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Behavioural Brain Research 275 (2014) 269–280

Contents lists available at ScienceDirect

Behavioural Brain Research

jou rn al hom epage: www.elsev ier .com/ locate /bbr

esearch report

nfluence of robotic shoal size, configuration, and activity on zebrafishehavior in a free-swimming environment

achit Butail a,b, Giovanni Polverinob,c, Paul Phamduyb, Fausto Del Setteb,aurizio Porfirib,∗

Indraprastha Institute of Information Technology Delhi (IIITD), New Delhi, IndiaDepartment of Mechanical and Aerospace Engineering, New York University Polytechnic School of Engineering, Brooklyn, NY, USADepartment of Biology and Ecology of Fishes, Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany

i g h l i g h t s

We study zebrafish response to free-swimming biologically inspired robotic fish.We develop an online control system to autonomously maneuver multiple robotic fish.Fish behavior is assessed as we change the speed, number, and orientation of robots.Increase in the number of robots significantly affects the response of zebrafish.Orientation of robots differentially modulates the time spent near them by zebrafish.

r t i c l e i n f o

rticle history:eceived 2 July 2014eceived in revised form 4 September 2014ccepted 8 September 2014vailable online 18 September 2014

eywords:nimal–robot interactionebrafishehaviorobot

a b s t r a c t

In animal studies, robots have been recently used as a valid tool for testing a wide spectrum of hypothe-ses. These robots often exploit visual or auditory cues to modulate animal behavior. The propensity ofzebrafish, a model organism in biological studies, toward fish with similar color patterns and shape hasbeen leveraged to design biologically inspired robots that successfully attract zebrafish in preferencetests. With an aim of extending the application of such robots to field studies, here, we investigate theresponse of zebrafish to multiple robotic fish swimming at different speeds and in varying arrangements.A soft real-time multi-target tracking and control system remotely steers the robots in circular trajec-tories during the experimental trials. Our findings indicate a complex behavioral response of zebrafishto biologically inspired robots. More robots produce a significant change in salient measures of stress,with a fast robot swimming alone causing more freezing and erratic activity than two robots swimming

slowly together. In addition, fish spend more time in the proximity of a robot when they swim far apartthan when the robots swim close to each other. Increase in the number of robots also significantly altersthe degree of alignment of fish motion with a robot. Results from this study are expected to advance ourunderstanding of robot perception by live animals and aid in hypothesis-driven studies in unconstrainedfree-swimming environments.

© 2014 Elsevier B.V. All rights reserved.

. Introduction

A key aspect of social behavior is the perception of size andxtent of groups by individuals. For a social animal, the attractive-ess of a group can be based on several perceived advantages such

∗ Corresponding author. Tel.: +1 718 260 3681; fax: +1 718 260 3532.E-mail address: mporfiri@nyu.edu (M. Porfiri).

ttp://dx.doi.org/10.1016/j.bbr.2014.09.015166-4328/© 2014 Elsevier B.V. All rights reserved.

as safety from predators [1–4], foraging benefits [5–8], and terri-torial defense [9–11]. Previous research on fish social behavior hasexplored the role of size and activity [12–14], nutritional state [8],size of individuals [15–17], predation risk [1], and species compo-sition [18] on the foraging efficiency and shoal choice of individual

fish. Overall, the results of these studies indicate that fish com-plete a foraging task faster when in smaller groups [12], preferlarger and more active shoals [1,13], and are more likely to occupyfront positions in a shoal given a large body size [16]. In addition,

2 rain Research 275 (2014) 269–280

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Fig. 1. An underwater view of the robotic fish in the experimental tank. While theoutward appearance (color pattern, aspect ratio and shape of caudal fin) was kept

70 S. Butail et al. / Behavioural B

ndings from these studies suggest that fish can even shoal withnimated images of conspecifics [18] and that food-deprived fishrefer well-fed shoals over food-deprived ones [8].

The recent use of robots has allowed for the evaluation of aultitude of untested hypotheses in social fish behavior [19–26].

or example, in [19], the number of robotic replicas of fish wereradually increased to show that individuals respond only when

minimum number of conspecifics perform a particular behav-or; in [20], a similar replica was used to demonstrate leadershipnd recruitment in groups of fish. In [23], a tail-beating robotic fishas used to show that body movement may constitute an attrac-

ive stimulus. In [24], a similar robot was utilized in a water tunnelo test whether biomimetic locomotion creates a wake where fishan gain a hydrodynamic advantage, and in [26] the effect of tail-eat frequency and color on fish preference was studied. Finally,he role of varying color morphs on courtship was investigatedn [25] through a miniature platform, which allowed for accurate

aneuvering of robotic replicas.As animal behavioral studies gain from recent advancement in

obotics [21,22,27], the mechanical design of robotic devices andhe synthesis of control strategies for their operation continue toenefit by taking inspiration from animals [28,29]. With fish as an

nspiration, underwater propulsion systems based on undulatingmart materials have been designed [30–33], and sensors inspiredy the lateral line structure and placement have been realized forow sensing [34,35]. Similarly, several control and navigation algo-ithms that are motivated from and replicate animal behavior haveeen proposed in the last few years [36–39].

