The final publication is available at
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Noname manuscript No.
(will be inserted by the editor)

Towards a socially acceptable collision avoidance fora mobile robot navigating among pedestrians using apedestrian model
Masahiro Shiomi · Francesco Zanlungo · Kotaro Hayashi · Takayuki
Kanda

Received: date / Accepted: date

Abstract Safe navigation is a fundamental capability
for robots that move among pedestrians. The traditional approach in robotics to attain such a capability
has treated pedestrians as moving obstacles and provides algorithms that assure collision-free motion in thepresence of such moving obstacles. In contrast, recent
studies have focused on providing the robot not only
collision-free motion but also a socially acceptable behavior by planning the robot’s path to maintain a “social distance” from pedestrians and respect their personal space. Such a social behavior is perceived as natural by the pedestrians and thus provides them a comfortable feeling, even if it may be considered a decorative element from a strictly safety oriented perspective.
In this work we develop a system that realizes humanlike collision avoidance in a mobile robot. In order toachieve this goal, we use a pedestrian model from human science literature, a version of the popular Social
Force Model that was speciﬁcally designed to reproduce
conditions similar to those found in shopping malls andother pedestrians facilities. Our ﬁndings show that the
proposed system, which we tested in two-hour ﬁeld trials in a real world environment, not only is perceived
as comfortable by pedestrians but also yields safer navigation than traditional collision-free methods, since it
better ﬁts the behavior of the other pedestrians in thecrowd.
Keywords Safe navigation, Collision avoidance,
Pedestrian modeling, Field experiments
Masahiro Shiomi, Francesco Zanlungo, Kotaro Hayashi, Takayuki
Kanda
ATR-IRC, Kyoto, 619-0288, Japan, and JST, CREST
Tel.: +81-774-95-1432
Fax: +81-774-95-1408
E-mail: m-shiomi@atr.jp

Fig. 1 The video frames and the illustrations show a real world
situation in which using a traditional collision-avoidance method
generated a possibly unsafe situation. (a) Initially, the pedestrian
did not notice the robot. (b) Then he unexpectedly found therobot in front of him and felt unsafe although the robot had
already started to avoid him; (c) as a consequence the pedestrian
quickly moved aside and (d) looked at the robot with surprise.

1 Introduction
Safe navigation for mobile robots remains a major research topic in robotics. Due to recent progress in ﬁelds
such as localization techniques, robots are now much
more capable of moving in real-world environments, enabling applications such as guiding people in museums[1, 2] and supermarkets [3], or delivery to oﬃces [4].
Collision avoidance with respect to humans is an
essential element for safe navigation in human environments. The traditional approach to robot collision
avoidance considers people as moving obstacles and applies collision-avoidance techniques. Various planning[5–8] and prediction [9–12] techniques have been developed, and robots are capable of planning their trajectories to avoid undesired physical contact with pedestrians.
However, we have experienced some diﬃculties whenusing traditional collision-avoidance methods in real-The final publication is available at
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world environments. Fig. 1 shows one of these troublesome circumstances, in which a pedestrian initially did
not notice the robot, and, when he eventually saw it,
was surprised and jumped aside to avoid it. In this setting the robot was correctly following a collision free
trajectory, but its behavior was not perceived as safeby the pedestrian. This and similar potentially unsafe
conditions, or better, conditions that convey a feelingof being unsafe to people, usually arose when pedestrians perceived the robot only when it was quite close tothem. Even if they did not lead to any collision, theycause abrupt motions in pedestrians, that may be potentially dangerous.

After determining which parameters values should
be used in the robot version of the model in order to obtain trajectories as similar as possible to the pedestrian
ones, we explicitly verify that our system is perceived assafer than traditional avoidance methods. We ﬁrst test
it in single-person settings and ask to participants torate its performance compared to a traditional avoidance method, and then operate it in a real world environment to test that it does not cause any potentiallyunsafe situation.

In this paper we are going to pose, and test, thehypothesis that human-like collision-avoidance is indispensable to avoid these situations that are perceived as
dangerous by pedestrians, and that, by causing sudden
reactions, may be potentially unsafe. We will thus claimthat human-like collision avoidance is indispensable forrobots to safely navigate among pedestrians, who expect other moving agents (pedestrians or robots) tofollow given “social norms”. An agent that does not respect such social norms, despite having a good planningcapability for collision-free motion, would be perceived
to behave unexpectedly and generate possibly unsafe
behavior. While human behavior can involve complex
cognitive processes, human science studies show thatthe resulting collision avoidance trajectories can be reproduced to a good extent using simple models in which
each agent (i.e., pedestrian) only reactively avoids local
collisions [13].

2.1 Collision-free navigation

In this paper, we apply the pedestrian model introduced by [13], and brieﬂy described in appendix A,
which was explicitly developed for relatively low-density
situations like those occurring in a shopping mall, toachieve collision avoidance in a diﬀerential drive wheeled
robot with a humanoid torso, navigating through a human environment. The merits of using the pedestrianmodel are twofold: ﬁrst, since it is a model of humansocial behavior, it is likely to produce human-like motion in a robot and thus provide comfortable feelings topedestrians in collision avoidance, as previous studies[14,15] have shown. Second, since the pedestrian model
was developed to describe many-person settings, it canbe easily applied to such situations, that were not tackled or resulted in expensive path planning computations in the previous attempts to develop socially acceptable navigation systems. On the other hand, theimplementation of the pedestrian model on the robot is
not straightforward due to the limited perception andlocomotion capabilities of the machine with respect tothe human ones.

