In this paper, we compare different aggregation strategies for cue-based aggregation with a mobile robot swarm. We used a sound source as the cue in the environment and performed real robot and simulation based experiments. We compared the performance of two proposed aggregation algorithms we called as the vector averaging and naïve with the state-of-the-art cue-based aggregation strategy BEECLUST. We showed that the proposed strategies outperform BEECLUST method. We also illustrated the feasibility of the method in the presence of noise. The results showed that the vector averaging algorithm is more robust to noise when compared to the naïve method

Arvin, Farshad

Bellotto, Nicola

Emre Turgut, Ali

Yue, Shigang

Farshad Arvin

Ali Emre Turgut

Nicola Bellotto

Shigang Yue

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Comparison of Different Cue-Based Swarm Aggregation Strategies

Turgut, Ali Emre

The University of Manchester - Institutional Repository

University of Lincoln Institutional Repository

Comparison of Different Cue-Based Swarm AggregationStrategiesFarshad Arvin1, Ali Emre Turgut2, Nicola Bellotto1, and Shigang Yue11 Computational Intelligence Lab (CIL), School of Computer Science, University of Lincoln,Lincoln, LN6 7TS, UK2 Laboratory of Socioecology and Social Evolution, KU Leuven, B-3000 Leuven, Belgium{farvin,syue}@lincoln.ac.ukAbstract. In this paper, we compare different aggregation strategies for cue-based aggregation with a mobile robot swarm. We used a sound source as the cuein the environment and performed real robot and simulation based experiments.We compared the performance of two proposed aggregation algorithms wecalled as the vector averaging and naı¨ve with the state-of-the-art cue-basedaggregation strategy BEECLUST. We showed that the proposed strategiesoutperform BEECLUST method. We also illustrated the feasibility of the methodin the presence of noise. The results showed that the vector averaging algorithmis more robust to noise when compared to the naı¨ve method.Keywords: swarm robotics, collective behavior, cue-based aggregation.1 IntroductionAggregation is a widely observed phenomenon in social insects and animals such ascockroaches, honeybees and birds [1]. It provides additional capabilities to animalssuch as forming a spore-bearing structure by slime mold or building a nest by termites[2]. In general, two types of aggregation mechanisms are observed in nature: cue-based or clueless. In cue-based aggregation, animals follow external cues to identifyoptimal zones such as a humid location for sow bugs and then they aggregate on thesezones. Whereas in clueless aggregation, animals aggregate at random locations in anenvironment such as aggregation of cockroaches [3]. From swarm robotics perspective[4], aggregation can be defined as gathering randomly distributed robots to form anaggregate. Due to limited sensing capabilities of the robots, aggregation turns out beone of the challenging tasks in swarm robotics.Many different studies have been performed in cue-based and clueless aggregationin swarm robotics. We first discuss cue-based aggregation. In one of the earliest studieson cue-based aggregation, Kube and Zhang [5] proposed an aggregation algorithm inwhich robots are required to aggregate around a light box and then push it. Melhuishet al. [6] proposed an algorithm for aggregation of robots around an infrared (IR)transmitter. The robots after reaching the IR transmitter start to emit sound resemblingthe vocalization of frogs and birds in order to help the other robots to estimate thesize of the aggregate they are in. Honeybee aggregation is another example of cue-based aggregation method that was studied in [7,8]. In these studies, micro robots weredeployed in a gradually lighted environment to mimic the behavior of honeybees whichY. Tan et al. (Eds.): ICSI 2014, Part I, LNCS 8794, pp. 1–8, 2014.c© Springer International Publishing Switzerland 20142 F. Arvin et al.aggregate at a zone that has the optimal temperature. An aggregation algorithm calledBEECLUST that relies on inter-robot collisions was proposed [9]. The aggregationmethod has been used and evaluated in several researches [10,11,12]. In another study[13], two modifications on BEECLUST – dynamic velocity and comparative waitingtime – were applied to increase the performance of aggregation. In addition, a fuzzy-based reasoning method has been proposed in [14,15] which increases the performanceof the system significantly.Clueless aggregation mechanism was employed in various studies. Trianni et al.