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5th International Conference on Advances in Control and 5th International Conference on Advances in Control and Optimization of Dynamical Systems 5th International Conference on in and 5th International Conference on Advances Advances in Control Control Optimization of Dynamical Systems Available onlineand at www.sciencedirect.com February 18-22, 2018. Hyderabad, India Optimization of Dynamical Systems 5th International Conference on Advances in Control and Optimization of Dynamical Systems February 18-22, 2018. Hyderabad, India February 18-22, 2018. Hyderabad, India Optimization of Dynamical Systems February 18-22, 2018. Hyderabad, India February 18-22, 2018. Hyderabad, India

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IFAC PapersOnLine 51-1 (2018) 512–517

BflyBot: BflyBot: Mobile Mobile robotic robotic platform platform for for BflyBot: Mobile robotic platform for implementing Butterfly mating BflyBot: Mobile robotic platform for implementing Butterfly mating implementing Butterfly mating phenomenon implementing Butterfly mating phenomenon phenomenon phenomenon∗∗ Ashok Urlana ∗∗∗ Chakravarthi Jada ∗∗ Lokesh chintala ∗∗ ∗∗∗

Chakravarthi Jada Lokesh∗∗∗∗ chintala Ashok ∗ ∗∗ ∗∗∗ † ∗ Lokesh ∗∗ Baswani ∗∗∗ Chakravarthi Jada chintala Ashok Urlana Urlana Shaik Gouse Basha Pavan ∗∗∗∗ † Chakravarthi Jada chintala Ashok Urlana Shaik Gouse Basha Pavan Baswani ∗ Lokesh ∗∗ ∗∗∗ ∗∗∗∗ † Chakravarthi Jada Lokesh chintala Ashok Urlana ∗∗∗∗ † Shaik Gouse Basha Pavan Baswani Shaik Gouse Basha ∗∗∗∗ Pavan Baswani † ∗ Shaik Gouse Basha Pavan Baswani ∗ Electrical Engineering, RGUKT, Nuzvid, India (e-mail: Electrical Engineering, RGUKT, Nuzvid, India (e-mail: ∗ ∗ Electrical Engineering, RGUKT, India [email protected]) .. RGUKT, Nuzvid, Nuzvid, India (e-mail: (e-mail: [email protected]) ∗ Electrical Engineering, Electrical Engineering, RGUKT, Nuzvid, India (e-mail: [email protected]) .. [email protected]) [email protected]) . Abstract: The diversified ecology in the nature had various forms of swarm behaviors in many Abstract: The diversified ecology in the nature had various forms of swarm behaviors in many Abstract: The diversified ecology in the nature had various forms of swarm behaviors in many species. The butterfly species is one of the prominent and a bit insight in their random flights Abstract: The diversified ecology inofthe nature had various forms of swarm behaviors in flights many species. The butterfly species is one the prominent and a bit insight in their random Abstract: The diversified ecology in the nature had various forms of swarm behaviors in many species. The butterfly butterfly species is one one of the the prominent prominent and aenormous bit insight insightpossibilities. in their their random random flights and convert that into artificial metaphor would leads to This paper species. The species is of and a bit in flights and convert that into artificial metaphor would leads to enormous possibilities. This paper species. The butterfly species is one of the prominent and a bit insight in their random flights and convert that into artificial metaphor would leads to enormous possibilities. This paper considers one such metaphor known as Butterfly Mating (BMO). In BMO, the and convert that into artificial metaphor would leads toOptimization enormous possibilities. This paper considers one such metaphor known as Butterfly Mating (BMO). In BMO, the and convert that into artificial metaphor would leads toOptimization enormous possibilities. This paper considers one such metaphor known as Butterfly Mating Optimization (BMO). In BMO, the Bfly follows the patrolling mating phenomena and simultaneously capture all the local optima of considers one such metaphor known as Butterfly Mating Optimization (BMO). In BMO, the Bfly follows the patrolling mating phenomena and simultaneously capture all the local optima of considers one such metaphor known as Butterfly Mating Optimization (BMO). In BMO, the Bfly follows the patrolling mating phenomena and simultaneously capture all the local optima multimodal functions . This paper studied the various steps involved in the BMO and envisaged Bfly follows the patrolling mating phenomena and simultaneously capture allBMO the local optima of of multimodal in the and envisaged functions . This paper studied the various steps involved Bfly follows the patrolling mating phenomena and simultaneously capture all the local optima of multimodal functions This paper papertermed studiedasthe the various to steps involved in source the BMO BMO andworkspace envisaged to apply on multi mobile-robots BflyBots, detect aa light in the multimodal functions .. This studied various steps involved in the and envisaged to apply on multi mobile-robots termed as BflyBots, to detect light source in the workspace multimodal functions . This paper studied the various steps involved in the BMO and envisaged to apply on multi mobile-robots termed as BflyBots, to detect a light source in the workspace and sequentially embedded part and experimental results of BflyBot sense to apply on multipresents mobile-robots termed as BflyBots, to detect a light source which in thecould workspace and sequentially embedded part and experimental results of BflyBot which sense to apply on multipresents mobile-robots termed as BflyBots, to detect a light source in thecould workspace and sequentially presents embedded part and experimental results of BflyBot which could sense the light intensity using LDR’s with the help of gyro, communicate with other bots using and sequentially presents embedded part and experimental results of BflyBot which could sense the light intensity using LDR’s with the help of gyro, communicate with other bots using and sequentially presents part and experimental results of BflyBot which could sense the light light intensity using embedded LDR’s with the help of gyro, gyro, communicate withare other bots using using Xbee modules simultaneously and select their local mate. The experiments conducted and the intensity using LDR’s with the help of communicate with other bots Xbee modules simultaneously and select their local mate. The experiments are conducted and the intensity using LDR’s thetheir helplocal of gyro, withare other bots using Xbeelight modules simultaneously andwith select their local mate. communicate The experiments experiments are conducted and preliminary results are presented. Xbee modules simultaneously and select mate. The conducted and preliminary results are presented. Xbee modules simultaneously and select their local mate. The experiments are conducted and preliminary results are preliminary results are presented. presented. © 2018, IFACresults (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. preliminary are Patrolling, presented. Keywords: Butterflies, Butterflies, B-flies, BflyBot, BflyBot, Zigbee, Zigbee, Localization. Localization. Keywords: Patrolling, Keywords: Butterflies, Butterflies, Patrolling, Patrolling, B-flies, B-flies, BflyBot, BflyBot, Zigbee, Zigbee, Localization. Localization. Keywords: B-flies, Keywords: Butterflies, Patrolling, B-flies, BflyBot, Zigbee, Localization. 