Recently, a biologically inspired robotic fish with onboard elec-ronics was designed in [40,41] for possible deployment in fieldtudies. Through the incorporation of visual cues, such as colorattern, aspect ratio, and tail undulation, it was demonstratedhat such a robot is effective in attracting zebrafish in preferenceests [23,42–44]. Zebrafish, due to its strong preference to stay inroups [45] and having vision as a primary sensory modality, con-titutes an important animal model [46,47] in such studies. Theobustness of zebrafish preference to biologically inspired robotsas further utilized in pharmacological studies to standardize a

obotics-based experimental paradigm for investigating the effectf psychoactive compounds on animal behavior [48]. While theseobots are not perceived as conspecifics [23], a recent study byur group demonstrated that they are able to modulate zebrafishollective behavior in a free-swimming environment [49]. Theobots, though larger in size than live zebrafish, can be utilizedor testing further more complex hypotheses, while sustaining theromise of field deployment for animal control and protection43,50].

The goal of this paper is twofold: first, to advance the labo-atory application of robotic fish for studies in a free-swimmingnvironment, where robots can maneuver autonomously; and sec-nd, to investigate zebrafish perception of size and structure of aroup of biologically inspired robotic fish. Specifically, we controlhe robotic fish to study the effect of the number of robots (onend two) and their configuration (two robots swimming close andar) on a single zebrafish. While we do not expect the fish to shoalith the robots as if they were conspecifics, we hypothesize that

change in the number and configuration of the robots will mod-late fish behavior (hypothesis H1). In particular, with respect tohe robotic shoal size, we expect that an increase in the numberill produce a stronger preference toward the robots (hypothe-

is H1a). With respect to the change in robotic shoal structure, wenticipate that fish will interact differently with robots that are

ar from each other than with robots that swim close togetherhypothesis H1b). The nature of this interaction is quantified inerms of behavioral measures, including distance to the robots,ime spent near them, and alignment with their motion. Finally,

the same as in [49], upgrades were made to the onboard electronics and shell designto improve the robots’ reliability. (For interpretation of the references to color in thisfigure legend, the reader is referred to the web version of the article.)

we hypothesize that the speed of the robots will also affect fishbehavior, whereby fish will spend more time near robots that areswimming faster (hypothesis H2), and thus perceived to be moreactive.

2. Materials and methods

2.1. Ethics statement

The experiments performed as part of this study were conductedfollowing the protocols AWOC-2012-101 and AWOC-2013-103approved by the Animal Welfare Oversight Committee of the Poly-technic Institute of New York University (now New York UniversityPolytechnic School of Engineering).

2.2. Animals and housing

We used wild-type zebrafish (Danio rerio), 3–4 cm in body length(6–8 months old) in our experiments. The fish were acquired froma genetically heterogeneous population in three separate batches,in December 2012, February 2013, and June 2013, from an onlineaquarium source (LiveAquaria.com, Rhinelander, WI, USA), andacclimatized in the laboratory for a two week period prior tothe start of the experiments. The fish were maintained in 37.8 l(10 gallon) tanks with no more than 20 fish per tank. Water qualityin the holding tanks was monitored regularly and the temperatureand pH maintained at 27 ± 1 ◦C and 7.2, respectively. The holdingtanks were kept under a 12 h light and 12 h dark photoperiod [51].Fish were fed with commercial flake food (Hagen Corp./ Nutrafinmax, Mansfield, MA, USA) at approximately 7 pm every day. Exper-iments for this study were conducted between 12 and 7 pm witheach batch of fish tested for two weeks.

2.3. Robotic fish

Fig. 1 shows sample robotic fish used in our experiments. Fol-lowing the design in [40], the robotic fish consisted of a bodyand a tail section made of rigid acrylonitrile butadiene styreneplastic, and a flexible caudal fin attached to the tail for propul-sion. Color pattern, aspect ratio, and tail-beat frequency have beenshown to be determinant factors in the attractiveness of the roboticfish to zebrafish [42,52]. To match zebrafish coloration, the robotswere painted bright blue with silver stripes and a yellow head.Robot size (15.8 cm × 4.8 cm × 2.6 cm, length × height × width) wasdesigned to match the aspect ratio of a fertile zebrafish female.The undulation of the flexible caudal fin mimicked carangi-form/subcarangiform patterns characteristic of zebrafish [18]. The

robots were positively buoyant and swam just under the surface ofthe water.