[16] proposed an aggregation behavior of mobile robots using artificial evolution withstatic and dynamic strategies. In static strategy, when robots create an aggregate, theyare not allowed to leave it. However, in the dynamic strategy, robots are allowed toleave an aggregate with a certain probability depending on the size of the aggregate.Static strategy resulted in many small compact aggregates, whereas with the dynamicone robots are able to form a few larger aggregates. Soysal and S¸ahin [17] proposeda probabilistic aggregation method using a combination of basic behaviors: obstacleavoidance, approach, repel, and wait. They studied the effects of various parameterssuch as control mechanisms, time, and arena size in performance of the system. Thisstudy was continued by using an evolutionary approach in order to investigate thevarious effective parameters in aggregation, such as the number of generations, thenumber of simulation steps which are used for fitness evaluations, population size, andsize of arena [18]. Bayrindir and S¸ahin [19] proposed a macroscopic model to studythe effects of population size and probability of leaving an aggregate on aggregationperformance in a clueless aggregation scenario.In this paper, different than the previous studies, we compare three differentaggregation mechanisms in a cue-based aggregation scenario. Specifically, we extendthe BEECLUST algorithm and propose two new algorithms which we call as vectoraveraging and naı¨ve aggregation algorithms. Through systematic real-robot andsimulation-based experiments, we analyze and compare the performance differencesbetween those two algorithms and the BEECLUST algorithm.2 Aggregation Methods2.1 BEECLUSTBEECLUST aggregation [9] follows a simple algorithm as shown in the Fig. 1. When arobot detects another robot in the environment, it stops and measures the magnitude ofthe ambient audio signal and waits based on this magnitude. The higher the magnitude isthe longer the waiting time (w) becomes. The waiting time is estimated by the followingformula assuming that four microphones are used to detect the ambient audio signal:w(t) = wmax · Ma(t)2Ma(t)2 + μ, (1)where Ma(t) = 14∑4i=1 Mi(t) is the average magnitude of the four microphones, Miis the magnitude of signal from the ith microphone ranging from 0 and 255, wmax isthe maximum waiting time, and μ is a parameter which changes the steepness of thewaiting curve. wmax and μ are determined empirically: wmax = 65 sec and μ = 5500.Comparison of Different Cue-Based Swarm Aggregation Strategies 3Fig. 1. Control diagram of the aggregationWhen the waiting time is over, the robot rotates φ degree, which is a random variabledrawn uniformly within [−180◦, 180◦].2.2 Naı¨ve MethodIn the naı¨ve aggregation method, we employ a deterministic decision makingmechanism based on both the intensity and the direction of the sound signal. Thewaiting time is still calculated using (1) based on the average intensity (Ma), but inaddition to that, we estimate the direction of the sound source by setting it to the angleof the sound sensor that has the highest reading. φ = θi, i ∈ {1, 2, 3, 4} and θi is theangle of the sensor {45◦, 135◦, 225◦, 315◦} with respect to the frontal axis of the robothaving the highest reading.2.3 Vector Averaging MethodIn vector averaging method, the direction information and intensity of the sound sourceare utilized. We employ an averaging calculation based on both intensity and directionof sound signal to estimate φ.φ = atan2(∑4i=1 Mˆi sin(βi)∑4i=1 Mˆi cos(βi))(2)φ is the estimated angular position of the source speaker, βi is the angular distancebetween ith microphone and the robot’s head. Mˆi, i ∈ {1, 2, 3, 4} is the captured audiosignal’s intensity levels from microphone i.3 Experimental Setup3.1 Robot PlatformAMiR (Autonomous Miniature Robot) as shown in Fig. 2 (a) is an open hardware mobilerobot platform [20]. AMiR is specifically developed for swarm robotics studies. Therobot has two small geared DC motors that make it move with a maximum speed of8.6 cms[21]. Six IR proximity sensors using 60◦ topology is used, which allows AMiRto scan its surrounding area without turning [22]. AMiR also has an audio