1. INTRODUCTION People also have have inspired inspired from from the the swarm intelligence intelligence 1. People 1. INTRODUCTION INTRODUCTION Peopleofalso also have inspired from the swarm swarm intelligence behavior the species and designed various artificial 1. INTRODUCTION People also have inspired from the swarm intelligence behavior the species and designed various artificial 1. INTRODUCTION Peopleof also have inspired from the swarm intelligence behavior of the species and designed various artificial metaphors such as Particle Swarm Optimization (PSO), behavior of the species and designed various artificial Nature is is inhabitant inhabitant for for enormous enormous number number of of species. species. metaphors such as Particle Swarm Optimization (PSO), Nature behavior ofsuch the as and designed various artificial metaphors such asspecies Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Glowworm Swarm Opmetaphors Particle Swarm Optimization (PSO), Nature is inhabitant for enormous number of species. These species exhibit and follow different customs and Ant Colony Optimization (ACO), Glowworm Swarm OpNature is inhabitant for follow enormous number of species. These species exhibit and different customs and metaphors such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Glowworm Swarm Optimization (GSO) etc to multimodal search spaces. And Nature inhabitant for enormous of species. Ant Colony(GSO) Optimization (ACO), Glowworm Swarm And OpThese species exhibit inand and follow different customs and timization strategies tois sustain their life. different Fornumber instance, though etc to search spaces. These species exhibit follow customs and strategies to in their life. For instance, though Ant Colony(GSO) Optimization (ACO), Glowworm Swarm And Optimization (GSO) etc to multimodal multimodal search systems. spaces. And implemented these etc models into multi-robotic multi-robotic For These species exhibit and follow different customs and implemented timization to multimodal search spaces. strategies to sustain sustain inants their life. Forsimple instance, though they are tiny in size, do apply cooperative these models into systems. For strategies to sustain in their life. For instance, though they are tiny in size, ants do apply simple cooperative timization (GSO) etc to multimodal search spaces. And implemented these models into multi-robotic systems. For various applications and they have shown extensive ways strategies to sustain in their life. For instance, though implemented these models into multi-robotic systems. For they are tiny in size, ants do apply simple cooperative rules are among themselves for do foraging, buildingcooperative nests and various applications and they have shown extensive ways they tinythemselves in size, ants apply simple rules among for foraging, building nests implemented models intohave multi-robotic systems. For various applications and they they have shown extensive ways of solving solving thethese problems. Recently, by inspiring fromways the they are tinyactivities in size, in ants do apply lives. simple cooperative various applications and shown extensive rules among themselves for foraging, building nests and and other social their daily Similarly, fish of the problems. Recently, by inspiring from the rules among themselves for foraging, building nests and other social activities in their daily lives. Similarly, fish various applications and they have shown extensive ways of solving the problems. Recently, by inspiring from the bizarre flight of the butterflies, our research group (2016) rules among themselves for foraging, building nests and of solving theofproblems. Recently, by inspiring from the other social activities in their their maneuverings daily lives. lives. Similarly, Similarly, fish schooling willactivities do extraordinary extraordinary to avoid avoid fish the bizarre flight the butterflies, our research group (2016) other social in daily schooling will do maneuverings to the of theof Recently, inspiring from the bizarre flight ofaproblems. the butterflies, our by research group (2016) hassolving proposed swarm intelligence algorithm named as other social activities inabove their said dailyspecies lives. but Similarly, fish bizarre flight the butterflies, our research group (2016) schooling will doonly extraordinary maneuverings toalso avoid the has predators. Not the many proposed a swarm intelligence algorithm named as schooling will do extraordinary maneuverings to avoid the predators. Not only the above said species but also many bizarre flight of the butterflies, our research group (2016) has proposed a swarm intelligence algorithm named as ”Butterlfly Mating Optimization (BMO)” which would sischooling will do extraordinary maneuverings to avoid the has proposed a swarm intelligence algorithm named as predators. Not only the above said species but also many others such as birds for travelling longer distances, honey Mating Optimization (BMO)” which would predators. Not only the above saidlonger species but also honey many ”Butterlfly others such as for distances, has proposed a swarm algorithm named as ”Butterlfly Mating Optimization (BMO)” which would sisimultaneously capture all intelligence the local local(BMO)” optima of a multimodal multimodal predators. Not onlynectar, the above saidlonger species but also many multaneously ”Butterlfly Mating Optimization which would siothers such as birds birds for travelling travelling longer distances, honey bees for forsuch collecting termites for building building complex capture all the optima of a others as birds for travelling distances, honey bees collecting nectar, termites for complex ”Butterlfly Mating Optimization (BMO)” which would simultaneously capture all the local optima of a multimodal search functions. This paper deals with the hardware arothers such as birds for travelling longer distances, honey multaneously capture all the local optima of ahardware multimodal bees for collecting collecting nectar, termites for building complex apartments etc are most most popular. All for these species complex perform search functions. This paper deals with the arbees for nectar, termites building apartments etc popular. All these species perform multaneously capture the local optima of ahardware multimodal search functions. Thisall paper deals with named the hardware architecture of the the mobile robotic platform as Bflybot Bflybot bees for collecting nectar, termites building complex search functions. This paper deals with the arapartments etc are are most popular. All for these species perform these complex activities using simple and elegant rules. chitecture of mobile robotic platform named as apartments etc are most popular. All these species perform these complex activities using simple and elegant rules. search functions. This paper deals withand thecapabilities hardware architecture of the the mobile mobile robotic platform named as Bflybot Bflybot which is designed to the requirements of apartments etc are most popular. All these species perform chitecture of robotic platform named as these complex activities using simple and elegant rules. They perform these activities using their very limited body which is designed to the requirements and capabilities of these perform complexthese activities using simple and elegant rules. They activities using their very limited body chitecture of the mobile platform named as it Bflybot which is in designed to algorithm therobotic requirements and capabilities of the Bfly the BMO in view of utilizing to dethese complex activities using simple and elegant rules. which is designed to the requirements and capabilities of They perform these activities using their very limited body shape, sensorimotor parts, and communication systems. Bfly the BMO algorithm in view of utilizing it to They perform these activities using their very limited body the shape, sensorimotor parts, and communication systems. which is in designed tosources the requirements and capabilities of the Bfly in thesignal BMO algorithm in2-D view of utilizing itvarious to dedetectBfly multiple in aain environment, They perform these activities using their very limited body the in the BMO algorithm view of utilizing it to deshape, sensorimotor parts,know and communication systems. For instance, instance, mosquitoes the human presence by tect multiple signal sources in 2-D environment, various shape, sensorimotor parts, and communication systems. For mosquitoes know the human presence by the Bfly in the BMO algorithm in view of utilizing it to detect multiple signal sources in a 2-D environment, various individual sensing experimental results also presented and shape, sensorimotor parts, and communication systems. tect multiple signal sources in a 2-D environment, various For instance, mosquitoes know the human presence by sensing heat released released by know human. Similarly, bats sense sense sensing experimental also and For instance, mosquitoes theSimilarly, human presence by individual sensing heat by human. bats tect multiple signal sources a results 2-D environment, various individual sensing experimental results also presented presented and discussed couple ofexperimental ongoinginand