2 Related work

Collision-free navigation techniques have been extensively studied in robotics. A basic method is the dynamic window approach [5], whose extension and development have been widely researched. Stachniss andBurgard integrated path-planning into collision avoidance [6], and Seder and Petrovic developed the timevarying dynamic window (TVDW) method by considering moving obstacles [7]. While the dynamic window
approach mainly takes into account a small neighboring area around the robot (usually a “stop distance”
within which the robot can stop), other approaches,such as collision cones [8] and velocity obstacle [16],consider the motion of objects far from this neighboring area. Many techniques have also been developed tomake planning computationally feasible, including the
D-Lite algorithm [17] .
Research eﬀorts have also improved the prediction
accuracy of people’s future behavior. A basic method topredict future behavior is to perform a velocity-based
linear projection [7, 9] while other researchers have used
a pedestrian model to predict people’s future behavior
more accurately in tracking [10]. These methods provide reasonable approximations for short-term behavior, but they are not reliable for long-term prediction;
to overcome this limitation, some researchers have usedstatistical knowledge from a large amount of previously
observed trajectories in the environment [11, 12].
Another approach is to refer to a model of people’s behavior for planning or prediction. Henry et al.
[18] proposed a method to learn an eﬀective planningof robot navigation in a crowded environment using apedestrian crowd simulator, while Tamura et al. [19]used a pedestrian model to predict people’s collision
avoidance toward the robot, allowing the latter to better avoid pedestrians. Ratsamee et al. realized a collision avoidance behavior that uses a pedestrian model
taking also in consideration the body pose and face orientation of pedestrians [20].

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Such planning and prediction techniques can provide collision-free trajectories to robots, but they do
not necessarily generate a behavior that resembles people’s collision-avoiding strategies and norms. On thecontrary, the purpose of this study is not to use thepedestrian model as a prediction tool, but to explicitly
introduce a human like collision avoidance behavior in
the robot in order to make its navigation perceived as
more natural and thus to avoid potentially unsafe situations.
Fig. 2 Overview of our framework.

2.2 Human-like social behavior in navigation
Following the seminal work of Hall on human interactions [21], a few recent studies in robotics have reproduced human-like social behavior in human-robot interaction. Some of these works focused on the importanceof proximity and reported, for example, that people try
to maintain a 0.45 ∼ 1.2 m distance when interactingwith a robot [22, 23], and a motion planning techniqueconsidering such psychological constraints has been developed [14]. Pacchierotti et al. developed a robot thatavoids intruding into the personal space of a walking
person by starting to deviate from its path 6 m fromthe on-coming person to create space [15]. Kirby et al.
proposed a constraint-optimizing method for personacceptable navigation for a mobile robot, which considers personal space as a social convention [24]. Pandey
et al. have developed a framework towards a sociallyaware mobile robot by considering social conventions
such as human proximity guidelines and clearance constraints [25]. Qian et al. have developed a frameworkfor human-compliant robot navigation which considers
a set of safety strategies to gurantee human physicalsafety and mental comfort [26].
From a diﬀerent perspective, Lichtenthaler et al.
have focused on legibility and perceived safety in crossing situations and show the eﬀectiveness of legible motion in providing impressions of safety [27]. Rios-Martinez
et al. have developed a navigation method considering human comfort, by using a stochastic and adaptive
optimization algorithm [28]. Moreover, several learning
approaches are proposed; Henry et al. have used inverse reinforcement learning to learn navigation behavior from a huge amount of example paths. They conﬁrmed the eﬀectiveness of the approach by using a realistic crowd ﬂow simulator [18]. Also Luber et al. takea learning approach by using paths of people in public
space to realize socially-aware robot navigation [29].
However, these previous approaches suﬀer of two
limitations. First, they address only single-person situations and are diﬃcult to apply to interactions withmany people, which are clearly the case in real-world

settings. Second, it has not been investigated whether
these human-like behaviors are indispensable for safe
navigation, or if they are just decorative elements not
required to safely deploying mobile robots in daily environments.
The present study addresses these two issues by using a pedestrian model that allows reproducing humanlike behavior even in many-person settings, and by testing the proposed system in a real world environment toreveal whether such human-like behavior is indispensable for safe navigation, checking if its more natural
behavior avoids generating sudden and potentially unsafe motions in pedestrians.

3 Using the Pedestrian Model for sociallyacceptable collision avoidance
3.1 Problem deﬁnition
The purpose of this paper is: To investigate the eﬀect of
the introduction of a human-like collision avoiding system for the deployment of a robot in a real world humanpedestrian environment. The proposed collision avoidance system has thus to be able to avoid the pedestrians
in a way that is perceived as natural and safe by them.
Furthermore, the system has to be stable enough to be
deployed in safety in a real world environment, and thepresence of the robot in such an environment has notto generate sudden and potentially dangerous motionsin the surrounding pedestrians.
In order to accomplish this goal we need to:
1. Calibrate a human-like collision avoidance model forour robot: based on the results of [13] we assume that
the CP-SFM model (speciﬁed by Eqs. (1) and (3),see appendix A) gives a good enough approximation of the pedestrians’ avoidance strategy, at least
for navigation in a shopping mall or similar environment. To account for speciﬁc interaction patterns