extensionmodule which is composed of four condenser microphones (45◦, 135◦, 225◦, and 315◦)as shown in Fig. 2 (b).4 F. Arvin et al.Fig. 2. (a) Autonomous Miniature Robot (AMiR) is equipped with an audio extension moduleand (b) architecture of developed audio signal processor module3.2 Simulation SoftwareIn order to test the proposed algorithm in large scale, Player/Stage simulation softwareis utilized. We modeled AMiR and its sensors in Player and used Stage as the simulationplatform.3.3 Experiment ConfigurationReal Robot Experiments. In real robot experiments, due to laboratory limitations, arectangular arena with a size of 120x80 cm2 is utilized. When compared to the sensingrange of the robots, the arena is approximately two times larger than the total sensingarea of 6 robots. The experiments are performed with different number of robots N ∈{3, 4, 5, 6}. We placed a sound source at one of the edges of the arena, that plays asingle tone of frequency of approximately 1050 Hz. The sound source serves as theenvironmental cue in the experiment. With the current configuration of the arena, theintensity and waiting times are shown in Fig. 3. Each experiment is repeated 10 timesand at the start of each run, the robots are placed in the arena with random positions andorientations.Fig. 3. (a) Intensity of audio signals in the arena and (b) relative waiting time in different positionsof the arena. Dashed arc shows the predefined optimal aggregation zone around the sound source.Comparison of Different Cue-Based Swarm Aggregation Strategies 5Simulation-Based Experiments. In simulation-based experiments, in order to uselarge number of robots N ∈ {5, 10, 15, 20, 25}, we used an arena with a size of240 × 160 cm2. Similar to the real-robot experiments, the experiments are repeated10 times and position and orientation of the robots are set randomly at the beginning ofeach experiment. We perform two sets of experiments: One set without sensing noise,the other with sensing noise. In the latter, we add noise to the sound measurements,which is modeled as a uniformly distributed random variable. For the ith microphone,the noise is added as Mi = |Mi + σρ| where Mi is sensor reading of ith microphone,σ ∈ {0.1, 0.3, 0.5} is the noise scaling factor which determines the amount of noise tobe added to the reading and ρ is a random value within [−255,+255].3.4 MetricsIn this study, we are interested in having fast aggregation around the sound source.Therefore, we use aggregation time as one of our metrics. In order to calculateaggregation time, T , we define an aggregation area which is shown with a dashed arcin Fig. 3. The aggregation time is the duration of an experiment in which the number ofthe robots aggregated in the aggregation area reaches 80% of the total robots.4 Results and Discussion4.1 Real Robot ExperimentsWe first performed experiments with real robots. In general, when the number ofrobots increases, the aggregation time reduces significantly as shown in Fig. 4a, sinceincreasing the population size increases the number of collisions eventually causingfaster aggregation. It should be noted that, the reduction rate in the aggregation time isnot the same in the three algorithms. Results show that, vector averaging aggregation isfaster than naı¨ve and BEECLUST.Fig. 4. Aggregation time as a function of population size for vector averaging, naı¨ve, andBEECLUST aggregation methods with (a) real robot and (b) simulated robot experiments6 F. Arvin et al.4.2 Simulation-Based ExperimentsIn these experiments, the aggregation methods are implemented using the simulator.Fig. 4b shows the aggregation time as a function of population size in vectoraveraging, naı¨ve, and BEECLUST methods. Results show that, population size hasa direct impact on the aggregation time. Simulation results also showed that thevector averaging method performs faster aggregation in comparison with naı¨ve andBEECLUST owing to more precise estimates φ values after each collision, whichincreases the performance of the aggregation. In case of BEECLUST that relies onrandom rotation, the aggregation time is longer than the others. Due to random rotations,robots occasionally move in opposite direction of the sound source, that results in robotsleaving the aggregation zone. Therefore, it increases the aggregation time and numberof collisions.Fig. 5. Effects of different noise values