and prospective works. Next For instance, mosquitoes theSimilarly, human presence by discussed individual sensing results also presented and sensing heat movements released byofknow human. Similarly, bats sense environment the prey, obstacles etc using couple of ongoing prospective works. Next sensing heat released by human. bats sense environment movements of the prey, obstacles etc using individual sensing experimental results also presented and discussed couple of ongoing and prospective works. Next part of this paper presents the brief survey on the major sensing heat movements released byof Similarly, sense part discussed couple ofpresents ongoingthe andbrief prospective works. Next environment movements ofhuman. the prey, obstacles etc using using the phenomena of reflection of ultrasonic ways.bats of this paper survey on the environment the prey, obstacles etc the of of ways. discussed couple ongoing and prospective works. Next part of algorithms this paperofof presents the brief survey on the major major swarm similar the to BMO and the on architectures environment movements of the prey, obstacles part of this paper presents brief survey the major the phenomena phenomena of reflection reflection of ultrasonic ultrasonic ways. etc using swarm algorithms of similar to BMO and the architectures the phenomena of reflection of ultrasonic ways. part of algorithms this paper of presents the brief and survey on the major Various research groups are inspired from the strateswarm algorithms of similar to BMO and the architectures of mobile robotic platforms designed to implement those the phenomena of reflection of ultrasonic ways. swarm similar to BMO the architectures Various research groups are from the mobile robotic platforms designed to those Various research groups are inspired inspired from the stratestrate- of swarm algorithms similar to BMO and the architectures gies Various of biological biological species and applied applied at daily daily technological of mobile robotic of platforms designed to implement implement those algorithms. research groups are inspired from the strateof mobile robotic platforms designed to implement those gies of species and at technological algorithms. Various research groups are inspired from the strategies of biological biological speciestoand and applied at daily daily technological of mobile robotic platforms designed to implement those aspects and succeeded a large extent. Specifically, the algorithms. gies of species applied at technological algorithms. aspects and succeeded to aa large extent. Specifically, the gies of biological species applied at daily technological aspects and succeeded toand large extent. Specifically, the algorithms. present fast succeeded growing embedded technology is giving giving the 2. RELATED WORKS WORKS aspects and to a large extent. Specifically, present fast growing embedded technology is the 2. aspects and to aand large extent. Specifically, the present fast succeeded growing embedded technology is giving giving pro2. RELATED RELATED WORKS WORKS boost. Recently developed easy to use embedded present fast growing embedded technology is the 2. RELATED boost. Recently developed and easy to embedded propresent fast as growing embedded technology isand giving the RELATED boost. Recently developed and easy to use use etc embedded proBio-inspired 2. robotics is the the WORKS most prevalent prevalent choice choice for for cessorsRecently such Raspberry pi, beaglebone beaglebone sensors boost. developed and easy to use embedded proBio-inspired robotics is cessors such as Raspberry pi, etc and sensors boost. Recently developed and easy to use embedded proBio-inspired robotics is days. the most most prevalent choice for cessors such asdepth Raspberry pi, beaglebone etc and and sensors many applications in these In specific, swarm based such as kinect sensor, hokuyo laser range finder, gas Bio-inspired robotics is the most prevalent choice for cessors such as Raspberry pi, beaglebone etc sensors many applications in days. In swarm based such as kinect sensor, hokuyo laser finder, gas Bio-inspired the most prevalent choice for cessors such Raspberry beaglebone etc and sensors many applications in these theseis days. In specific, specific, swarm based such as humidity kinectasdepth depth sensor, hokuyo laser range range finder, gas robotic approachrobotics adding cooperation as the theswarm main ingresensor, sensor arepi, some among those. Added to robotic many applications in these days. In specific, based such as kinect depth sensor, hokuyo laser range finder, gas approach adding cooperation as main ingresensor, humidity sensor are some among those. Added to many applications in these days. In specific, swarm based such as kinect depth sensor, hokuyo laser range finder, gas robotic approach adding cooperation as the main ingresensor, humidity sensor are some among those. Added to dient to the main stream. Over the recent years, swarm this, the advancement in the miniaturization of processors robotictoapproach adding cooperation as theyears, main swarm ingresensor, humidity sensorinare some among those. Added to dient the main stream. Over the this, the advancement the miniaturization of processors adding assystems, theyears, main ingresensor, sensorin are some among those. Added to robotic dient toapproach the used mainto stream. Over the recent recent years, swarm this, thehumidity advancement ininto themore miniaturization of the processors robotics are detectcooperation pressurized chemical are making making the devices devices proximate to reality dient to the main stream. Over the recent swarm this, the advancement the miniaturization of processors robotics are used to detect pressurized systems, chemical are the into more proximate to the reality to the used main Over the recent years, swarm this, the advancement ininto the miniaturization of the processors robotics are used tostream. detect pressurized systems, chemical are making the devices into more proximate to the reality dient plume tracking, light sources etc. Among these, swarm opin imitating the nature and biological species behavior. robotics are to detect pressurized systems, chemical are making the devices more proximate to reality plume tracking, sources etc. these, swarm opin imitating the nature and biological species behavior. robotics arebased usedlight to detect pressurized systems, chemical are making the into proximate toneedle the reality plume tracking, light sources etc. Among Among these, swarm opin imitating thedevices nature andmore biological species behavior. timization robots are most prominent. Their ease in Some of the recent inventions includes painless from plume tracking, light sources etc. Among these, swarm opin imitating the nature and biological species behavior. timization based robots are most prominent. Their ease in Some of the recent inventions includes painless needle from plume tracking, light sources etc. Among these, swarm opin imitating the nature and biological species behavior. timization based robots are most prominent. Their ease in Some of the recent inventions includes painless needle from utilization gives the best flexibility to apply them to wide mosquitoes biting, Harvard dragonfly etc. timization based robots are most prominent. Theirto ease in Some of the biting, recent inventions includes painless needle from utilization gives the best flexibility to apply them wide mosquitoes Harvard dragonfly etc. robots most prominent. Theirto ease in Some of the biting, recent inventions includes painless needle from timization utilization based gives the the bestare flexibility to apply apply them them to wide mosquitoes biting, Harvard dragonfly dragonfly etc. utilization gives best flexibility to wide mosquitoes Harvard etc. utilization gives the best flexibility to apply them to wide mosquitoes biting, Harvard dragonfly etc. 2405-8963 © 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Copyright © 2018 IFAC 544 Copyright 2018 IFAC 544Control. Peer review© under responsibility of International Federation of Automatic Copyright © 2018 IFAC 544 Copyright © 2018 IFAC 544 10.1016/j.ifacol.2018.05.086 Copyright © 2018 IFAC 544

