Memòria de la Fase II de

Joves i Ciència

Research Science Institute 2018

Massachusetts Institute of Technology

Eloi Fernandez Puig

INTRODUCCIÓ:

Durant aquest estiu he tingut la gran oportunitat de participar en el programa

ResearchScienceInstitute (RSI) gràcies a Joves i Ciència. L’RSI és un programa del Centre for

Excelence in Education(CEE). I consisteix en un programa de recerca de sis setmanes

alMassachusettsInstitute of Technology (MIT).L’objectiu de RSI és proporcionar la oportunitat a

80 estudiants, 50 alumnes americans i 30 internacionals més o menys; de realitzar recerca

científica en alguns dels àmbits de l’anomenada educació STEM (Science, Technology,

Engineering, Mathematics).Molta gent està implicada en aquest programa a part dels

estudiants: Hi ha els councelors, els mentors, els tutors, els TA’s... Tots ells donen suport al llarg

del programa tot i que la millor ajuda ve donada pels companys, que ajuden sense cap problema

ens els moments més difícils.

PERIODE DE REALITZACIÓ DE L’ESTADA:

El programa RSI es va dur a terme del 23 de juny al 4 d’Agost de 2018, exactament sis setmanes.

ADREÇA:

El programa es va realitzaral campus del MassachusettsInstitute of Technology (77

MassachusettsAve, Cambridge, MA 02139, Estats Units) a la residència d’estudians del Campus

MIT “MacGregor House Dormitory” (450 Memorial Dr. Cambridge, Boston, MA 02139, USA). Tot

i així jo vaig realitzar gran part del meu projecte amb col.laboració amb el Departament

d’Enginyeria, CABCS, de la Universitat de Tufts, 419 Boston AvenueMedford, Boston, MA 02155,

USA.

DESCRIPCIÓ DEL CAMPUS:

El programa RSI es va dur a terme al campus universitari del MIT. A la zona Oest del campus es

trobava la residència d’estudiants on ens vam allotjar. Tot el campus estava ple d’edificis que o

bé eren residències, despatxos, laboratoris per a la recerca o establiments per a menjar. Tots els

edificis estaven enumerats. També hi havia varies biblioteques i sales d’informàtica amb

ordinadors per a ús dels estudiants repartides pel campus.

OBJECTIUS DEL PROGRAMA:

RSI és un programa que no està especialitzat en un àmbit concret de la ciència i el seu objectiu

principal és introduir i permetre viure l’experiència de la recerca als estudiants seleccionats. El

programa dona moltes facilitats per a que puguis desenvolupar la recerca. I t’assigna durant un

mes a un “mentor” amb qui desenvolupes aquesta recerca. A més un dels objectius és la

convivència en un ambient de recerca amb els companys de programa i d’altres programes

també.

ACTIVITATS DESENVOLUPADES EN EL PROGRAMA:

La primera setmana vam assistir a unes classes impartides per ex-alumnes de RSI. Hi havia dues

classes obligatòries: humanitats, on vam debatre la obra Frankeinstein de Mary Shelly, i

informàtica, on ens ensenyaven LateX, Beamer, i el sistema Athena del MIT. També vam escollir

dues assignatures més entre les opcions de matemàtiques, física, química, biologia i enginyeria.

Jo vaig triar física i enginyeria i van ser realment al·lucinants. Aprenies coses que realment eren

interessants com per exemple com es busquen noves partícules subatòmiques o com es pot

transmetre informació a través d’un fluorescent. A més durant aquesta setmana tothom també

va dur a terme un petit projecte que servia per familiaritzar-se amb les eines amb les que

estaríem fent l’article científic i per anar practicant per a la presentació final. A finals de la

setmana cadascú va conèixer el seu mentor, amb qui estarien fent la recerca les següents

4setmanes. Jo vaig tenir la sort de no estar sol en el projecte sinó tenir companys també de RSI

amb qui ajudar-nos mútuament.

Un altre aspecte excel·lent sobre RSI va ser la sèrie de conferències de convidats on destacats

doctors i científics van presentar els seus treballs i la seva recerca. Vam assistir a diverses

conferències com la del doctor Nobel Laureate Dr. Wolfgang Ketterle. Tots ells van ser molt

interessants i van provocar el meu interès en diversos temes de recerca. És inspirador tenir la

possibilitat de sopar amb alguns d'ells i fer-los preguntes sobre els seus camps d'investigació i

les seves meritoses carreres. Una de les meves conferències preferides va ser la conferència de

la doctora Molly Pepples. Anava sobre astrofísica, que m'interessa molt. Ens va mostrar una

simulació d'una galàxia que em va fascinar.