in different population sizes for vector averaging, naı¨veand BEECLUST methodsAlthough aggregation time decreases with increasing population size, when thedensity of swarm reaches a certain value the performance decreases. This is due toover-crowding effect observed, when the density of robots reaches a certain value [23].In our current setting, the performance increases up to 25 robots.The results of the experiments performed with sensing noise is depicted in Fig. 5 .The rotation angle, φ, for naı¨ve and vector averaging methods relies on the predictionof the direction of the sound source. However, in BEECLUST method which a randomvalue is used as the rotation angle, robots do not employ the magnitude of the audiosignal. Hence, only naı¨ve and vector averaging methods are tested against sensing noise.Results show that the vector averaging algorithm is more robust to noise than naı¨vealgorithm.4.3 Statistical AnalysisWe statistically analyzed the results of simulated robot experiments with and withoutnoise using analysis of variance (ANOVA), the F-test method (see Table 1).Comparison of Different Cue-Based Swarm Aggregation Strategies 7Population size has the highest influence on aggregation time for the vector averagingmethod (Fρ = 116.60), which means that increasing the swarm density improves theperformance of the vector averaging method the most. Noise has the least influenceon the vector averaging method (7.327, and 12.39 for the vector averaging and naı¨vemethods, respectively).Table 1. Results of F-test in analysis of variance (ANOVA)Experimental Parameters Aggregation MethodSetup Averaging Naı¨ve BEECLUSTWithout Noise Population 116.60 85.51 76.75With Noise Noise 7.32 12.39 –Population 34.28 28.43 –5 ConclusionIn this paper we evaluate three cue-based aggregation methods, namely, vectoraveraging, naı¨ve and BEECLUST. The aggregation methods were implemented usingreal- and simulated-robots with different population sizes. The results showed thatthe proposed vector averaging method improves the performance of the aggregationsignificantly. The swarm density has direct impact on the performance of theaggregation. Hence, an increase in population improves the performance of aggregationin all three algorithms. In addition to these, results revealed that the additional noise hasless impact on vector averaging method in comparison to the naı¨ve method.Acknowledgments. This work was supported by EU FP7-IRSES projects EYE2E(269118), LIVCODE (295151) and HAZCEPT (318907). The second author wouldthank TUBITAK-2219-Program.References1. Camazine, S., Franks, N., Sneyd, J., Bonabeau, E., Deneubourg, J.L., Theraulaz, G.: Self-organization in biological systems. Princeton University Press (2003)2. Parrish, J., Edelstein-Keshet, L.: Complexity, pattern, and evolutionary trade-offs in animalaggregation. Science 284(5411), 99–101 (1999)3. Jeanson, R., Rivault, C., Deneubourg, J.L., Blanco, S., Fournier, R., Jost, C., Theraulaz, G.:Self-organized aggregation in cockroaches. Animal Behaviour 69(1), 169–180 (2005)4. S¸ahin, E., Girgin, S., Bayındır, L., Turgut, A.E.: Swarm robotics. In: Blum, C., Merkle, D.(eds.) Swarm Intelligence, vol. 1, pp. 87–100. Springer, Heidelberg (2008)5. Kube, C., Zhang, H.: Collective robotics: From social insects to robots. AdaptiveBehavior 2(2), 189–219 (1993)6. Melhuish, C., Holland, O., Hoddell, S.: Convoying: Using chorusing to form travellinggroups of minimal agents. Robotics and Autonomous Systems 28(2), 207–216 (1999)7. 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Comparison of different cue-based swarm aggregation strategies

In this paper, we compare different aggregation strategies for cue-based aggregation with a mobile robot swarm. We used a sound source as the cue in the environment and performed real robot and simulation based experiments. We compared the performance of two proposed aggregation algorithms we called as the vector averaging and naive with the state-of-the-art cue-based aggregation strategy BEECLUST. We showed that the proposed strategies outperform BEECLUST method. We also illustrated the feasibility of the method in the presence of noise. The results showed that the vector averaging algorithm is more robust to noise when compared to the naive method

Comparison of different cue-based swarm aggregation strategies

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