5th International Conference on Advances in Control and Optimization of Dynamical Systems Chakravarthi Jada et al. / IFAC PapersOnLine 51-1 (2018) 512–517 February 18-22, 2018. Hyderabad, India

range of onboard multi-source detection problems. Out of many swarm optimization algorithms, Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Glowworm Swarm Optimization (GSO) are so powerful and bench marked algorithms too. Couple of multi-robotic platforms were constructed and experimented to detect various signal sources. In many swarm inspired robotics, all the robots(agents/ particles) meant to have similar designs and characteristics, and this part is very crucial in the design of swarm robots. The next part of this paper explains the recent designs and embedded parts in various popular algorithms which are relevant to Bflybot, the term introduced in the above chapter. Particle Swarm optimization (PSO) is said to be the basic inspiration to many of the swarm algorithms. In PSO, the particles will move based on cognitive (personal) and social (global) bests which would make all particles to mutual cooperative and correlate among themselves and leads them to global maximum (minimum) level of the function profile. Couceiro et al. (2013) developed eSwarBot for implementing the PSO algorithm. eSwarBot interfaced with Zigbee module for communication purpose, light sensor for sensing the light intensity, ultrasonic sensor for obstacle avoidance, RGB-LEDs to represent different swarms. Recently Jatmiko (2016) and his group, have designed Al-fath bots for localizing the single and multiple odor sources. For building the Al-fath bots, they utilized the compass, sonar ranging sensor, incremental encoder, wireless communication device and a pair of odor sensors to detect the odor intensity. For finding the positions of robots web cameras are used as local GPS and the odor source is liquid ethanol. The liquid ethanol is evaporated at room temperature so that they considered ethanol as a source. The relevant software simulations were performed for odor source localization using multiple odor sources.

