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  • International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – INTERNATIONAL JOURNAL OF ELECTRONICS AND 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 2, February (2014), pp. 57-68 © IAEME COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET) ISSN 0976 – 6464(Print) ISSN 0976 – 6472(Online) Volume 5, Issue 2, February (2014), pp. 57-68 © IAEME: www.iaeme.com/ijecet.asp Journal Impact Factor (2014): 3.7215 (Calculated by GISI) www.jifactor.com IJECET ©IAEME BIOMIMETIC ROBOTS: BASED ON ANTS Dr. Ashwin Patani1 Indus Institute of Technology & Engineering, Ahmedabad, Gujarat Prof. Miloni Ganatra2 Indus Institute of Technology & Engineering, Ahmedabad, Gujarat ABSTRACT Nature is considered as the main source of inspiration and guidance. As the title name suggests, the robot which we are intended to make is inspired from the nature’s creation: Ants, and is known as biomimetic robot. A biomimetic robot is an autonomous machine whose structures and functions are inspired from biological systems or processes. Here we are supposed to make robots by studying the behavior of ants. The features to be included are going to be mimicked by studying the way ants sense their food, the way they calculate the shortest path and communicate with each other. The robot would therefore perform the functions like an ant, sensing the target (like food by ants), calculate and follow the shortest path and communicate with other robots to follow the same path. Ants exhibit a variety of behaviors which result in discovery and exploitation of food sources around the nest. These behaviors can be adapted to robot teams which are performing exploratory missions on uncharted landscapes. Also, aspects of mobile robot coordination and control can be adapted to enhance the functionality of the team. This project proposes a structure for a team of antemulating robots, a strategy for field operation, and behaviors which can be incorporated into the mobile agents on a foraging or exploratory mission. INTRODUCTION The community of human beings has always depended on nature for its own needs; be it for food, shelter or any other possible needs. In today’s’ world we might have advanced in terms of technology, but we still depend on nature for inspiration. We make an effort to learn from the natures’ most interesting, tiny creature, ‘Ant’. An important part of nature is the animals that have evolved to occupy almost any environment in which we might want to operate a robot, save outer space. In principle, a set of behavioral acts that a cockroach or a lobster might use to locate food is an ideal blueprint for how a 57
  • International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 2, February (2014), pp. 57-68 © IAEME robot might find agents of harm such as mines or biological agents. Thus, the ‘behavioral set’ of model animals provides a blueprint of how robots, based on animal systems, might operate in a variety of environments and perform tasks which are significant in context of remote sensing inspection of sites and areas dangerous/ out of reach of humans, or other defense, space exploration applications. A Biomimetic approach, or neurotechnology, confers the performance advantages of animal systems on this relatively new type of robot. Biomimetic robots are, in principle, relatively small, agile and cheaper, relying on electronic nervous systems, sensors and novel actuators. Most importantly, they can take advantage of capabilities proven in animals for dealing with real-world environments such as desert, the woods, the sea bottom or the sky.Biomimetic robotics is attracting the interest of a growing number of robotics researchers worldwide. The advancements of robotics technologies have recently led to an increased interest towards Biomimetic robotics, also in the scientific fields related to biology and to the study of living organisms. Biomimetic robotics can represent today a powerful tool for experimental investigation of the sophisticated mechanisms and amazing sensory-motor performance that many living organisms show [5]. By taking inspiration from the female dominated specie of the nature, ants and developing a colony of robots that perform functions similar to the ants. Amalgamating (to combine) the various ant behaviors, a usable model of a team of robots is made. Ant colonies are distributed control systems which use a combination of individual behaviors to create a complex and intricate team structure. However this ‘problem definition’ does not propose a fully distributed model. The concept of a team leader (forager) or a central overseer and 2 followers are brought into play. This robot (team leader) can fulfill the requirements of housing the mobile robots, creating and relaying a global map of the territory to the mobile ‘ants’ from local information. It can also make macro decisions about the strategy to be followed in a sector of the search area. On making the biomimicked robots based on ants we try to accomplish autonomous communication between 3 robots which will behave like ants and will work as a team to find the target etc. The robots will follow the shortest path it has calculated and find the target. Thereafter communicate this path (to the target) to the other robots which will follow the same path and reach the destination. Here, the robots will communicate autonomously with each other with a provision for human interference as required. BIOMIMETIC BEHAVIOR OF AN ANT Each ant colony has at least one or more queens. The queen ant can intentionally lay eggs destined to develop into a specific caste- males, females and workers. Worker ants are supposed to take care of larvae and collect food for survival. Ants always work in groups in a discipline. They randomly search for food sources in an area and if any ant succeeds, other ants are informed about the food source’s location and path and then all ants work in a