The body section contained a single-cell lithium-polymer bat-tery, an Arduino Pro mini microcontroller (Sparkfun electronics,

rain Research 275 (2014) 269–280 271

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Fig. 2. The angle between direction of motion and the next waypoint is used todetermine heading control.

S. Butail et al. / Behavioural B

oulder, CO, USA), and an nRF2401A transceiver chip (Nordic Semi-onductor, Oslo, Norway). The body-tail joint was actuated using

Hitec HS-55 servomotor (Hitec RCD 155 USA Inc., Poway, CA,SA) inserted into the tail section for propelling the robot. Inrder to improve long-term underwater usage and radio commu-ication further modifications on the design in [40] were madeithout changing the robot’s outward appearance. In particular,

he main body section was partitioned laterally with holes drilledn the lower shell for counterweights; a surface mount regulator

as used on the onboard circuit (instead of an external regula-or) for easier access to the electronics board; the antenna’s wireas exposed through a waterproof hole in the upper shell; and

he closing lip was grooved for mounting a rubber silicone o-ing.

The robots were controlled remotely with a control unit con-isting of an Arduino Duemilanove microcontroller (Sparkfunlectronics, Boulder, CO, USA) and an nRF2401A transceiver chip.ontrol parameters, namely tail-beat frequency, tail-beat ampli-ude, and tail-section offset were transmitted via a universal serialus (USB) from a desktop computer to the microcontroller and thenransmitted wirelessly to the robot every 3/5th of a second. Theobot was steered by varying the tail-section offset from a trimalue of zero degrees corresponding to the robot body in line withhe tail section.

.4. Multi-target tracking and control

The multi-target tracking and control system was developed inATLAB (R2011a, Mathworks, Natick, MA, USA) and used a Kalman

lter [53] to estimate two-dimensional position and velocity ofach target (robot or fish) in the tank and global nearest-neighborssignment algorithm to assign measurements to target tracks54,55]. The tracking and control algorithm for a single robot isetailed in [49]. For completeness, we describe the same here inrief with the additional collision avoidance step.

Estimation. We used a Kalman filter to estimate the positionnd velocity of robots and the live fish [49]. Briefly, the instanta-eous state of a target i at a frame k was given by Xi[k] = [ri vi Ai 1]T.ere, ri ∈ R

2 is the two-dimensional position, vi ∈ R2 is the two-

imensional velocity, and Ai is the area in pixels. The state vectoras augmented with 1 to ensure that the mapping from pixel to

m (measurement model) is linear. The measurement of the tar-et i, Zi[k] ∈ R

3 consisted of two-dimensional pixel measurementf the center of the target and the area in pixels. Measurementsere obtained by subtracting a background image from each frame

ollowed by connected-component analysis (regionprops routine inATLAB) to isolate the foreground as pixel blobs. Thresholds for

ntensity and blob area in pixels were computed online to ensurehat no more than the expected number of targets was detectedn overhead video. (Note that this threshold computation step iserformed during habituation phase.) The area measurement wassed to differentiate the robots from the fish based on the numberf pixels.

Automatic waypoint tracking. At the start of a trial, each robotommunicated with the computer through a separate channel. Thishannel-robot pair was maintained throughout the experiment.

e assumed that the robot was always oriented in the direction ofotion. Denoting robot estimates with subscript R and superscript

where j ∈ {1, 2} each robot position r1,2R [k] and velocity v1,2

R [k]

ere used to calculate the steering control input u1,2o [k], computed

eparately for each robot. The control signal was transmitted every

/5th of a second. The robots followed clockwise a set of sixteenaypoints ws, s = 1, . . ., 16, spaced equally on a circle centered in the

ank. The waypoints for each robot were set a fixed angle �0 apartn the circular trajectory on the basis of the configuration (�0 = 30◦

for close and �0 = 180◦ for far). At each frame k, the desired directionof movement of the robot j, with j = 1, 2, vj

R, was computed as

vjR[k] = wj

s[k] − rjR[k]

‖ wjs[k] − rj

R[k] ‖, (1)

where wjs[k] is the current waypoint-to-reach for robot j. The

value of waypoint-to-reach was updated at frame k′ when therobot was within a threshold distance (15 cm) of the currentwaypoint-to-reach. The control input uj

o, corresponding to thetail-beating offset, was a function of the error ej = sin(�j), where�j[k] = arg(vj

R[k] − vjR[k]) is the angle between the robot direction

of motion and the desired direction of motion (Fig. 2). Duringnormal operation, the waypoints were sufficiently close to eachother so that the error was well approximated by the angle itself.At the same time, in the rare event that the robot got entrapped inthe corners of the tank, the use of a sine function aided in reducingthe control authority at large errors.