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(2009) have proposed GSO. In GSO, the agents carries a luminescence called as luciferin. The agents emit the light where intensity is proportional to luciferin which would encodes the information about the function profile. The mating in the variable decision-domain range of the agents leads the agents to co-locate at all local optima of multimodal function. In the kinbots, each bot is equipped with infrared sensors which are interaction modules provides luminescence emission/detection. Each Kinbot broadcasts the luminescence and also receive the similar in terms of 8-bit data. Then each Kinbot detect their mates and move towards it, this leads to localization towards multiple signal sources. They used this kinbots to detect multiple light, odor and plume sources in the indoor environment. Inspired from the bees foraging based bee colony optimization (BCO), Alers et al. (2014) developed multirobots and implemented a subset of the bee algorithms i.e, the navigation principle with local communication to simulate a food foraging application. To achieve this, they made the thurtlebot, which is equipped with a core i3 CPU, kinect sensor, wheel odometry, gyro and six unique markers. In Thurtlebot, the core i3 CPU for computation and to run the robot operating system framework. Kinect sensor is to detect static obstacle, wheel odometry and gyro are to estimate the robot position. Six unique markers are to enable visual robot to robot detection, with the help of these markers they avoid the robot to robot collisions. Finally, they concluded that all bots start from the initial position and explore the unknown environment randomly by communicating with each other. They found the food location and converged to foraging. After having looked into the various designs and implementations, it is imperative that the agent bots should almost mimic the behavior of the agents in algorithm per se particles in the PSO. So, by keeping this in mind we meticulously designed and constructed Bflybot to the requirement of Bfly in the BMO algorithm. Before explaining the bot design and experiments, we will explain the BMO algorithm briefly in next the chapter. 3. BUTTERFLY MATING OPTIMIZATION 3.1 Butterfly Communication Strategies