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with our robot, we perform experiments in whichhuman subjects interact (avoid a collision) with ourrobot, and use the method of [13], as described in
section A.2, to ﬁnd the values of parameters Ar andBr that, substituted in Eq. (3), better describe howpedestrians avoid our robot (section 3.3). Our hypothesis for the implementation of an avoidance system considered as natural and safe by the pedestrians is to reproduce in the robot the same avoidance
behavior that pedestrians had with respect to it in
the calibration experiment. In order to do that, we
will correct the parameters Ar and Br to values Arc
and Brc that account for limitations in the robot’s
motion (section 3.4).
2. Provide a safety system: we use the CP-SFM model
to provide a collision avoidance felt as natural andsafe by the pedestrians, but this method has notbeen developed to provide a safe navigation for arobot system in the sense discussed by [30]. For thisreason we provide our method with a backup safetysystem using the well-known traditional planning
method for safety of [7] (Section 3.5).
3. Evaluate the perception that pedestrians have about
the collision avoidance behavior of the robot: we perform controlled experiments with subjects to conﬁrm that our system, which has been developed tobehave naturally, i.e. in a human like manner, is wellperceived by pedestrians. During the experiments
we compare our method with an eﬃciency oriented
one, and ask to the subjects to rate how comfortable
their interaction with the robot was (more speciﬁcally, if they could walk keeping their preferred velocity, if they felt their path to be collision free, andwhat was the overall evaluation of the robot’s behavior; section 4.1).
4. Test that the robot can be safely deployed in a realenvironment: we conduct a ﬁeld test to conﬁrm that
our robot may safely navigate a pedestrian environment; furthermore we compare it to a traditional
avoidance method to test that it produces less sudden motions in the surrounding pedestrians (andthus less possibly unsafe situations; section 4.2).
To accomplish tasks 1 and 2 we rely on previous contributions [13, 7], that have nevertheless to be adapted
to our system. Task 3 resembles the investigation performed by [15], while task 4 may be considered the main
original contribution of our work. As described in section 4.2, our main criterion for evaluating the safetyof the robot system is to test that it may be deployedin the environment without disrupting the normal ﬂow
and behavior of surrounding pedestrians; see our discussion in section 4.2 for a comparison with the usual
meaning given to the term safety in robot navigation,

as deﬁned for example by [30].
Obviously this paper does not completely fulﬁll all therequirements for a safe and socially acceptable navigation system that may be used in a real world environment for practical applications, and in section 5 wediscuss the problems that have to be coped with before
achieving such a goal.

3.2 Hardware
We used a 120-cm-tall, 60-cm-wide humanoid torso robot
whose mobile base is a Pioneer 3-DX (Active Media),
at a maximum velocity of 750 mm/s and a preferredvelocity of 700 mm/s (the maximum acceleration is
600 mm/s2 ). The pedestrian model needs information
about people’s positions far from the robot, which is noteasy to collect using only the robot’s on-board sensors.
Thus, we used eight laser range ﬁnders and applied thehuman-tracking system described in [31].

3.3 Calibrating the Social Force toward the robot:
H-R (human-robot) model
We speciﬁcally calibrated the pedestrian model on humanrobot interaction with our robot to account for the potential diﬀerences in the collision-avoiding behavior between a pedestrian and the robot with respect to theinter-pedestrian interaction.
In the data collection experiments, performed in a
shopping mall corridor, the robot moved straight toward a participant at 700 mm/s, and the participant
walked toward the robot starting from a distance of 18
m. Participants were instructed to walk freely toward
a goal located behind the robot, but were informedthat the robot would not change its course to avoid
collisions. Fourteen subjects participated to the experiments, each participant repeating the trial nine times.
We used the genetic algorithm described in section A.2
to select the parameter values that maximize the similarity among the trajectories generated from the pedestrian model and those obtained in the data collection.
Calibration yielded parameter values Ar =0.62,
Br =1.07, which generate a collision avoidance behavior
that does not qualitatively diﬀer from the inter-humanvalues of [13] (Ah =1.13, Bh =0.71), at least for the headon encounter experiments that we performed to calibrate the robot. More in detail, since Ah > Ar , themaximum interaction intensity is lower towards robots,
but since Br > Bh the interaction range with robots
is wider. It is nevertheless important to notice that for
the head-on experiments, the interaction distance d′ij of
Eq. (3) is typically in the order of ≈ 1 meter, for which

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the (Ah , Bh ) and (Ar , Br ) parameters yield very similarvalues. We may thus assume that the behavior of pedestrians with respect to our robot is basically equivalent
to the inter-pedestrian one, even if we refer to section
5 for a discussion of the limitations concerning the useof only head-on encounters for calibration.

3.4 Calibrating the force taking into accountlocomotion capabilities
In our study, position information is provided by theenvironmental human-tracking system [31], while therobot controller (Fig. 2) converts the output of Eq. (3)into velocity commands to navigate the robot. Namely,
the pedestrian model outputs the eﬀect of the social
forces as an update in the Cartesian velocity (vx ,vy ),and the controller translates it into a polar coordinates
velocity command (vp , ωp ) to be implemented in thewheeled locomotion used in our robot. To compute thetarget velocity, the system should ideally use the H-R
parameter values in Eq. (3) However, the robot’s motion capability is limited by its hardware (e.g., slow acceleration and inability to move aside by being a diﬀerential drive robot), while limitations and delays in theperception system may aﬀect the computation of theinteraction forces. For these reasons a straightforward
application of Eq. (3) with the H-R parameters (Ar , Br )by the robot controller may result in real robot trajectories quite diﬀerent from those that would result froma numerical integration of the equation using Ar , Br
(we will refer to this latter trajectory as the “ideal trajectory”, meaning the trajectory that the robot should
ideally follow in order to resemble the behavior of pedestrians in the experiment of section 3.3).
To compensate this diﬀerence, we further calibrated
the values of the pedestrian model parameters to obtainin the real robot system a trajectory as similar as possible to the “ideal” one. In order to do that, we numerically computed a few trajectories in diﬀerent settings
using the “ideal” H-R model (Eq. (3)) and ﬁxed the parameter values used by the robot controller so that the
motion of the real robot was as close as possible to theideal one. As a result, we found that both the intensity
and range of the social force for the real robot should
increase to Arc = 0.93, Brc =1.61 (robot controller parameters), which are 1.5 times larger than the original
H-R model parameters, in order to reproduce as faithfully as possible the ideal trajectories in the real robot
system.

