
when using a laser scanner and radio wave sensor respectively (orientation B was similar to that of A and had no much diﬀerence). Whenusing the radio wave sensor, compared with the laser scanner, the shop
ﬂoor space and average separation distance both decreased. Table 3
summarizes a comparison of the shop ﬂoor space required for each
operation in the experiment and the mean values of the separation
distances between the closest human body parts. When using the radio

Fig. 11. Heat map of the end-eﬀector and the two participants including chest
and both hands during the task scenario of orientation A for laser scannerlayout. Each mesh grid represents 1cm2 in accordance to the coordinates shownin Fig. 8.

wave sensor, compared with the laser scanner, the shop ﬂoor space FS
decreased by 58% and 20% for the orientation A and B respectively.
Also, when using the radio wave sensor, in comparison with the laser
scanner, the average separation distance decreased by 50% and 40% for
the orientation A and B respectively.

4.3. Frequency of intrusion
Fig. 13 shows the data compiled for participant 1 in orientation A,
comparing the Sp(t) and the actual separation distance S(t) from theparticipant’s chest to the robot end-eﬀector when using a laser scanneras the safety-related sensor. Fig. 14 shows data compiled for participant
1 in orientation B, comparing the Sp(t) and the actual separation distance S(t) from the participant’s left hand when using a radio wave
sensor system as the safety-related sensor. Both Fig. 13, and Fig. 14 areshown in duration time of 300 s to include one cycle for ﬁnishing thebattery assembly task among the four total battery completion. As
shown in Fig. 13, intrusions were observed during the moving task

Fig. 9. Test Case 2: Layout when using the 3D radio wave sensor system as thesafety-related sensor. Three radio wave sensors S1, S2, and S3 were installed on
the end-eﬀector of the robot, and each side end of the work area respectively.
First, when the two sensors in the side end detect the human body, the radio
wave sensor system is enabled. Second, only the radio wave sensor installed on
the end-eﬀector of the robot detects the human hand and slows or stops therobot according to the calculated protective separation distance. Finally, two
diﬀerent orientations, A and B, were tested.
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Fig. 12. Heat map of the end-eﬀector and the two participants including chest
and both hands during the task scenario of orientation A for radio wave sensor
layout. Each mesh grid represents 1cm2 in accordance to the coordinates shownin Fig. 9.

Fig. 14. Comparison of the protective separation distance Sp and the separation
distance S between the left hand of the subject and the end-eﬀector of the robot(orientation B) when using a radio wave sensor.Some epoch is highlighted toshow intrusion by the human operator during the screwing, and inspection task.

Table 3Results of the required ﬂoor space (FS) and comparison of the average separation distance (S̄ ) between the end-eﬀector and the body parts of the operator .

2FS (m )
S̄ (m)

Laser (A)Radio (A)

Laser (B)Radio (B)

4.77
1.58

2.01
0.79

4.40
1.76

3.49
0.69

Fig. 15. Acceleration calculated by DME to each body part of the participants
according to the relative orientation between the human and the robot whenusing a radio wave sensor as the safety-related sensor.

4.4. Comparison of protective separation distance and PRAM-t
For comparison of PRAM-t, runaway acceleration of the robot respect to the relative position of the human and the robot was computed
based on experimental setup shown in Section 3.2. Fig. 15 shows acomparison of acceleration calculated by DME ellipsoid using measured
position data of subjects and the robot end-eﬀector (123687 samples
total). As a result, the mean runaway acceleration of the robot decreased by changing the orientation from A to B for the chest (45 %),left hand (61 %), and right hand (51 %).
Fig. 16 shows a comparison of runaway margin RSp − Sp with respect to each body part of the participants when using the radio wave
sensor. Regarding the results, the mean runaway margin decreased by
31 %, 61 %, and 60 % for the chest, left hand, and right hand, respectively. In addition, all the maximum values of the runaway margin
during the experiment were less than 6 cm.
Finally, the simulation result was normalized by scaling the lengthof the robot arm and the maximum joint torque of the robot is shown in
Fig. 17. The simulation assumes that all the arm lengths and theFig. 13. Comparison of the protective separation distance Sp and the separation
distance S between the chest part of the subject and the end-eﬀector of therobot (orientation A) when using a laser scanner. Red rectangles show intrusionby the human operator during the moving task.

when using the laser scanner for orientation A and was same with thatof the orientation B. Also, intrusion was observed only during themoving task in case of using radio wave sensor for the orientation A. In
case of using radio wave sensor for orientation B, intrusions were observed during screwing and inspection task (Fig. 14). As a result, in caseof using the radio wave sensor for orientation B, robot halted 41 times(27 times and 14 times for participant 1 and 2 respectively) for thescrewing task of the human operator.