Fig. 1.

a. Al-fath bot

b. Thurtle bot

Inspired from the PSO efficacy, based on the ants foraging behavior Dorigo et. al (2004) have proposed ACO as a metaheuristic for combinational optimization problems. In this algorithm, the ants will find the shortest path using the pheromone evolution between the nodes. Many developments took place in multi-robot swarm with ants behaviors. Recently, S-bot is one such design. These bots equipeed with 14 PIC processors for coordinating and accelerometer for rotation. The main control is LINUX based processor. It also consists of omni-directional camera, RGB colour leds, one speaker, four microphones and one degree of freedom flexible arm. With this structure S-bot can be able to form 1D, 2D and 3D rotations by bending its body. The application of this S-bot is semi-automatic space exploration, search and rescue. In the same line of thought but with significant differences, Krishnanand et al. 545

The necessity of communication in butterflies is mainly for mating and defense. There are two main major forms of communication strategies namely patrolling and perching. In patrolling, male butterflies continuously search for female butterflies, based on the UV reflection the male butterflies recognize the female butterflies. In this, the auxiliary traits are color and odor. In perching, the male butterflies are mostly immobile and recognize the female butterflies by their movement. The main attracting parameters are size and movement of female butterflies. Apart from these, butterflies also use defense mechanism to protect themselves from predators. They change their shapes such as a leaf or stick or blend in their background. Recently, in our debut work (2014), we have simulated the movement of butterflies based on the patrolling and perching, and found that if male and female discrimination is removed, then the localization occurs effectively. Then, we (2016) proposed BMO algorithm based on the patrolling strategy to capture all the local optima in various

5th International Conference on Advances in Control and 514 Optimization of Dynamical Systems Chakravarthi Jada et al. / IFAC PapersOnLine 51-1 (2018) 512–517 February 18-22, 2018. Hyderabad, India

mutlimodal search functions. The various phases of BMO algorithm are explained below.

Fig. 2.a, 2.b, 3 shows 3-D plot of three peaks function, UV convergence, emergence of the Bflies at various iterations.

3.2 Description of BMO algorithm

Pseudo code of the BMO algorithm: While

Butterfly mating optimization model is formulated on the base that there is no difference between males and females. Every butterfly has to reflect and absorb UV-light simultaneously. Hence, this algorithm suggests meta butterfly model namely B-fly in search space. This algorithm contains four phases that are explained below. (a) . UV updation Phase: In this phase, each B-fly UV is updated in proportion to its fitness function value at the present location of B-fly according to (1) U Vi = max {0, b1 ∗ U Vi (t − 1) + b2 ∗ f (t)}

{ for each Bfly UV Updation; UV Distribution; Select l-mate; Update position; }

UV is updated at time index ’t’ to give more priority to the present fitness value compared to the past UV. To satisfy this choose the b1 and b2 constants such that 0≤ b1 ≤1 and b2 >1. (b) . UV distribution Phase: In this phase, each B-fly distributes its own UV to the remaining B-flies based on the distance such that the nearest B-fly get more share compared to farthest one. The below approach is followed for this distribution. An ith B-fly having U V i reflects its UV value to the j th B-fly at a distance dij which is given by d−1 ij UVi→j = UVi ∗ −1 dik

Fig. 2. a. 3-Peaks function b. Variation of UV for all Bflies and its convergance to its max values

(2)

k

Where i=1,2,....,N is number of B-flies; j=1,2,...,N j�=i; K=1,2,...,N k�=i; U V i→j is UV absorbed by j th B-fly from ith B-fly; dij is euclidean distance between ith and j th Bfly; dik is euclidean distance between ith and k th B-fly. (c) . Local mate (l -Mate) Selection Phase: The l -mate selection is done in the following manner. Initially an ith B-fly arranges itself all remaining B-flies in the descending order based on the UV values they have reflected toward it. Based on the descending order each Bfly compares its fitness value to the corresponding Bfly fitness. Now, which Bfly reflects more fitness towards it then it choose that Bfly as its local mate. Now, if every B-fly chooses one B-fly in its descending order as its l -mate and move towards it, it leads to some kind of localization and sensing of peaks. But to capture local peaks simultaneously an ith B-fly should also consider the UV of remaining B-flies and choose its l-mate which satisfies the below condition (3) UV(ith Bf ly) < UV(j th Bf ly) Where i=1,2,...,N ; j=1,2,...,N-1 ; j is the index of B-flies in the descending order of ith B-fly. (d) . Movement Phase: Each B-fly move towards its mate as following. xl−mate (t) − xi (t) xi (t + 1) = xi (t) + Bs ∗ (4) ||xl−mate (t) − xi (t)|| where Bs is B-fly step size; xi (t) is position of ith B-fly at time t. Below is the pseudo code for the algorithm and 546

Fig. 3.