Després de tot l’esforç que comporta la recerca, cal fer un article per escrit i una presentació

oral, exposant en què ha consistit la recerca, que es presenta a continuació d’aquest escrit. Per

ajudar a fer tot això, teníem l’ajuda del tutor, que corregia i donava consells tant en la redacció

de l’article com en preparar la presentació. A més de l’ajuda proporcionada pel tutor també hi

havia els TA’s que corregien el teu article per a millorar-lo. Finalment a la última setmana s’havia

de presentar l’article acabat i per escrit a més de fer una presentació

VALORACIÓ DE L’ESTADA:

No sé ni per on començar, és una experiència fascinant, inoblidable. És increïble en molts

aspectes. És una experiència que val molt la pena perquè et fa adonar de molts aspectes, no tots

relacionats amb la ciència. Realment ha estat una de les millors experiències de la meva vida i

sempre en tindré un bon record. Tot i que vaig haver de treballar de valent, un cop he pogut

descansar a casa, he pogut veure que realment val molt la pena. Les classes eren apassionants,

et trobaves envoltat de gent amb passions similars a les teves i amb les qui podies comptar

sempre que volguessis. El “mentorship” va ser una experiència real de la recerca, que em va unir

molt amb els companys de projecte i amb els mentors. Ja que passes moltíssimes hores

treballant en el projecte i en el meu cas buscant errors en el codi...

Ser part de RSI és una experiència que no deixa a ningú indiferent, que et fa veure el món amb

una mirada diferent. Clarament RSI no ha estat el que m’esperava però ha estat una sorpresa

agradable. No sabria dir ben bé per quin motiu, és per la suma de tot de petites coses que ho

fan, com ja he dit inoblidable.

Swarm Robotics:Splitting groups scheme

Eloi Fernandez

Under the direction of

Dr. Dawn WendellDepartment of Mechanical EngineeringMassachusetts Institute of Technology

&

Dr. Matthew CainCenter for Applied Brain and Cognitive Science

Tufts UniversityU.S. Army Natick Soldier Research, Developement and Engineering Center

Research Science InstituteJuly 31, 2018

Abstract

The paper deals with studying strategies implemented by human operators whose task hasbeen to control a swarm of robots that could be split into two subgroups led by a preliminaryassigned leader for each of the subgroups. The user also could recombine the two subgroupsinto a single swarm. For this evaluation, we conducted a user test in a simulation, in whichthe subjects had to cross a simple maze and guide the swarm to an objective. A total of25 mazes had to be solved. For the evaluation, we measured different parameters like thetotal time to reach the objective and the number of collisions per maze. Sixteen subjectssolved each of the 25 mazes and the recorded the parameters were analyzed. Overall, westatistically determined that the strategy to split the swarm in two groups appears to be apromising way to control swarm of robots.

Summary

Swarm robotics is a very powerful approach to control a large number of robots, thatnowadays is catching substantial attention. Using swarms of robots, or large groups ratherthan individual units, can improve the ability to reach objectives or facilitate the delivery ofobjects, such as aid in the developing world. We analyze different strategies to control theseswarms to assess which ones are more intuitive and easy for human controllers. We validatedthe different strategies adopted by the users in order to gain insight into human behavior ina leadership position. We recorded different metrics and we discover unexpected insights tocontext-dependent leadership actions via control of swarm robots.

1 Introduction

1.1 Swarm robotics - a new field

Swarm robotics is an approach to the coordination of multi-robot systems (robotic swarms),

which consist of large numbers of relatively simple robots. These swarms are controlled by

an operator and individual robotic swarm members are guided by rules. These rules dictate

movement based on sensing and input from the human operator [1]. Robotic swarms could

be coordinated to work cooperatively towards a common goal [2].

Pre-programming autonomous robots, where each individual robot in the swarm knows

how to act before the deployment is already well-known. However, swarms are starting to be

used in more complex scenarios. As a result, further investigations are needed to determinate

how to control these swarms in real time after deployment [3].

Robotic swarms are useful in various tasks that require searching of large areas, e.g.

planetary science exploration, urban search and rescue, or landmine remediation [4], or for

tactical operations for the military.

1.2 Control schemes

Recent research has focused on studying the behaviour of robotic swarms under different

types of control schemes to manage swarms [5]. Control schemes can be categorized in two

different groups: Centralized and Distributed (Figure 1).