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Fig. 16. Runaway margin RSp − Sp of each body part of the participants according to the relative orientation between the human and the robot whenusing a radio wave sensor as the safety-related sensor.

Fig. 18. Velocity boundary of the robot end-eﬀector and expected transfer
energy to the subject when using the radio wave sensor as the safety-related
sensor for orientation A, where vrel is the relative speed between the end-effector and the human right hand, and vp is the permissible velocity limit calculated based on dynamic properties of the robot.

the robot end-eﬀector and the expected transfer energy calculated byrelative speed and eﬀective mass between the participant and the robot
when using the radio wave sensor as a safety-related sensor for orientation A. From the result, it can be known that transfer energy andrelative speed were high enough to violate the energy limitation andmaximum permissible velocity during the screwing task (180 s).
5. Discussion
5.1. Cycle time and frequency of intrusion
Contrary to what we expected, it can be known that the orientation
B requires more cycle time than that of the A as shown in Fig. 10. The
major reason for this is considered as due to the increased waiting time
for the operator until the robot ﬁnishes its task. One of the reasons thatthe waiting time has been increased compared to that of orientation A
can be explained by the intrusion of the participant into the PSD during
the regular operation of the robot. Further, the screwing task of orientation B took 10 seconds longer on average than orientation A. According to the comment from the participants, they felt unsettled when
the robot halts and it made their tasks were less productive until they
completely moved away from the allowable safety distance.
The results from the orientation A group indicate that the cycle time
when using the 3D sensor as a safety-related sensor can be decreased interms of reducing the time cost for the moving task. However, the resultof the orientation B group shows that the cycle time can rather increase
with frequent access to the PSD. It can be interpreted as more attention
should be given to an estimation of potential intrusion in the process
designing stage. Therefore, it is important to conﬁgure and utilize theappropriate layout even if the separation distance between the humanand the robot is shortened because of the 3D sensor.

Fig. 17. Simulation result using one cycle sample of the battery assembling task
conducted by one of the participants in the layout of orientation A. Robot arm
length and the maximum joint torque were scaled based on the physicalparameters of VS-060.

maximum joint torques are increased with the same proportion for the
robots used in the experiment. Further, the results were summarized toinclude all the runaway margin respect to the speciﬁc body parts of thesubjects during one cycle application of the experiment. From the results, as the length of the robot’s arm increased, the mean runaway
margin also increased. For example, when only the maximum torque
was varied, the mean runaway margin 2.7 times, and 7.5 times increased when the maximum joint torque increased 2 times, and 4 times
respectively. On the other hand, as the length of the robot arm increased, the trend tends to decrease. For instance, when only the lengthof the robot arm was varied, the mean runaway margin decreased to
37 % and 14 % when the length scale of the robot arm increased 2 times
and 4 times respectively.

5.2. Comparison of ﬂoor space and potential contact scenarios
The result of Table 3 shows the shop ﬂoor space beneﬁts whenshifting from a 2D sensor to a 3D sensor. First, it can be known that FS islargely reduced when using the radio wave sensor for both orientations
A and B compared to that of the laser scanner from Table 3. The main
reason for the FS is decreased is mainly due to the conﬁguration of the
intrusion distance is diminished when using the radio wave sensor.
Conversely, when using the radio wave sensor, the FS of orientation B

4.5. Velocity boundaries and transfer energy analysis based on dynamic
properties of the robot
In order to derive maximum permissible velocity, transfer energy
was computed in accordance with the experimental setup shown in
Section 3.3.3. Fig. 18 shows an example of the velocity boundaries of9

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E. Kim, et al.

thought that the proposed framework can link between SSM and PFL todesign and control robots for a higher level of safety and productivity.
In order to realize the fusion of plural safety functions in unstructured
environments, practice insights are fundamental, such as on-robot
sensing techniques and point of interest analysis of the robot end-effector.