group to reduce the length of the path from food source to the nest. When workers find pieces of solid food, they carry them back to the nest. On the other hand, when they find sweet liquid food such as honeydew, they store the liquid in the crop in their abdomen and walk back to the nest. When they reach the nest, they feed the liquid food in drops directly from their mouth to the mouth of other members of the nest [5]. The worker ants can be divided in two groups i.e. forager and follower. The ants searching for food randomly and marking the pheromone trails are known as forager as they lead entire colony of worker ants to the food source. The remaining group of ants following the trail scent is known as follower ants. All the ants work in unity to find the shortest path connecting the food source and the nest. As explained earlier, ants use their pair of antennae to detect the strongest pheromone scent trail. Ants use the soil surface to leave pheromone trails that can be followed by other ants. A forager that finds food marks a trail on the way back to the colony; this trail is followed by other ants, these ants 58
  • International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 2, February (2014), pp. 57-68 © IAEME then reinforce the trail when they head back with food to the colony. When the food source is exhausted, no new trails are marked by returning ants and the scent slowly dissipates. This behavior helps ants deal with changes in their environment. For instance, when an established path to a food source is blocked by an obstacle, the foragers leave the path to explore new routes. If an ant is successful, it leaves a new trail marking the shortest route on its return. Successful trails are followed by more ants, reinforcing better routes and gradually finding the best path. Ants use pheromones for more than just making trails. A crushed ant emits an alarm pheromone that sends nearby ants into attack frenzy and attracts more ants from further away. Several ant species even use "propaganda pheromones" to confuse enemy ants and make them fight among themselves. Ants use the phenomenon of pheromone deposition to mark their trail from the nest to food and back. This pheromone has a time variant strength after deposition. In certain ant species, the strength of the pheromone is proportional to the quality of the food source discovered by them. Once food has been discovered, mass recruitment is used in some species as a tool for gathering foragers to exploit a particular food source. Mass recruitment is a direct recruitment mechanism. Another mechanism for recruitment is indirect, known as all elomimetic communication. Here, the pheromone trails deposited by ants are followed by other ants in random forays through the search area. They leave their own trails and strengthen the pheromone trail such that the probability of the trails being followed by other unsuccessful ants, and a shortest path to food being traced is high. Ants retire to their nest if their foraging trip is unsuccessful after some time. BIOMIMETIC ANT BASED ROBOT The design of the robotic Ants was inspired by nature, hence the moniker, "Natural Design". This is an idea with two parts, the first being a design methodology, while the second is to base our expectations on observations from nature. The "hardware" and "software" of natural ants are inseparable; they have evolved together for hundreds of millions of years. In order to build a robot like this, the hardware must be designed with the software in mind, and the software must be written with the hardware in mind. When the hardware is designed for one purpose, it can provide a substantial amount of sensory filtering. For example, the food sensors (RGB sensors) do nothing else but detect food (blue boxes). Therefore, the software does not need to perform extensive processing on the data from these sensors, either they detect food, or they do not. This is similar to how many insect senses operate. There is a very select stimulus that excites a purpose-built sensor, eliminating the need for further processing. An example for the opposite case would be to use a camera to find food. The output of the camera has nothing to do with whether or not food is there, it reports the intensity and luminance of incident light. The software then has to sort through all this data to figure out if there is food present or not. The other idea is that nature is not perfect. When attempting to emulate a natural system, what you expect might not always be what a correctly functioning system will produce. The artificial standards that researchers judge their robots on must be tempered with the ultimate standard, nature. For example, the Ants have no way of ensuring that they drive in a straight line. Real ants do not walk in straight lines; they are constantly bumping in to objects and using information from their sensors to change their course. Communications are not perfect, sensations are not perfect, and the environment is not perfect, the list goes on and on. In addition to not expecting the robots to perform perfectly, they are not even programmed to achieve such an unrealistic goal. When a real ant finds food, she then goes back to the nest to report her find to her nest mates. Her way back is far from the straight line that you might assume. She makes errors, gets lost, finds the trail again, turns around, etc. She will eventually get home, but over a very indirect course. Using that as an example, it would be silly to expect a robot to be able to travel directly home after finding food. If nature does not worry about 59