In Centralized schemes such as the “leader control scheme” the Leader receives the com-

mands from the human operator, and takes decisions for the entire swarm. The remaining

robots simply “follow the leader”.

Distributed schemes are those in which there is not a clear leader or a distinct robot which

takes decisions or receives all the commands. Instead, every robot receives the commands

directly from the commander. An example of this type of scheme is the “lockstep control

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scheme” where all the robots receive and execute the same commands so that all the robots

and the swarm itself move in the same way, similarly to a marching band [5].

Figure 1: Two types of control schemes: (a) Leader control scheme (the leader is marked asred), (b) Lockstep control scheme.

An example of recent development in other types of control schemes and interactions

between robots include a recent study utilizing the so called Bat algorithm to swarm robotics

[6]. The Bat algorithm is a powerful bio-inspired swarm intelligence method with remarkable

applications in several industrial and scientific domains. In the study, the swarm is applied to

the problem of coordinated exploration, where the individual self-organizing robots generate

an intelligent collective behavior. The feasibility and performance carried out demonstrated

that the Bat algorithm is well suited for coordinated exploration [6].

In this paper we examine the viability of swarm splitting as a method to find the most

intuitive and relatively easy way for humans to control robotic swarms.

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2 Methods

2.1 Experimental Design

In our proposed method the human operator can split and rejoin the swarm. To determine

if our proposal is more intuitive than other control schemes mentioned above, we undertake

a user test study. We use the Unity platform and C# scripts to design and create the user

test that consists of 25 mazes in which the user controls the swarm with the objective of

reaching the Goal Area (round circle) located at the end of the maze. When all the bots

arrive at the Goal Area, the next maze appears. The swarm is composed of one Leader, one

Coleader (that acts as a second leader when the swarm splits), and 10 Followers. At any

time the user has the options of splitting or combining the swarm. When the swarm splits,

5 Followers will follow the Leader and the other 5 will follow the Coleader. In this case,

the user has to control the Leader and Coleader simultaneously with different keys from the

keyboard. When the swarm is rejoined, the Coleader and all the Followers will follow the

Leader.

The maze consists of 5 gaps separated by barriers (Figure 2). The Goal Area is located

after the fourth barrier. A barrier is composed of one or more walls that are aligned in the

same y-axis. There are a total of 25 mazes which differ in position, distance between gaps,

and number of gaps in the barriers.

We have established several metrics to analyze the effectiveness of different strategies.

We measure the amount of collisions of the whole group when the swarm is not split, the

number of collisions of the Leader, those of the Coleader when the swarm is split, and the

number of collisions of the Followers of both subgroups separately when the swarm is split.

In addition, we have a general timer that counts the amount of time spent in each level and

a Split Timer that counts the amount of time that the swarm is being split. We also have

placed counters in the walls to know the amount of time that takes the human operator to

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Figure 2: Example of one of the mazes of the experiment

get all the bots through each gap and to know the total number of collisions in each barrier.

Specifically, the 18 metrics that we measure are:

1. Total Time: Time the swarm needs to reach the target.

2. Split Time: Time the swarm is split.

3. Group Collisions : Total amount of collisions of the swarm.

4. Alpha Group Collisions : Total amount of collisions of the Leader when the swarm is

not split.

5. Alpha Split Collisions : Total amount of collisions of the Leader when the swarm is

split.

6. CoAlpha Collisions : Total amount of collisions of the Coleader.

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7. Followers Collisions : Total amount of collisions of the followers when the swarm is

split.

8. Cofollowers Collisions : Total amount of collisions of the Coleader followers.

9. Gap Times : Time to get through each gap of the maze.

10. Barrier Collisions : Number of collisions on each barrier of the maze.

We conducted our user test on 16 subjects. At the beginning of the test, each user read

the instruction text shown in the first screen. Four keys (W, S, A, D) corresponding to the

four basic directions are used to move the Leader through the maze. The four arrow keys are

used to move the Coleader. By pressing the spacebar the user splits and rejoins the swarm.

When all the 12 bots reach the target the next maze appears automatically. The first maze

is a “practice level” where the metrics are not recorded. All the following mazes are part of

the experiment where metrics are recorded.

Strategies are evaluated principally by analyzing how often users split the swarm, total

time to finalize the maze compared to the time that the swarm was split and by comparing

the number of collisions when the user splits the swarm to when it does not.