3.5 Safety system
The collision avoidance system described by Eqs. (1)and (3) has not been designed to be safe in the sense
discussed in [30]. While the system described by theequations is arguably collision free in the pedestrian
density of interest for the scope of this work, this is true
for the noiseless “ideal” trajectories, and could fail when
implemented in a robot with speciﬁc motion and sensor
limitations. Furthermore, the model has not been designed to deal with pedestrians whose behavior is very
far from the norm (for example, very low attention levels, or possibly dangerous curiosity driven approaches
to the robot). For these reasons we introduced in oursystem a backup safety check.
In detail, the polar coordinate (vp , ωp ) velocity command computed by the robot controller is examinedusing a safety-check mechanism. We implemented thissafety system using the TVDW method [7], whose window time was set to 1.5 s, a time interval long enoughto stop our robot. For the safety implementation, thepedestrian future positions were projected using theiraverage velocity from the previous 0.5 s. These parameters were chosen on the basis of the maximum acceleration and velocity of the robot, along with the precisionof the tracking system. In detail, since the maximum
velocity of the robot is 750 mm/s, and the acceleration
is 600 mm/s2 , the robot can stop in 1.5 s even if a 200
ms delay occurs. We also empirically veriﬁed that a 500
ms average over the velocity output of the tracking system provides the best information about the pedestrianvelocity, by ﬁltering out noise.
The TVDW method uses the robot’s and pedestrians current velocities to project their positions in the
future and computes a safe but maximally eﬃcient pathfor the robot. It does not include notions of “sociallyacceptable distance” like those investigated by [15], so
in a head-on approach like those of sections 3.3 and 4.1
it will have a tendency to deviate later and less thanour method (or not to deviate at all, if the pedestrian
avoids the robot in advance). As a result we may expect, in normal conditions, the safety system to havevery little or no eﬀect on the robot’s motion.

4 Evaluation
We conducted two diﬀerent evaluation tests, in which
we compared the proposed method with the eﬃciency
oriented one of [7], which was implemented using thesame parameters of the safety system described in 3.5.
1. Evaluate the perception that pedestrians have about
the collision avoidance behavior of the robot: we use

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a controlled experiment with head-on collision setting, in which the subjects are asked to rate the collision avoidance behavior of the robot. The purposeof this experiment is to test and evaluate our system
in a simple scenario before deploying the robot in an
uncontrolled real world environment, and also to reproduce the results found by [15] while using a rulebased model under a similar single person controlled
setting (the main diﬀerence between our scenario
and theirs is that in our work also the controlled
experiments with subjects were performed in a realworld environment, and not in the laboratory).
2. Test that the robot can be safely deployed in an uncontrolled real environment: we conduct a ﬁeld test
to conﬁrm that our robot may be operated in a realworld environment without disturbing the natural
ﬂow of pedestrians. In the scope of this experiment,
our deﬁnition of safety diverges from the one of [30];we are interested to check the occurrence of situations in which the robot causes sudden and possibly
dangerous movements in the surrounding pedestrians.

4.1 Evaluation of comfortable feeling4.1.1 Method

condition, the average speed of the robot was 0.65m/s
and the pedestrian velocity was 1.16m/s, while the average minimum distance between the robot and pedestrians increased to 0.87m.
Fig. 3 shows the questionnaire results. We conducteda pair-wise t-test for each item of the questionnaire.
There were signiﬁcant diﬀerences between the impression of the pedestrians when interacting with the robot
using the proposed method or with TVDW; in detail,for obstruction-free we have (t(25)= 3.231, p=.004, r=0.54),
regarding whether they were able to walk at their preferred velocity we have (t(25)=3.180, p=.004, r=0.54),
and for overall evaluation (t(25)=2.964, p=.007, r=0.51).
The results suggest that the pedestrian model was ﬁt
to the participants’ natural way of walking and allowed
them to walk at their preferred velocity, and for thesereasons they perceived the robot as obstruction-free.
Figs. 4 and 5 show the average robot and participant trajectories during the collision-avoiding experiment. The robot started moving from the left side, andthe participant started from the right side. Under the
proposed method condition, the robot started deviatingfrom its straight trajectory after four seconds, i.e., at a
distance of approx. 8 m from the pedestrian, a value inagreement with the one reported in [15]. On the other
hand, under the TVDW condition, the robot’s collisionavoiding behavior occurred much later: after t = 8 s,neither the robot nor the pedestrian had deviated fromtheir straight path. Under this condition the pedestrian
deviated more strongly from the straight path (at t=12sec), and the robot’s collision-avoiding behavior was reduced.