The control input ujo was computed in a proportional-integral-

derivative PID loop [56] as

ujo[k] = Kj

pej[k] + Kji

k∑l=k′

ej[l]�t + Kjd

�ej[k]�t

, (2)

where Kjp, Kj

i, and Kj

dare the proportional, integral, and derivative

control gains of robot j; �t is the length of a timestep (0.2 s); and�ej[k] = ej[k] − ej[k − 1]. The control gains were tuned to obtain theminimum error in test trials.

Maintaining configuration/collision avoidance. A constant phasewas maintained between the two robots by implementing of aswitching control strategy on the tail-beat amplitude to speed up orslow down a robot. Variation in tail-beat amplitude within a fixedrange leads to proportional change in thrust [40,41] and thereforerobot speed. Consequently, the switching control lowered the tail-beat amplitude of the leading (or trailing) robot by 5◦ if the phase

between the robots increased (or decreased) above a set threshold(�0 ± 5◦). Specifically, denoting the phase between the two robots

272 S. Butail et al. / Behavioural Brain Research 275 (2014) 269–280

−60 0 60−60

0

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(cm

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0 1 2 3 4 50

20

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60

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100

120

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although the tail of the robot was not moving. For conditions witha single robot, the radius of the circular trajectory was set at 38 cm;for two robots, the radii were set at 36 and 40 cm.

ig. 3. Robot trajectories (red and green) and distance between them in close (a and

f the references to color in this figure legend, the reader is referred to the web ver

t k as �[k], and the nominal amplitude a0 = 20◦, we imposed thewitching amplitude control u1,2

a [k] separately for each robot as

u1a[k] =

{a0 − 5 if (�[k] − �0) > 5◦

a0 otherwise,

u2a[k] =

{a0 − 5 if (�[k] − �0) < −5◦

a0 otherwise,

(3)

here we arbitrarily set 1 for the leading robot. The control signalonsisting of tail-beat amplitude and offset was transmitted to theobots at all times, while the tail-beat frequency was set constantt the beginning of the trial. Fig. 3 shows the robot trajectories inlose and far configurations with the distance between them as aunction of time.

.5. Experimental setup

The subjects were filmed with an overhead web cameraMicrosoft LifeCam, Microsoft, Redmond, WA, USA) mounted50 cm above the water surface in an experimental tank120 cm × 120 cm × 20 cm, length × width × height). To obtain

high-contrast background for ease in tracking, the tank surfaceas covered with a non-reflective white contact paper. The waterepth was maintained at 10 cm during the experiments. Theeb camera recorded frames at a rate of five frames per second

nd a resolution of 640 × 480 pixels through a USB interface.The relatively low sampling rate and resolution allowed for softeal-time processing on MATLAB.) Four 25 W fluorescent tubesAll-Glass Aquarium, Franklin, WI, USA) were used to illuminatehe observation region with diffused overhead light (Fig. 4). Theoft real-time multi-target tracking and control algorithm was runn a Pentium dual core desktop computer (2.5 GHz processor and

GB memory) running MATLAB image acquisition toolbox on anbuntu 10.04 Linux operating system. Dark curtains were used to

solate the setup during experiments.

.6. Experimental procedure

The experiments were designed to explore the effect of robotichoal size, configuration, and activity on a single zebrafish. In

far (c and d) configurations. The fish trajectory is shown in black. (For interpretationf the article.)

particular, we considered six experimental conditions to inves-tigate the effect of shoal size (single vs. two robots) × activity(speeds) and six experimental conditions to investigate the effectof robotic shoal configuration (close vs. far configuration) × activity(speeds). For brevity, the two robot conditions are noted with “far”and “close” in parentheses wherever needed. With three conditionscommon between the two analyses, nine experimental conditionswere performed in total (Fig. 5). These conditions comprised asingle robot swimming by itself, two robots swimming close toeach other, and two robots swimming diametrically opposite toeach other, all in circular trajectories of fixed size and at threedifferent speeds from a tail-beat frequency of 1 Hz, 2 Hz, and3 Hz. These tail-beat frequencies corresponded to robot speedsof approximately 2 cm s−1, 3 cm s−1, and 4 cm s−1, respectively,and were selected to explore variations in the reference conditioncorresponding to 2 Hz, which was considered in the preferencetests described in [23,42,44,48]. Two more conditions to controlfor the effect of robot presence (NoRobot) and robot movement(0 Hz) were also tested. In the 0 Hz condition, the robot was placedat equidistant points on a circle. To compensate for the effectof noise, the servo motor was kept on during the 0 Hz condition

Fig. 4. Schematic of the experimental setup similar to the one used in [49]. Theexperimental apparatus consisted of a square shallow tank with overhead ultravioletlighting and camera for soft real-time tracking.