Emergence plots at various iterations

4. BFLYBOT: DESIGN AND ARCHITECTURE The Butterfly mating optimization (BMO) metaphor has four phases. So all Bfly’s should follow the mentioned phases at each iteration. To implement BMO algorithm for a real time application, we have designed and constructed a mobile-robot, named as BflyBot. According to the BMO algorithm, the Bflybot should sense, distribute, choose lmate and move towards the l-mate and finally update it’s position. So, we designed the BflyBot carefully to follow all the phases to the requirements of detecting multiple light sources in a pre-defined experimental work space. Next section deals various design and architectural stages in the preparation of the BflyBot for the experiments. 4.1 Work space Arena with signal source Here, we prepared a workspace for implementing the BMO algorithm practically. It was white background floor with compact fluorescent lamp (CFL) of 18 watts is placed in the centre. It has an outer circle for better visualization of BflyBots step size actions. It has an inner circle for indicating the localization/colocation phenomenon. Outer circle diameter is 180 cm and inner circle diameter is 50 cm. We consider the left most down corner as the origin for all experiments. The light source placed in the (90,95) position of the work space. Because of this


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placement the CFL emanates its intensity equally to all directions of the work space. This placement of the light source is deliberately chosen for the sake of preliminary experiments.

Fig. 6. a. Linearized and b. Non-linearized behaviour

Fig. 4.

Experimental work-space

4.2 Determination of light intensity To imitate UV updation phase in BMO algorithm, we require intensity of BflyBot at the present location. To achieve this, we used light dependent resistor (LDR) sensor for finding the light intensity values. Based upon the light intensity values, LDR changes it’s resistance. So, we will get the different analog values through analog pins. These LDRs have three pins. Two pins are ground and VCC , and the third pin is an analog pin. These pins are connected to the processor as shown in Fig. 5. On the Bflybot, LDR’s are placed exactly facing opposite to each other (see fig. 15) for the sake of optimal sensing. As, an experimental check, the LDR’s equipped bot has moved linearly and non-linearly on the work space as shown in Fig. 6. The corresponding LDR’s variations are shown in Fig. 7. Also, to avoid near-accurate values due to only two LDR’s, we moved the bot to one revolution (360o ) and capture series of values and considered the maximum value for further step. Fig. 7.a shows the corresponding LDR’s variations and Fig. 7.b show the Bflybot placed at three proximity locations of varying lamp intensities. We can observe that proximity-3 case is superior and 900 would be the LDR value at that placement of the bot.

Fig. 5. LDR connections

Fig. 7. a.Intensity variations b. Proximities of light sensor initial position as the origin, we moved the mouse in various directions to get the sense of all quadrants. Fig. 9.a and Fig. 9.b shows the traversed 2-D plots of the mouse movement using the coordinates.

Fig. 8. Mouse based odometry

Fig. 9. a. Coordiantes movements plotted in x-y plane b. Dynamic behavior of mouse

4.3 Methodology for calibration of coordinate system 4.4 Mesh communication Architecture In BMO algorithm, distributing UV based upon the distances is crucial and it required the coordinates of all Bfly’s in each time step. Here, we have used PS2 mouse pointer movement odometry protocol to find the coordinates. This design has shown in Fig. 8. By taking 547

According to description of BMO algorithm to proceed the l-mate selection of every BflyBot, need to communicate with each other and transfer their coordinates and intensity values to remaining bots. To achieve this RF zigbee

5th International Conference on Advances in Control and 516 Optimization of Dynamical Systems Chakravarthi Jada et al. / IFAC PapersOnLine 51-1 (2018) 512–517 February 18-22, 2018. Hyderabad, India

module (s2c model) is used. It is a low power wireless RF module, operates 3.3 volts as VCC . The zigbee module has 1.2 km range with 250 kbit/sec data rate.

Fig. 12. Accelerometer connections

Fig. 10. Zigbee connections Fig. 10 shows the the connection diagram and Zigbee module along with Arduino UNO (ATmega328, Vcc = 5 volt, 16 MHz clock speed) as the central processing unit for each Bflybot. For mesh communication, zigbee modules were configured using XCTU software tool in AT mode. Fig. 11 shows the view of mesh-communication among 4 zigbees. Table 1 shows the intensity values, coordinates and their choosen l-mate along with updated position.