The evaluation was conducted in the same shopping
mall corridor used for data collection. Participants freely
walked toward a goal, while a robot moved from thatgoal toward the point from which the participant started
walking, i.e. the robot and pedestrian had to avoid eachother to reach their target. Participants started walking
at a 18 m distance from the robot.
An analysis of these average trajectories suggests
The experiment was conducted as a within-subjects
design, and the order of the sessions was counter-balanced. that while under the TVDW condition the collision
avoiding load was mainly on the pedestrian, that clearly
25 Japanese subjects participated in the experiment (14felt the robot’s avoiding behavior to be too reduced
females and 11 males, average age 23.1 years with S.D.
or too delayed, under the proposed method the colli5.6 years) and ﬁlled out a questionnaire for each session avoidance load was mainly on the robot. As a resion after reaching the goal. In the questionnaire parsult, as shown by the results of Fig. 3, the pedestrians
ticipants graded their impressions of the behavior offelt obstruction-free and could walk with their own prethe robot towards them on a 1-to-7 point scale, where
ferred velocity. The method of [13] is intended to take in
7 stands for the most positive, 4 is neutral, and 1 is theaccount the reciprocity in collision avoiding when calmost negative impression, based on the following criteibrated on pedestrian-pedestrian interactions (see also
ria: 1) obstruction-free, 2) their own ability to maintain[32] about the importance of reciprocity). Nevertheless,their preferred velocity, and 3) overall evaluation.
our calibration process on single person human-robot
interaction seems to have led to an anticipation of the
4.1.2 Resultcollision avoidance behavior on the robot’s part, which
Under the TVDW condition, the average speed of theis well perceived by the pedestrians. The evaluation ofrobot was 0.65m/s and the one of the pedestrians was
section 4.2 will show that our robot does not “over1.17m/s. The average minimum distance between the
avoid” the pedestrians and can be stably deployed in arobot and pedestrians was 0.76m. Under the proposed
multi-person real world environment.

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Fig. 3 Impressions from participants.
Fig. 6 Map and image of the ﬁeld trial site.

Fig. 4 Average trajectories under the proposed method.

crowd by causing abrupt motions in the surrounding
pedestrians. For simplicity sake’s we name such situations as “unsafe”, even if this is not the usual meaning assigned to this term in robot navigation [30]. We
video recorded the scene of the ﬁeld trial, and we reliedon two coders to understand if the robot was causing
any “unsafe situation”. In detail, for each human robot“encounter”, i.e. for each person who passed within 5m from the robot, we determined whether the robot’s
behavior was safe for the pedestrian using the following
criterion:– the robot behavior was judged unsaf e if the pedestrians appeared to feel themselves to be in an unsafe
situation, e.g., about to collide with the robot, andquickly changed their walking speed and/or moving
direction (e.g., jumped aside) to avoid the robot;– otherwise, the robot’s behavior was coded as saf e.

Fig. 5 Average trajectories under the TVDW method.

Fig. 8 shows an example of unsafe behavior.

4.2 Field test of safety in navigation

4.2.2 Result

4.2.1 Method

In the evaluation, we conducted a two-hour test foreach condition, each test consisting of 27 trials. Under
the TVDW condition, there were 168 encounters, i.e.
168 visitors walked within 5 m from the robot, while
under the proposed method condition, there were 160
such encounters. Two coders classiﬁed the interactions
between all 328 visitors and the robot as safe or unsafe by observing the recorded videos. Cohen’s kappa
coeﬃcient was 0.89, indicating that their observations
were highly consistent. Moreover, for consistent analysis, they discussed and reached a consensus on all theobserved situations.
As a result, six behaviors over 168, i.e., 3.6% of theencounters, were coded as unsafe under the TVDW condition, but no unsafe behavior was found when using
the proposed method. A χ-square test revealed signiﬁcant diﬀerences in the ratio of the occurrence of unsafe
behaviors (χ2 (1) = 5.821, p=.030).

This evaluation was conducted as a ﬁeld trial in a shopping mall, and thus the robot did not interact with instructed subject, but with uninstructed pedestrians ofa real world crowd. The robot was placed in a 10 x 20 m
area of a large corridor (Fig. 6) bordered on both sides
by restaurants and a variety of shops. The visitors weremainly families, couples, and sightseers, all of whom
could freely walk down the corridor. The robot was autonomous in the experiments except for the start signal
sent by an operator to trigger it to move. After sending the signal, the robot started to move from points
A/B to B/A fully autonomously. We deﬁned a single
movement from these points as one trial.
Our aim was to reveal whether the robot could navigate in a socially acceptable way in a real-world environment, without disrupting the normal ﬂow of theThe final publication is available at
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8

The experiment results revealed that the robot using the proposed method is perceived as safer than
the alternative method, suggesting that when using thepedestrian model the robot moves similar to humans
and gives the impression of performing socially acceptable collision-avoiding. As a result the proposed method
does not disrupt the normal ﬂow of the crowd by causing sudden motions in the surrounding pedestrians. Thisconsideration is supported by the observation of somescenes in which pedestrians and the robot collaboratively avoided collisions (Fig. 7). In this scene, the robot
approaching two pedestrians starts to change its moving direction a few seconds before reaching the contact
distance; at the same time the robot does not deviate
very strongly from its trajectory, but does it in such a
way that the collision is smoothly avoided through thecollaboration of the two pedestrians, who deviate fromtheir trajectories in a similar way. Such a behavior (anticipating the collision-avoiding behavior in order not
to surprise the opponent, and enhancing collaborationfrom other pedestrians) is an example of socially acceptable avoidance behavior. This “social norm” wasnot explicitly introduced in the pedestrian model, but
was implicitly coded in the parameter values throughthe learning process based on human trajectories.
In contrast, the unsafe behaviors generated by the
TVDW method appear to reﬂect the lack of any attempt to reproduce such a socially acceptable behavior. This method plans a trajectory that is assured not
to collide with pedestrians by assuming them to bemoving obstacles with constant velocity, an assumptionthat results to be strong enough to provide collisionfree navigation. As expected, this method never caused
collisions and was coded as “safe” (according to themeaning given to this term in this section) in the large
majority of the encounters (96.4%) during the experiment. However, despite being collision free, the robot
was sometimes perceived as unsafe by the pedestrians,
since it might not correspond to the expected “social
norm”. Fig. 8 shows a scene coded as unsafe. Here thepedestrian started to slightly deviate from his course
before getting close to the robot (Fig. 8-left). The robot
waited to avoid the pedestrian until reaching a close
distance, where it stopped and started to turn right(Fig. 8-middle). This behavior was felt as unsafe by thepedestrian, who probably expected the robot to start
to deviate much earlier and more smoothly. The pedestrian eventually almost jumped aside to avoid the robot(Fig. 8-right). The occurrence of such a possibly unsafe
situation suggests that the robot’s behavior was not socially acceptable.
We further analyzed how the robot behaved in more
crowded situations. Fig. 9 shows a scene in which theFig. 7 Safe behavior under the proposed method.