S. Butail et al. / Behavioural Brain Research 275 (2014) 269–280 273

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ig. 5. The experimental conditions consisted of a single robot, two robots close

ifferent speeds. The experimental design permits two two-way comparisons to cohen speed and configuration of the robots.

Ten trials were performed for each condition. In each trial, aingle experimentally naive fish was transferred from the hold-ng tank to the experimental tank with a hand net. The trackingnd control system began by computing an average backgroundmage and an intensity threshold to ensure that the single fish wasetected consistently. Robot communication and tail-beat move-ent were tested next by beating the robot tail at predetermined

ffset angles of ±30◦. In the case of multiple robots, the commu-ication channel was identified and the experimenter placed theobot at a predetermined position in the tank (opposite corners forar, and adjacent sides for close). After placing the robots, the exper-menter left the room and a habituation time of ten minutes wasiven to avoid novelty effects [23,57–59]. Depending on the con-guration, the robots followed waypoints on the circular trajectoryxed angle apart (30◦ for close, and 180◦ for far). In addition toisual habituation, the servomotor inside the robot was turned onuring this time to create a uniform acoustic background. The startignal was communicated to the robots at the end of 10 min. At thend of the 5 min experimental time, the fish was removed from theank.

.7. Data analysis

Trajectory data was verified and repaired if needed for each indi-idual trial using a custom MATLAB Graphical User Interface (GUI).he GUI allowed a user to view the videos with the trajectoriesverlaid on top of the fish. A script advanced the frames until arack was terminated, at which point the user was requested to

ark the correct measurement. Although care was taken to havehe robots swim in the predefined circular trajectories (and pos-tions in the case of 0 Hz condition), signal attenuation in wateraused the robots in some trials to collide with each other or withhe tank walls; such trials were not used in analysis. Consequently,

= 7 for 2 Hz, 2 Hz(far), 3 Hz, and 3 Hz(close), n = 10 for NoRobot, = 9 for 3 Hz(far), and n = 8 for the remaining conditions.

Individual fish behavior was quantified in terms of speed andotal distance traveled as indicators of activity [60]; absolute turnate to score erratic activity [61,62]; and time spent in the cor-ers of the tank to quantify avoidance. In addition, we record theime spent freezing [62–64] as a complementary measure of stress-elated behavior along with erratic activity and avoidance. Totalistance traveled in meters was computed as the sum of difference

n successive positions in each frame. Freezing was measured ashe percentage of the total experimental time during which the fishpent at least two continuous seconds within a circle of 2 cm radius

44,49]. Absolute turn rate, measured in deg s−1, was defined as theate of change in the direction of motion [62] and was computeds the angle ranging from 0 to � between the fish velocity vectorn two successive frames divided by the acquisition time. The time

h other, and two robots swimming diametrically opposite to each other at threee the response of a live zebrafish as a function of speed and number of robots, and

spent in the tank corners was scored as the percentage of the totalexperimental time in which the fish was positioned at a distance ofmore than 70 cm from the center of the tank (Supplementary data).

Fish interaction with the robot was measured in terms of thetime spent near the robots (15 cm from a robot center), minimumdistance to a robot, and alignment with a robot. Alignment with arobot was defined as the cosine of the angle between the fish andthe robot directions of motion; a value of −1 would be recordedif the robot and the fish were moving in opposite directions and 1implied that they were moving in the same direction. For more thanone robot in the tank, the maximum of this value was consideredin the analysis. Given that the robots were mobile and periodicallycovering the experimental region, the effect of distance to a roboton probability of freezing was computed by calculating the fre-quency of freezing as a function of distance from a robot when onlya single mobile robot was present (conditions 1 Hz, 2 Hz, and 3 Hz).Two-way ANOVAs, first with speed × number of robots, and secondwith speed × robot configuration as the independent variables, andbehavioral parameters as the dependent variable were performed.

Multiple comparisons were performed using Fisher’s protectedleast significant difference (PLSD) across all conditions if a signif-icant interaction effect was found; if a main effect was found dueto number of robots or robot configuration, one-way ANOVA wasperformed for each factor (one and two robots, or, near and farconfiguration) with speed as the independent variable followed bymultiple comparisons using Fisher’s PLSD. To check for the pos-sibility of time-effect on fish interaction, mixed two-way ANOVAswith time as the within-subjects factor and robot speed as betweensubjects factor was performed. Post-hoc comparison with controlconditions was performed using two-tailed t-test with Bonferronicorrection. Statistical significance was set at p < 0.05 for all tests. Allstatistics were computed in MATLAB.