Fig. 13. L-mate locating strategies a. Acute b. Obtuse 4.6 Stepwise Movement towards local mate The final phase in BMO algorithm is the movement phase. In this, the BflyBot should move towards it’s local mate (l-mate). For this movement, 12 volt DC motors were used. A two channel motor driver (L293D) was used as an interface to supply 12 volts to the motors and drive them. This is PWM enabled motor shield, so speed of the motors can be conrolled. There is no onboard power supply for this motor driver (L293D), so this should be enabled by using Arduino. It has continuous output current 600mA and peak output current 1.2A per channel.

Fig. 11. Communication between zigbees

Here, by fixing the small step size of each BflyBot there is more probability that all the mobile robots converges nearer to the source position. Fig. 14 shows the motor driver circuit connections. Finally after combining all the above designs the complete block diagram had shown in Fig. 15 and the full assembled Bflybot has shown in Fig 16 and Fig 17 shows the set of four Bflybots made ready for the experiments.

Table 1. Sample mesh communication experiment Bot A B C D

Intensity 460 575 179 237

L-mate B No mate A B

Gyro angle 134.24 0 184.39 117.76

Updated (1.42, (1.20, (2.12, (2.40,

position 1.22) 1.00) 1.67) 1.63)

4.5 Architecture for Angular Rotation According to BMO algorithm BflyBot need to rotate required angle to make a movement towards it’s local mate. For this, we used the Accelerometer (MPU 6050) to measure the angle of rotation with its initial position. Here, the accelerometer is used for two purposes. First one is to rotate 360o angle in present position to take the maximum value of intensity using the light sensors (see sec 4.2). Second is to rotate the inclination angle in the direction of it’s l-mate. Fig. 13 shows two of possible rotations (acute and obtuse). Fig .12 shows the corresponding connection diagram. 548

Fig. 14. Motor Driver Connections 5. ONGOING EXPERIMENTS ON THE BFLYBOT SWARM The BMO algorithm has interfaced into the Bflybot swarm in the Fig. 17. The experiments are conducted on


Fig. 15.

Block diagram of BflyBot circuitry.

517

Fig. 19. Variations in intensity of all bots towards the light source the experiments being conducted to place multiple sources with different colors and analyze the working nature of the bots. As a future work, we planned to replace a static light source with a moving source,which could be an ultimate epitome for majority of the practical applications. 6. CONCLUSIONS This paper presented the various phases in the recently developed Butterfly Mating Optimization (BMO) algorithm along with the simulation results. Then presented the Bflybot design and constructional details, which took the role of Bfly in the BMO algorithm. In each individual task, design presented along with basic testing and corresponding results. Experimental results are shown for the detection of Bflybot swarm to a light source. Finally, the present ongoing and prospective possible experiments planned are discussed briefly.

Fig. 16. BflyBot with various components

REFERENCES Fig. 17. Multi BflyBots for source localization

Fig. 18. Bots locations at various time-steps the workspace shown in Fig. 4 to check the convergence of bots with a step size of 10cm. The experiments show the validation of all the internal architecture of the Bflybots. Fig. 18 shows the Bflybots convergence at various timings and finally co-located to the inner circle. Fig. 19 shows the plot for each Bflybot with their LDR variation towards the source. At the final time all the bots LDR values are approximately same. Currently, the experiments are going on to check the convergence efficacy of the swarm for variations in the step size and initial placements. Apart from these variations, 549

S. Alers. Biologically inspired multi-robot foraging. Sjriek, Gerhard Weiss. & Co., Chicago, 2014.pages 1683–1684. Ch. Sowmya. Butterfly Communication Strategies: a prospect for soft-computing techniques, Jada, Vadathya. & Co., 2014. pages 424–431. J. Chakravarthi. Butterfly Mating Optimization, Vadathya, Shaik. & Co., 2016. pages 3–15. M. Couceiro. A PSO multi-robot exploration approach over unreliable MANETs, Rocha, Ferreira. & Co., 2013. pages 1221–1234. W. Jatmiko. PSO algorithm for single and multiple odor sources localization problems: Progess and Challenge, Jovan, Dhiemas. & Co., 2016. pages 1431–1478. K.N Krishnanand. A glowworm swarm optimization based multi-robot system for signal source localization, Ghose. & Co., 2009. pages 49–68. K.N Krishnanand. Glowworm swarm optimisation: a new method for optimising multi-modal functions, Ghose. & Co., 2009. pages 93–119. M. Dorigo research director. Ant Colony Optimization. The MIT Press Cambridge, Massachusetts London, England, june 2004. http://www.swarm-bots.org/. http://www.ctan.org/pkg/booktabs. http://www.digi.com.