Fig. 8 Unsafe behavior under the TVDW method.

proposed method successfully navigated the robot in a
many-people setting. The robot was initially heading
toward a group of people, which yielded a social force
strong enough to make the robot turn right to avoid allof them. After avoiding the ﬁrst group, the robot againchanged its moving direction to successfully avoid a second group. This example illustrates that the proposed
method reproduces human-like collision avoidance evenin many-people settings.
In contrast, the TVDW method generated awkward
situations in multi-people settings. Fig. 10 shows a situation in which the robot controlled by the TVDW
method headed toward a group of pedestrians. It passed
through the group, since the pedestrians yielded before
the robot started to change its motion to avoid them.
The members of the group had to part to allow the
robot to pass, a situation that is seldom observed ininter-pedestrian interaction. The collision-free computation of the TVDW method was performed correctlyin this situation, since at the moment the pedestrians
parted to avoid the robot, the latter had still plentyof time to modify its trajectory to avoid the collision.
Nevertheless this collision-avoiding behavior occurred
too late to be perceived as acceptable by the pedestrians.

The final publication is available at
http://link.springer.com/article/10.1007/s12369-014-0238-y9Fig. 9 Video frames and illustration of a situation in which
the robot safely navigated through a crowd using the proposed
method.

Fig. 10 Video frames and illustration of a situation in whichsome pedestrians had to part to avoid the robot using the TVDW
method.

5 Discussion
5.1 Alternative methods for safe navigation
In this research work, in order to attain a safe collisionavoidance movement for the robot, we used a pedestrianmodel as a compact method to reproduce human-likeand thus socially acceptable collision avoidance behavior for a robot deployed in a pedestrian crowd. A similar result could probably be reproduced using diﬀerent
methods as, for example, a motion planning methodwith constraints about social distance or personal space.
We nevertheless stress that introducing the concept ofsocial space in a motion planner, even if done in accordance with social studies, does not imply necessarily
realistic behavior in a multi-person setting. Concepts
as social distance are usually introduced for two people
situations and very often for static settings. Thus theintroduction of these concepts in a motion planner has
to be performed with a calibration on real pedestrian
trajectories, which may be a non-trivial process.
We believe that using a pedestrian model calibrated
on actual pedestrian behavior is a relatively easy way toeﬀectively reproduce human-like collision-avoidance inthe robot. Since the pedestrian model uses simple equations to calculate the social forces to represent humanlike collision-avoidance movements, it is not only simple to implement and eﬃcient from a computational
point of view but also more stable to environmental
changes than traditional approaches that use relatively
complex equations. As written above, the pedestrianmodel calculates the social force based on collision predictions with regards to people. Therefore, it is robust
to changes of the density in the environment, while a
traditional approach for safe navigation might need ﬁnetuning of many parameters to be used at diﬀerent densities. Some previous studies required explicit learning
about the environment to be able to predict people’s
future behavior, e.g., where people would go and how
they would walk [33–35]. Since our method only performs a simple velocity-based prediction, it does not
require previous knowledge or learning of the environment properties. Based these considerations, we thinkthat using a pedestrian model as the one proposed inthis work is an appropriate way to attain human likecollision avoidance for a robot moving in pedestrian facilities.
5.2 Future work
This work deals with socially acceptable collision avoidance with respect to pedestrians in a possibly dense
but open environment. In order to safely navigate therobot in a pedestrian facility, all the aspects of navigation have to be developed, such as global path planning,and collision avoidance with respect to non-human obstacles of diﬀerent size and velocity. Others issues could
be investigated and represent possible future research
topics:The final publication is available at
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10