3. Results

Fig. 6 illustrates the spatial distribution of fish in the square tankfor each condition. The two dimensional histograms show that thefish spent up to 25 percent of the experimental times in the tankperiphery particularly in the corners for conditions 1 Hz, 2 Hz, 2 Hz(close) and 3 Hz. Speed distributions indicate that the fish swamless than 8 cm s−1 for majority of the time except for No Robot and3 Hz (far) conditions.

In the speed × number of robots two-way ANOVA comparison,the number of robots significantly affected individual fish behavior(Table 1). In particular, freezing (Fig. 7), absolute turn rate (Fig. 9),

and time spent in the tank corners (Supplementary data) were alldifferentially modulated by the number of robots, with lower val-ues attained with two robots. Although all trials were included inthe statistical comparison, three trials in the 0 Hz condition and one

274 S. Butail et al. / Behavioural Brain Research 275 (2014) 269–280

Fig. 6. Two dimensional histograms of fish position during the experimental time for all trials in each condition (top three rows). Probability distribution of speed for thesame data (bottom three rows).

S. Butail et al. / Behavioural Brain Research 275 (2014) 269–280 275

Table 1Results from two-way ANOVA comparisons evaluating hypothesis H1a for speed × number of robots (size). p- and F-values for each effect and their interaction (int.) areshown. Significant effects are shown in bold.

Parameter F1,39 (size) p (size) F2,39 (speed) p (speed) F2,39 (int.) p (int.)

Distance to a robot (cm) 3.87 0.06 1.83 0.17 0.26 0.77Time near the robots 0.00 0.95 2.31 0.11 0.29 0.75Alignment with a robot 13.24 0.00 1.55 0.22 0.35 0.71Speed (cm s−1) 2.43 0.13 0.64 0.53 1.11 0.34Total distance traveled (m) 2.35 0.13 1.14 0.33 1.42 0.25Freezing 5.76 0.02 1.10 0.34 0.33 0.72

tit((tc(auot

w

F

Absolute turn rate (deg s−1) 9.85 0.00Time spent in the tank corners 7.99 0.01

rial in the 3 Hz condition had unexpectedly high levels of freez-ng of more than 85%. When a single moving robot was present,he probability of freezing increased with the distance to a robotFig. 8). Among measures of fish interaction, alignment with a robotFig. 10) was significantly altered by the number of robots. Whilehe distance to a robot (Supplementary data) bordered on signifi-ance with the number of robots, fish speed, total distance traveledSupplementary data) and time near the robots (Fig. 11) were notffected by the number of robots. None of the stress-related meas-res were influenced by the robot speed. None of the subsequentne-way ANOVAs revealed a significant effect of speed for one or

wo robots within each condition.

Between speed × configuration of robots, robot configurationas found to significantly affect the measures of fish interaction,

ig. 7. Freezing was found to be significantly affected by the number of robots in the tan

0.56 0.57 0.51 0.612.42 0.10 1.06 0.36

namely distance to a robot, time near the robots and alignmentwith a robot (Table 2). Subsequent one-way ANOVA comparisonswithin each configuration showed that only time spent near robotswas significantly different when the robots were swimming close toeach other (p = 0.004, F2,20 = 7.170). Post-hoc comparisons showedthat time near the robots when they were swimming close withtail-beat frequency 3 Hz was significantly different from when therobots were swimming close with tail-beat frequency 1 Hz and2 Hz. Neither the configuration of the robots, nor their speed werefound to significantly affect the speed of the fish, absolute turn rate,and time spent in the corners. When more than one robot was

present in the tank, the time near each robot was similar (close:p = 0.15; far: p = 0.61), that is, no preference was found for a par-ticular robot during the trial. Among measures of fish individual

k (top) but not their configuration (bottom) or speed. Error bars denote ±SEM.

276 S. Butail et al. / Behavioural Brain Research 275 (2014) 269–280

Table 2Results from two-way ANOVA comparisons evaluating hypothesis H1b for speed × configuration of the robotic shoal (config.). p- and F-values for each effect and theirinteraction (int.) are shown. Significant effects are shown in bold.

Parameter F1,39 (config.) p (config.) F2,39 (speed) p (speed) F2,39 (int.) p (int.)