decreased less than that of orientation A. Considering that the decreaseof the mean separation distance of both orientations A and B were similar, this is thought to be due to the diﬀerence of the moving range ofthe operator caused by the diﬀerence of the layout, such as the directionof the battery displacement.
While 3D sensors allow higher eﬃciency, it is important to secure
the operator safe taking into account potential contact scenarios. Due tothe conventional 2D sensors force the HRC system to consider additional intrusion distance into the PSD, the probability of contact between the robot and the human due to the malfunction of the robot has
little been discussed. However, the comparison of FS shows that theworkspace sharing HRC task may result in physical contact since theseparation distance between the human and the robot has been decreased below the length of the human arm (850 mm). Considering therange of the human arm motion and that of the robot when using the 3D
sensors, the risk of physical contact may not be ignored during the
malfunctioning state of the robot or the foreseeable misuse of thehuman side.
Another to be mentioned is that FS only evaluates only the movement of the operator’s body. In practice, required shop ﬂoor spacecomprises space occupancy of the robot as well as the human operator.
In the present study, the shop ﬂoor space of the end-eﬀector of the robot
was 0.09m2, which is expected that other applications will show difference (Fig. 12). Thus, in a strict manner, a proper evaluation of shop
ﬂoor space should include the whole movement of the robot arm during
the task scenario.

6. Conclusion
In this study, two relative postures of the operator and the robot
were examined to estimate potential runaway motion of the robot—frontal orientation (A), and perpendicular orientation (B). From theresult, it was found that the volume of potential runaway motion space
for the case of orientation A is larger than that of B because orientation
A is more aﬀected by the main axis of the acceleration ellipsoid than B.
Therefore, in order to reduce the runaway distance, it is eﬀective to
avoid the frontal arrangement of robots and humans.
Considering the basic concept of risk, it is reasonable to take theseverity of contact into the SSM function. In this respect, expectedtransferred energy was used to calculate the maximum permissible
velocity of the robot assuming potential contact based on the dynamic
properties of the robot and the biomechanical limit of human injury.
The proposed framework is thought to be useful to link between SSM
and PFL to design and control robots for a higher level of safety andproductivity.
CRediT authorship contribution statement

5.3. PRAM-T and eﬀect of relative human and robot orientation
Eugene Kim: Conceptualization, Methodology, Software,
Resources, Data curation, Validation, Formal analysis, Writing - originaldraft, Writing - review & editing, Supervision, Investigation,
Visualization. Yoji Yamada: Methodology, Supervision, Writing - review & editing, Project administration, Funding acquisition. Shogo
Okamoto: Supervision, Visualization, Project administration, Writing review & editing. Mikihiro Sennin: Software, Validation, Data curation, Formal analysis. Hiroki Kito: Software, Data curation.

As shown in Fig. 16, the mean runaway margin RSp − Sp of orientation B was smaller than that of A for all of the measured bodyparts. The set of runaway accelerations of most commercially available
vertical articulated robots, including the VS-060 used in this experiment, can be expressed as an ellipsoid through the DME, and the main
axis tends to be formed in the frontal direction of the robot. In the samecontext, the reason why the volume of PRAM-t for the case of orientation A was larger than that of B was that orientation A was more
aﬀected by the frontal main axis of the acceleration ellipsoid than B. In
the experiment, although the increment of the PSD due to PRAM-t wasestimated to be smaller than 6 cm, it may increase depending on theacceleration/deceleration performance of the robot, the reaction timeof the robot, and the traveling speed of the robot. Especially, from theresult of Fig. 17, it can be known that the acceleration of the robot in
the runaway state is proportional to the power of the motor. On theother hand, acceleration in the runaway state had a negative relationship with the increase of the robot arm length which can be explainedby increasing the length of the arm can make the inertia matrix bigger.
However, considering the mass of the robot arm may also increase
according to the length of the arm gets longer, changing the reach of therobot might aﬀect more than the result shown in this study.

Declaration of Competing Interest
The authors declare that they have no known competing ﬁnancial
interests or personal relationships that could have appeared to inﬂuence the work reported in this paper.
Acknowledgments
This study was conducted through the “Knowledge Hub Aichi”
project (Stage II-R6), which is ﬁnancially supported by the Aichi
Science & Technology Foundation.
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