1. Pedestrian social norms. In this paper we copedwith the development of a socially acceptable navigation system for a mobile robot, but our approach
was based on the analysis of dynamical features ofpedestrian trajectories, under the assumption thatin a collision avoidance maneuver the behavior of
the involved pedestrians is completely symmetrical.
Nevertheless human behavior is more complex, due
to the presence of a few social norms. For example pedestrians are known to have a culturally dependent tendency to walk on the left or right sideof a corridor [36], which seems to be connected toa bias in collision avoidance [37]; there are usuallysome social priorities involved in deciding who has
to give the way [38]; furthermore social groups are
an important component of pedestrian crowds [39],and the correct behavior with respect to this groups
has to be taken in consideration, and introduced in
the pedestrian model. Recently, Zanlungo et al. [40]have reported that Japanese pedestrians have a tendency to avoid on the left and overtake on the right,
with eﬀects on the density and velocity distributions, and extended the CP model in such a way tocope with this social norm. The same authors introduced also a SFM based description of the behaviorof small social pedestrian groups [41].
2. HRI model. The pedestrian model used in this paper
only considers people’s goal-directed and collisionavoiding behaviors, while ignoring other social activities that humans may perform in pedestrian facilities. Thus, any pedestrian behavior that goes beyond the model would break the assumptions underwhich our system works. A case of particular interest for robot studies regards those pedestrians that
actively approach the robot to interact with it. Suchhuman-robot interaction behavior is not modeled inthis study. We consider that, for safe navigation, developing such a model is not mandatory, since people who intentionally approach the robot are obviously aware of its presence, and thus we may expectthat they do not behave dangerously from a navigation point of view. Nevertheless the developmentof a model of these people’s behavior will be indispensable when actually deploying a robot to provide
services in a pedestrian environment.
3. Evaluation. The current paper bases its evaluation
criteria entirely on subjective metrics (questionnaires,coders). Other works ([42,43]) use more quantitative criteria to evaluate the easiness of walking ofpedestrians, criteria that could be introduced in theevaluation of a “socially acceptable” pedestrian system as the one proposed in this work.

5.3 Limitations
Finally, we may discuss some limitations of our approach, whose solution may also be the subject of future
research work
1. The learning set. Even if we assume that Eq. (3)correctly describes human-like behavior, we need agood learning set of pedestrian trajectories aroundthe robot in order to use the method of [13], or anequivalent learning algorithm, to extract the correct interaction force function (i.e., parameters Ar ,Br ). The trajectories of our controlled experiments
in section 3.3 are far from fulﬁlling the deﬁnitionof a large enough learning set (the robot was interacting with a single pedestrian, the pedestrian wasalways coming from an head-on direction, and therobot was moving straight, i.e. it was not interacting). Designing multi-person setting experiments as
those used for inter-pedestrian model calibration in[13] is not trivial, due to the robot motion limitation
with respect to the pedestrians, and to the problem
of deﬁning the robot’s avoidance behavior during
the experiments (since in principle such a behavior has to be determined by the same experiments).
One solution could be to deploy the robot using thecollision avoidance parameters obtained in this workin a shopping mall, and use the trajectories of thepedestrians around the robot as a learning set, even
if this approach implies the use of a very high quality tracker in a real world setting.
2. Calibration to diﬀerent robots. The parameters forthe robot collision avoiding system that we obtainedin this work were determined through experiments
involving a particular robot model interacting withJapanese people. Since evidence suggests that people maintain diﬀerent distances depending on the
robot’s appearance [44] and cultural factors [36], themethod might need to be re-calibrated before being
applied to diﬀerent robots and cultures. For example, in our experiments of section 3.3, pedestrians
avoided the robot in a way similar to the one withwhich they avoid other pedestrians; such a behavior
is surely inﬂuenced by the fact that our robot’s size
is similar to the human size, and probably also by
the robot’s reduced velocity.
3. Environmental sensors. For this work we relied on
environmental sensors for tracking the pedestrians
around the robot. We believe that our system canbe used also with only on-board sensors, but it has
to be expected that the capability of tracking surrounding pedestrians would be reduced. It is noteasy, using just theoretical arguments, to predict therange of usage, in terms of pedestrian densities, ofThe final publication is available at
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11

the on-board system, and how it compares to the environmental sensors one, but it has to be expectedthat the former one will be more limited. Such aquestion has to be assessed in order to develop acompletely autonomous navigation system.
6 Conclusion
This paper reports a system for safe and comfortable
collision avoidance toward people by a mobile robot using a pedestrian model. We used a particular speciﬁcation of the Social Force model, CP-SFM, which has
been explicitly developed for such low-density settings
as those normally found in a shopping mall corridor,
to reproduce human-like collision-avoidance behavior inrobots. We ﬁrst tested the developed robot in a singleperson setting to conﬁrm that it provides a comfortable feeling to pedestrians. The results suggest that a
robot using the proposed method is signiﬁcantly moresocially acceptable than one using an alternative traditional method. Second, we conducted a ﬁeld experimentin a shopping mall corridor to investigate whether the
robot could navigate safely among pedestrians. The results revealed that our method enables safer navigation
without causing any possibly unsafe abrupt movementsin the surrounding pedestrians during a two-hour trial.
A Background work: Collision Prediction
Social Force Model

Fig. 11 Collision prediction among pedestrians with CP-SFM.
d′ij is the distance between pedestrians at the time of maximum
approach ti .

changes with d. The model is popular for its simplicity, and it
works well at the high densities that describe the egress conditions it has been developed for [46], but it fails in describing lower
density regimes and for this reason a few improved speciﬁcations,taking in account relative velocities in the computation of fi,j ,have been proposed [47].
Zanlungo et al. [13] compare a few of these speciﬁcations to the
Collision Prediction (CP) speciﬁcation that they propose. Thismodel, that develops on ideas originating from Reynold’s boid
model [48], uses the relative velocity between the pedestrians tocompute how their “future” distance d′i,j will vary with time
according to the hypothesis that the pedestrians will keep a constant velocity. The time at which the projected distance d′i,j assumes a minimum value is called the “interaction time” ti forpedestrian i and the value of the corresponding future distanced′i,j (ti ) (see ﬁgure 11) replaces the current distance in the equation for the CS speciﬁcation force (2) in order to obtain the CP
speciﬁcation equation′vi −d′ i,j /B di,j (ti )e.
′tidi,j (ti )A.1 Model deﬁnitionfi,j (di,j , vi,j , vi ) = A