The program developed for the user test is based on 2D gaming techniques. The program

slightly changes the position of the bots of the swarm giving the sense of motion in the

simulation at a rate of 60 times per second. Also at this rate, the program checks the

keyboard to notice whether the user wants to change the direction of the Leader or the

Coleader in case the swarm is split.

2.2 Analysis

For the analysis we used R, a program suite that allows to manage statistical data and

plot the corresponding graphs. 16 subjects performed the experiment. However one of the

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subjects was excluded from the analysis because he/she did not follow the given instructions.

9 subjects decided to never split the swarm. This represents 56% of the subjects, which was

a prominent subset. The subjects were divided into two groups depending on if they split

the swarm at least once at any of the levels or not at all. This enabled us to introduce an

additional evaluation analysis that compared both types of human behavior. The “practice

level” allowed subjects to familiarize themselves with the controls. Therefore it was removed

from the analysis. In addition, the levels that had excessive error, where too much time was

spent on one maze, were also removed. The following criteria was used: The time in one

of the levels has to be smaller than the mean of the total time per level plus 3 times the

standard deviation of the total time per level (Equation 1). Therefore, the trials that did not

fulfill this criteria were excluded from the analysis.

too error = mean(TotalT ime) + (3 ∗ sd(TotalT ime)) (1)

To carry out the analysis, I defined some new metrics that were product of the metrics

recorded in the user tests. The new metrics are:

• Total Collisions : consist in all the collisions from the different subgroups added.

• Split Collisions : consist in the Followers Collisions and the Cofollowers Collisions

added.

• Collisions per second : consist in the amount of collisions of one group of robots divided

by the amount of time that the group was used in the maze.

From the potential 400 trials (25 levels x 16 subjects), the analyses was carried out with

372 trials. A selected number of the acquired metrics that were representative of the whole

were used for the analysis presented below. For this analysis due to time limitations, the

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Gap Times and the Barrier Collisions were not used. But this analysis can be carried out

in future work. The analysis performed was always referred as an average of all the mazes.

A standard deviation analysis was done in some of the cases and is shown as error bars in

the graphics.

For the group of subjects that did not split the swarm at all (Block A), we analyzed

for each subject, the average Total Time, the average number of Group Collisions and the

average number of Alpha Group Collisions. For the subjects that did split the swarm (Block

B), we analyzed in addition to the block A metrics, the average Split Time and the proportion

of Split Time with respect to the Total Time. Finally, we compared the average Total Time,

average number of Group Collisions and average number of Alpha Group Collisions between

both groups. We applied a T-test statistical analysis to obtain the t and p values as a measure

of the statistically significance of the results.

3 Results and Discussions

Following are the results obtained for the different blocks. Block A is composed of 56%

of the subjects who decided to never split the swarm, whereas Block B corresponds to those

subjects that at least once decided to split the swarm in two subgroups. Block A was large,

suggesting that many subjects anticipated the difficulty of governing two subgroups of robots

independently.

3.1 Analysis of Block A: Subjects that never split the swarm

Figure 3 shows the average total time for the 9 subjects with the corresponding standard

deviation. Notice that all subjects spent between 41.24 s and 95.89 s to reach the goal for

each level with a mean value for all subjects of 59.71 s. The minimum standard deviation was

6.8 and the maximum was 52.90 with a total average between the subjects of 15.70 . Notice

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Figure 3: Average Total Time per level of each subject from block A

that in some cases the standard deviation is relatively low, which indicates that the subject

needed more or less the same amount of time to complete each level. Instead, those subjects

that have a larger standard deviation indicates that the amount of time needed to complete

each level was different, probably because the subject was getting more comfortable with

the controls as the experiment was going on.

The average number of Total Collisions for the subjects that did not split is presented in

Figure 4. The number of collisions per level is between 14.80 and 29.12, with a mean value

between all the subjects of 20.13 . The standard deviation between 1.89 and 3.13 suggests

that the number of collisions was similar throughout all the levels.

Figure 5 shows the average number of Alpha Group Collisions for the subjects that did

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Figure 4: Average Total Collisions per level of each subject from block A

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Figure 5: Average Alpha Group Collisions per level of each subject from block A

not split. The average Alpha Group Collision per level stays between 0.88 and 2.77 for

the different subjects, which is a low number of collisions compared to the value of the

Total Collisions indicating that most of the collisions were produced by the Followers. The

standard deviation values are between 1.12 and 2.39 with a mean value of 1.63 . These values

are low, indicating that the number of Alpha Group Collisions is almost constant through

all the levels.