Distance to a robot (cm) 77.24 0.00 1.74 0.19 1.35 0.27Time near the robots 6.97 0.01 2.19 0.12 0.38 0.68Alignment with a robot 302.39 0.00 1.40 0.26 0.07 0.93Speed (cm s−1) 1.04 0.31 2.61 0.09 1.05 0.36Total distance traveled (m) 1.50 0.23 2.51 0.09 1.69 0.20Freezing 0.05 0.83

Absolute turn rate (deg s−1) 0.04 0.84Time spent in the tank corners 1.36 0.25

0 20 40 60 80 100 1200

0.1

0.2

0.3

0.4

Distance to a robot (cm)

Pro

babi

lity

of fr

eezi

ng

Fig. 8. Probability of freezing as a function of distance to a robot for trails whereonly a single moving robot was present.

Fig. 9. Absolute turn rate, a measure of erratic activity, was found to be significantly affeor speed. Error bars denote ±SEM.

0.08 0.93 0.06 0.940.80 0.46 0.36 0.700.34 0.72 5.06 0.01

behavior, time spent in the tank corners was found to have a sig-nificant interaction effect between robot configuration and speed.Post-hoc comparisons showed that the time spent in tank cornersby the fish when the robots were swimming close to each other at2 Hz was significantly more than when they were swimming far at1 and 2 Hz or close at 3 Hz tail beat frequencies.

No time effect was found on the fish interaction with the robotduring the 5 min experimental time. In particular, mixed ANOVAresults show that the time near the robots (p = 0.15) and alignmentwith a robot (p = 0.72) did not change significantly with time forall the experimental conditions. One-way comparison of fish speedacross these conditions indicate that individual robot speed did nothave a significant effect on fish speed (p = 0.71).

Finally, post-hoc comparisons (paired t-test with Bonferronicorrection) with the control conditions No Robot and 0 Hz did notreveal a significant effect on any of the behavioral measures.

cted by the number of robots in the tank (top) but not their configuration (bottom)

S. Butail et al. / Behavioural Brain Research 275 (2014) 269–280 277

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ig. 10. Alignment of fish and robot direction of motion with the robot was found tout not by their speed. Error bars denote ±SEM.

. Discussion

We used biologically inspired robots to study the effect of groupize, configuration, and speed of a robotic shoal on the behav-oral response of single zebrafish. Zebrafish position and velocity

ere estimated as the robots swam in predefined circular trajecto-ies with varying speeds while maintaining their relative distance.verhead images of the experimental tank were used to tracknd control the motion of the robots in soft real-time. Analysis ofrajectory data shows that the number of robots as well as theironfiguration modulate fish response.

Our results indicate a complex behavioral response of zebrafisho biologically inspired robotic fish in a free-swimming environ-

ent. The modulation of fish behavior is reflected in significantariations in the time spent exhibiting stress-related locomotoryatterns, proximity to the robots, and alignment with the robots’otion. Such varying interactions are not reminiscent of zebrafish

ocial behavior [45,65,66], suggesting that robots are unlikely to beerceived as conspecifics by live subjects. Furthermore, not onlyid the single fish exhibit considerably higher levels of freezingnd erratic movements than in experiments performed in a simi-ar setup with three-member shoals [49], but they also never spent

ore than 15% of the observation time near a robot. These findings

orroborate our previous results in [23], which indicated that fishreferred conspecifics to such robots in a preference test, and in49], where shoals of zebrafish were shown to stay closer to eachther than to a similar robot. In both these studies, we attributed

gnificantly affected by the number of robots (top) and their configuration (bottom),

this behavior to competing cues offered by the robot, which attractszebrafish through its color pattern, aspect ratio, and movement,but possibly repels them due to its large size and to the noise pro-duced by its servo motor. The same effects are in play here, albeitmodulated by the number of robots.

In agreement with our expectations (hypothesis H1a), increas-ing the number of robots changed fish behavior. In particular, thebehavioral response to a single robot, which is characterized bylarge freezing instances and erratic movements, was altered withthe number of robots in the tank. While a general reduction isobserved in these behavioral patterns, further comparisons indi-cate that these values did not change significantly at differentspeeds for two robots. In terms of zebrafish perception, it is possi-ble that the shoal of two robots is correctly seen as a larger groupthan one robot and is therefore able to attenuate the stress-relatedmeasures. While not perceived as conspecifics, the presence of mul-tiple robots likely enriches the environment in a social context[13,18,67]. However, the abatement of stress-related response didnot translate into an increase in the time spent near the robots.This suggests that a larger shoal of closely swimming robots wasnot associated with a proportional increase of the attractive cues,although repulsive cues were mitigated.