Models of pedestrian collision-avoidance have been developed since
the 50s to deepen understanding of crowd dynamics and design
better facilities. The Social Force Model (SFM) [45] is a popularpedestrian model that describes the behavior of pedestrians in a
crowd through reaction forces inspired by physics. More than asingle model, SFM may be considered as a framework in which
the acceleration of a pedestrian i is given by

Here the term vi /ti is introduced to modulate the force in sucha way that the pedestrian is able to stop in time ti . According to
the analysis of [13], the CP-SFM model outperforms the previous
SFM speciﬁcations in simulating pedestrian collision avoidance in
low and average density multi-person settings, a characteristics
that makes this model suitable to robot applications.
[49] compares the performance of CP to other popular pedestrian
methods for egress oriented applications.

v 0 − vi (t) ∑dvi (t)fi,j (t).
= i
+
dt
τ

(3)

(1)

j̸=i

Here vi (t) is the pedestrian velocity at time t, vi0 is the pedestrian’s preferred velocity, a vector directed towards the current
pedestrian sub-goal and whose magnitude corresponds to the velocity the pedestrian is more comfortable walking at, while τ isthe relaxation time to recover the preferred velocity (0.66 s−1
in [13]). The actual avoidance behavior is determined by the interaction term with the other pedestrians j in the environment,fi,j , whose precise form determines the SFM specification. The
original Circular Speciﬁcation (CS) of the model was determinedby symmetrical repulsive forces asfi,j (t) = Ae−di,j (t)/Bdi,j (t),di,j (t)(2)

where di,j is the distance between the pedestrians, A is the maximum interaction intensity and B determines how the intensity

A.2 Model calibration
To calibrate and evaluate the CP-SFM model, [13] uses a setof pedestrian trajectories obtained in a controlled experiment towhich 8 subjects took part. Each subject was given a start andgoal point, and was prescribed to walk as naturally as possible
towards the goal. The trajectories of pedestrians were tracked ina square area with an 8 meters side. The start and goal points
were decided in such a way that the trajectories of all pedestrians will converge, if walking straight to the goal, at the centerof the experimental area, creating a potentially complex collision avoiding problem; but the density of the environment was
low enough to allow the pedestrians to freely choose their avoidance strategy. The calibration process used a genetic algorithm tominimize a ﬁtness function that consisted in the average distanceThe final publication is available at
http://link.springer.com/article/10.1007/s12369-014-0238-y12
between the simulated and actual trajectories of pedestrians plusa penalty term assigned to those trajectories that “collided” between them (more exactly, trajectories that reached a minimum
distance smaller than the distance between any pair of actual
pedestrians during the experiment). The genetic algorithm used
500 genomes per generation, over 1000 diﬀerent generations; tournament selection over a pool of 5 solutions, crossover and random
Gaussian mutation with probability 0.1. The solution was determined through 50 independent runs of the algorithm. The CPSFM method outperformed all the other speciﬁcations with anaverage position error of 30 ± 1 centimeters (55 ± 1 for CS-SFM).
Acknowledgements We thank the staﬀ of the Asia and Paciﬁc
Trade Center for their cooperation and ATR’s Yoshifumi Nakagawa for his help. We also thank the anonymous reviewers whose
comments helped us to improve the quality of our paper. This
work was supported by JST, CREST.

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Masahiro Shiomi received his M. Eng, and Ph. D. degrees
in engineering from Osaka University, Osaka, Japan, in 2004,
and 2007, respectively. From 2004 to 2007, he was an Intern Researcher at the Intelligent Robotics and Communication Laboratories (IRC) and is currently a Researcher at IRC at the Advanced
Telecommunications Research Institute International (ATR) in
Kyoto, Japan. He is currently a Researcher and a Group Leader ofAgent Interaction Design Department at ATR IRC. His researchinterests include childcare robotics, human-robot interaction, andnetworked robots.
Francesco Zanlungo received his Laurea in Fisica (Italian
degree in Physics) from the University of Milan in 2002, and his
Ph. D. in Physics from the University of Bologna, Italy, in 2007.
He was a post-doc researcher at the Department of Physics of
the University of Bologna from 2007 to 2009, and he is a Researcher at ATR IRC in Kyoto, Japan, since 2009. His researchinterests include many aspects of complex systems studies, fromchaotic maps to the dynamics of artiﬁcial life system, and focus
in particular on pedestrian behavior.
Kotaro Hayashi received an M. Eng and Ph. D. degrees inengineering from the Nara Institute of Science and Technology,
Nara, Japan in 2007 and 2012. Since 2009 to 2012, he was an
Intern Researcher at the Intelligent Robotics and Communication
Laboratories (IRC) and is currently a Researcher at IRC at theAdvanced Telecommunications Research Institute International(ATR) in Kyoto, Japan. His current research interests humanrobot interaction, interactive humanoid robots, Human factors
and ﬁeld trials.
Takayuki Kanda received his B. Eng, M. Eng, and Ph.
D. degrees in computer science from Kyoto University, Kyoto,
Japan, in 1998, 2000, and 2003, respectively. From 2000 to 2003,
he was an Intern Researcher at ATR Media Information Science
Laboratories, and he is currently a Senior Researcher at ATRIntelligent Robotics and Communication Laboratories, Kyoto,
Japan. His current research interests include intelligent robotics,
human-robot interaction, and vision-based mobile robots. Dr.
Kanda is a member of IEEE, the ACM, the Robotics Societyof Japan, the Information Processing Society of Japan, and theJapanese Society for Artiﬁcial Intelligence.