3.2 Analysis of Block B: Subjects that split the swarm

The subjects that split the swarm into two subgroups spent an average total time per level

between 43.75 s and 75.73 s to reach the goal (Figure 6). With a mean standard deviation

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Figure 6: Average Total Time per level of each subject from block B

of 12.04, which is similar to the standard deviation of the Total Time for the block A. The

mean value of the average total time for the different subjects of group B was 55.51 s.

In order to further understand how group B operated, The average Split Time has been

analyzed for the different subjects (Figure 7). Here we notice that there are two types of

subjects within group B, those who split the swarm with an average split time of about 32 s

per level (group B1) and those who only used an average split time between 5.36 s and 16.88

s per level (group B2). In fact, these two populations are 50% distributed within group B.

Notice that group B1 has a very high standard deviation in the average split time parameter

(from 12.52 to 22.14), indicating that the Split Time was not constant through the levels. In

fact this indicates that the amount of Split Time depends on the configuration of each maze,

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Figure 7: Average Split Time per level of each subject from block B

making the users variate the amount of time that they were split. Instead group B2 has a

very low standard deviation this is because group B2 only split the swarm in some occasions

so the standard deviation value does not give a good sense of the variation of Split Time.

Figure 8 displays the percentage of the split time related to the total time, for the different

subjects from block B, demonstrating that half of them split the swarm for an average time

of around 58.52% (group B1), whereas the other half split the swarm between 12.04% and

35.39% (group B2) of the time. Group B1 strongly believed that the use of a Coleader

would benefit the strategy of reaching the goal faster and with minimum collisions, whereas

group B2, which only split the group for a moderate amount of time, maybe thought that

splitting would produce excessive effort. This suggests that half of the subjects of group B

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Figure 8: Split Time related to Total Time in average per level

weren’t comfortable splitting the swarm, perhaps because of the difficulty of controlling a

split swarm. The other half was convinced that the use of a Coleader would benefit their

performance.

The average number of Total Collisions for the different subjects of group B is displayed

in Figure 9. Notice that the mean standard deviation is 2.31 . If we compare Figure 8 and

Figure 9 we can see that there is not a clear correlation because in Figure 9 we cannot

distinguish between group B1 and group B2 as we did in the previous analysis.

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Figure 9: Average Total Collisions per level of each subject from block B

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Figure 10: Comparison of Total Time when split and when not (block B subjects average)

Comparison behavior within Block B

Further analysis of the behavior of group B is shown in Figures 10, 11 and 12. Figure

10 displays a comparison between the total time to finalize a maze for the levels where the

swarm was split and where it was not. Here we note that the total time for the levels that

were split is slightly higher than for those which were not, 59.54 s and 51.37 s, respectively.

This is due to the complexity of driving two subgroups. There was a significant difference

between conditions t = −5.0575, p < 8.766−07.

Next we undertake a comparison between the average number of Collisions per second

when the group is split (Split Collisions) or not (Group Collisions) (Figure 11). Notice that

there is a large difference between both conditions, the Split Collisions being less than half

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Figure 11: Comparison of Collisions when split and when not (block B subjects average)

of the Group Collisions (0.15 collision/s vs. 0.38 collisions/s, respectively). This could be

explained with the fact that when the swarm is not split, it is composed of 12 Bots that try

to fit into a narrow gap. While when the swarm is split, each subgroup, has 6 Bots that can

fit better through the narrow gaps. Therefore, it is easier to cross the barriers through the

gaps and avoid collisions with a subgroup rather than with the whole swarm and it confirms

that the strategy of using a Coleader is very promising. There was a significant difference

between conditions t = 2.4715, p < 0.03405.

Finally, in Figure 12 we compare the average number of Alpha Collisions per second when

the group is split and not. For this case, the number of collisions per second of the Alpha

when split is also reduced by approximately half related to the Alpha collisions when not

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Figure 12: Comparison of Alpha Collisions when split (Alpha Split Collisions) and when not(Alpha Group Collisions) average of block B subjects

split, 0.016 and 0.030 collisions/s respectively. This is quite an unexpected result since one

would expect a larger number of collisions and an increased difficulty in driving a Leader and

a Coleader simultaneously, however there was not a significant difference between conditions

t = 1.2383, p < 0.2442, so no concluding conclusion can be extracted from this comparison.