Interaction with the robots measured in terms of proximity

to a robot was also regulated by the configuration of the roboticshoal (hypothesis H1b). In particular, the fish swam closer to,and spent more time with the robots, when they swam diamet-rically apart. Nevertheless, a reduction in the distance may be

278 S. Butail et al. / Behavioural Brain Research 275 (2014) 269–280

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ig. 11. The time spent near the robots (within 15 cm) was not found to be significffected by robot configuration in the tank (bottom). Overhead bars denote post-hoound with one-way ANOVA. Error bars denote ±SEM.

artially attributed to an increased coverage of the tank by theobots when they are far. In other words, the probability of thesh to be found closer to a robot will be higher when the robotsre swimming far from each other. The same reasoning, how-ver, cannot be used to discount the observed increase in theime near the robots in the same condition. This is because theime near the robots is scored only when the fish is within oneobot-body length allowing sufficient room for the fish to stayway from the robot if needed. When the robots swim in oppositeirections far from each other, they may be perceived as less cohe-ive and less coordinated than when they are swimming together.

hile there are several studies among fish highlighting the prefer-nce for a group with more conspecifics [1,19], studies on individualreference as a function of the group cohesion or coordination areather sparse [68] and cannot be used to offer insight into our find-ngs. With the same number of individuals, does a single fish prefer

less cohesive shoal? Given that the fish spend similar times withach robot even as they swim far from each other, it is likely that iterceives them as part of a larger shoal [13]. Further studies withore robots and a gradual change in group cohesion are expected

o answer this question.Changing the configuration and number of robots also influ-

nced the alignment of the fish motion with a robot’s motion.hange in alignment with a robot when the robots swam in dif-

erent configurations (two close versus two far) is likely due to thencreased probability of fish matching the direction of motion at a

affected by the number of robots (top) or speed, but was found to be significantlyparisons (p < 0.05) within the close configuration after a significant difference was

given time with one of the robots if they swim in opposite direc-tions. However, a similar geometric argument is unlikely to explainthe change in alignment due to an increase in the number of robots(one versus two close), since the robots swim close to each other,and the probability of matching the direction with any one robotshould not increase significantly. At the same time, this tendency tomove in the same direction as the robots cannot be interpreted asschooling [66], since the average distance of the fish was on theorder of tens of centimeters. Instead, the significant increase inmotion alignment could be due to the fish trading off between thecompeting cues associated with robot visual appearance and mobil-ity to maintain a set distance from it. The absence of a time effectduring the experiment offers evidence that zebrafish response wasconsistent throughout the experimental time and that the observedinteraction with the robots was not due to habituation.

The absence of any effect of the robot speed (hypothesis H2)with a single robot is in contrast to results of a similar study [49],where the speed of the robot was found to significantly alter cohe-sion and group speed of small shoals of zebrafish. Nevertheless,an important difference from [49] is the absence of conspecificsthat likely played a role in modulating the speed through socialenrichment [13,18,69]. The absence of any effect of speed in all

behavioral measures except time near the robots when two robotswere present could be due to the possibility that the number andconfiguration of the robots outweigh the effect of their speed.While robot speed was systematically varied to change their level of

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ctivity, it is possible that the 1 cm s−1 difference in speeds was tooarrow to significantly influence fish behavior. At the same time,ince the fish were found to spend more time near the fastest robotair swimming close, this could be construed as an indirect evi-ence that varying the robots’ speed could be a valid direction toursue for modulating fish behavior. Future studies will focus onree-swimming robots capable of larger variations in speeds.

In summary, the results of this study contribute to improvingur understanding of zebrafish perception of bioinspired roboticsh. By varying the number, configuration, and speed of robots,e demonstrate that group size and configuration differentiallyodulate the response of zebrafish, dominating over effects thatay be related to changes in speed. The robots are able to swim in

redefined trajectories for the full experimental time, while main-aining a constant group size and arrangement. Future studies willocus on advancing the use of robotics to better understand theerception and integration of robots by zebrafish. These include, onne hand, investigating the long-term and developmental effects ofocial enrichments as in other animal models [70] through the usef bioinspired robotic fish, and, on the other hand, the developmentf capabilities to control the motion of robotic fish as a function ofhe behavioral response of zebrafish.

cknowledgments

We acknowledge the assistance from Vladislav Kopman andankaj Rajput in fabricating the robotic fish, and Fabrizio Ladu,arah Panitz, and Farihah Begum for independent verification andepair of the trajectory data. This research was supported by theational Science Foundation under Grant nos. CMMI-0745753,MMI-1129820, DRL-1200911, and DGE-0741714. Additional sup-ort has been provided in part by the Honors Center of Italianniversities (H2CU) through a scholarship to F. Del Sette.

ppendix A. Supplementary Data

Supplementary data associated with this article can be found, inhe online version, at http://dx.doi.org/10.1016/j.bbr.2014.09.015.

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