3.3 Comparison behavior between Block A and B

Finally, in Figures 13, 14, and 15 we try to make a comparison between the human

behavior of block A (who decided to never split the swarm) and block B (who split the swarm

at some point). Figure 13 shows that the average total time for both populations is similar,

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Figure 13: Comparison of average Total Time per level between block A subjects and blockB subjects

59.71 s for group A and 55.51 s for group B, with a significant difference between conditions

t = −2.0242, p < 0.04365, thus validating the relevance of the result and confirming that it

is slightly faster to use the strategy of splitting the swarm at some point of the user test.

The analysis of the average number of collision for group A and B is displayed in Figure

14. The average number of the two populations is almost the same value (19.89 and 20.13

collisions per level, respectively). There was not a significant difference between conditions

(t = −0.25995, p < 0.7951), since both populations collided almost the same amount of

times, even though one of the groups split and the other did not.

Finally, Figure 15 compares the Alpha Group Collisions for the subjects who never split

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Figure 14: Comparison of average Total Collisions per level between block A subjects andblock B subjects

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swarm (group A) and the subjects that split the swarm at some point (group B). Here we

note that the number of Alpha Group collisions is larger for group A than for group B,

being 1.68 and 1.06 collisions per level, respectively. This is in agreement with the results of

Figure 12 where we concluded that the Leader collisions do not increase when swarm is split,

even though that’s what we would expect due to the difficulty of controlling both subgroups

simultaneously. Now, however, there was a significant difference between conditions t =

−3.1464, p < 0.00181. This does not necessarily mean that it is easier to direct the Leader

when the swarm is split. Instead, this result could be product of the subjects skills, since

one of the blocks did not split at all.

Figure 15: Comparison of average Alpha Group Collisions per level between block A subjectsand block B subjects

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One possible avenue for future research is an experiment similar to this one that does not

allow the subjects to split the swarm. Instead, the subject would have to control one swarm

in some of the mazes, and two subgroups in others.

4 Conclusion

A swarm of robots is composed of a Leader, a Coleader, and 10 Followers being driven by

one single person through 25 mazes. A user test was designed, programmed, and tested by 16

subjects, then user data was statistically analyzed to identify human behavior in controlling

swarms. A sample of 372 trials has been finally validated in the study, in which we defined

two blocks: block A, with 56% of the subjects, who never used the split facility of the

Coleader, and a block B, the remaining 44%, who decided to split the swarm at some point.

We hypothesize that a significant number of subjects anticipated difficulties in governing

both subgroups of robots independently and simultaneously. Although the total time spent

to finalize a level is on average between 50 s and 60 s in all cases, the number of Collisions

per second significantly decreases from 0.38 collisions/s down to 0.18 collisions/s when the

subjects split the swarm at some point. We observe that the strategy of using a Coleader

might be very promising in reducing the number of collisions, even though we realize that

most of the subjects were not comfortable using it. Further studies can assess more outcomes

of using Coleader -based robot swarms under human control.

5 Acknowledgments

I would first like to thank my mentors Dr. Wendell and Dr. Cain for designing this amazing

project. Also for providing me the bibliography needed for a proper background and for

their guidance along the way of this project. Grace Elliot for the experiment data collection.

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Secondly I want to give a great acknowledgement to Jenny Sendova, who helped me in

improving my paper and my presentation skills. I want to thank my project mates: Emre

Onal and Jonathan Ko for helping each other through out the project and William Ellsworth

for helping me with the data analysis. I also want to acknowledge Syamantak Payra, Sidhika

Balachadar and Zoe Weiss for giving me feedback and advise on the paper writing. I would

like to thank MIT, CEE, and RSI for giving me the opportunity to come to the Research

Science Institute. Lastly, I would like to recognize my sponsors: Ms. Carla Conejo Gonzalez,

Coneixement i Recerca, Fundacio Catalunya-La Pedrera and Ms. Eva Calves Parcerisas,

Director of Youth and Science Programs at Fundacio Catalunya-La Pedrera.

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References

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[2] Y. Mohan and S. G. Ponnambalam. An extensive review of research in swarm robotics.IEEE, pages 140–145, 2009.

[3] P. Walker, S. A. Amraii, M. Lewis, N. Chakraborty, and K. Sycara. Human control ofleader-based swarms. pages 2712–2717, October 2013.

[4] F. Steele and G. Thomas. Directed stigmergy-based control for multi-robot systems.ACM, page 223, 2007.

[5] A. Farinelli, L. Iocchi, and D. Nardi. Multirobot systems: a classification focused on coor-dination. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics),34:2015–2028, October 2004.

[6] P. Suarez and A. Iglesias. Bat algorithm for coordinated exploration in swarm robotics.Springer Singapore, 514:134–144, 2017.

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