  • International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 2, February (2014), pp. 57-68 © IAEME problems like this, perhaps there are design solutions where the engineer need not worry about it either. Biomimetic ant based robot colony is a set of three autonomous machines which has functions similar to the ants. In other words, the functions that the robot performs are analogous to the ant specie. In this project, the robot performs mainly five functions, they are, foraging, exploiting, recruiting, following and retirement. Foraging in an ant means to randomly search for food. Here foods for the robotic ants are blue boxes. The robots from their nest (the home) will search for the blue boxes randomly. In industrial applications this function can work to find a fault in some area the way randomly the ant searches for its food. There can be various such applications for this function of the robot. Exploiting is the second function performed by the robot. In an ant, recruiting means collecting the food samples as required by their colony. In the robotic ant, this function means that the robot when finds the blue boxes it collects the blue boxes. Recruiting in terms of the ant is returning to the nest with food and marking the path while returning back to the nest, so that the other ants know the path to the food and can go there to collect the remaining food. The robot ants will bring back the blue boxes to the nest marking the path while returning to the nest. Following in an ant means that the ants will follow the path of the forager, i.e. use the forager’s trail for finding food and taking it back to the nest. In the artificial colony the robotic ants will use the data of the forager robot ant and will follow that path. This data will be transferred to the other robot ants which will follow the same path to reach to the blue boxes and bring them to their nest. Retirement of an ant is when it does not find any food it comes back to its nest. In the artificial ant colony this will happen when the robot is not able to find any blue boxes in a certain amount of time it will return back to its nest. In addition to these five main functions, the robot ant will also have inbuilt functions to avoid the obstacles in its path of foraging or finding of food (blue boxes); this is done by the use of bump sensors. FLOW CHARTS FOR ANT BASED ROBOT An idea of developing a robot that is inspired from the nature. We worked on searching for the nature’s creation that can help human being to ease its life. We ended our search with the selection of Ants as our inspiration. Entirely dedicated to the study on ants and the features and tasks that we need to be done by the robots inspired from ants. We had a clear idea about what ants are, what they do and how they do. we require to develop a community of automated robots. We started with the selection of µC and sensors. Following block diagram shows the tasks to be performed by the robots. This simple block diagram is with respect to a forager robot. Figure 1: Basic working block diagram 60
  • International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 2, February (2014), pp. 57-68 © IAEME Each block consist a set of tasks to be followed. The flow chart of the entire process is as shown below. Start Random Search FCounter=5 min NO Food Found? NO, F-counter=0 End Yes Enable PCounter =1 min Set Forager bit=1 Disable FCounter Send Food bit=’1’ to other bot Pick up the food NO NO, F-counter=0 Send help bit to other bot Disable FCounter of all the bots (i.e. no retiremen t) Food Picked? Wait for other bot Enable Nest Search Collecti vely pick the food up Nest Found? Figure 2: Flow Chart 61 Disabl e PCount er End
  • International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 2, February (2014), pp. 57-68 © IAEME Figure 3: Random Search As shown in the figure 3, the very first thing a robot should do when started is the random search. The Robot will move randomly in space and it is programmed in such a manner as it does not roam around taking left and right turns continuously. It should not rotate 360 degrees while moving randomly. There is a specific amount of time given to robot to find its food. If it fails to do that in the prescribed time, it will stop searching further for food and will search for its nest. This phase of the robot is called ‘Retirement’. Figure 4: Food Detection 62
  • International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 2, February (2014), pp. 57-68 © IAEME Figure 5: Foraging food to the nest If any Robot succeeds in finding the food/food source, there are some tasks to be followed by it. The Robot that finds the food first is known as ‘Forager’. First, it will inform other Robots (‘Followers’) about the food. Simultaneously it will try to pick up the food and take it to the nest. If it cannot pick it up, it will ask for help from other robots. After picking up the food (either individually or in group), the next task to be done is now search for the nest. And exploit the entire food source exactly the same way as described above. These are the very basic tasks which an ant colony does. Here, Ants give us an inspiration on how the swarm behavior of it can be used to make tasks to be done automatically by robots’ mutual communication. The food source can be replaced by any sensor according to the application requirements. These kinds of micro robots if implemented successfully can even be used instead of sniffer dogs in anti-terrorist activities to search and prevent hazardous situations. REQUIRE COMPONENTS AND TESTING When one develops an autonomous machine of any kind, a µC or a microprocessor is bound to be a part of it. We needed to select a controller that gives the ease in programming and has the number of interrupts required for the robot’s correct functioning. On searching for the controller, we finally chose Atmel’s AVR series AT8535 controller. The Motor Driver (L293D), Wireless communication device (CC2500), The MAX232 is an integrated circuit that converts signals from an RS-232 serial port to signals suitable for use in TTL compatible digital logic circuits. The MAX232 is a dual driver/receiver and typically converts the RX, TX, CTS and RTS signals. Programmer’s notepad (version: v2.0.8.718-basie) is used for writing programs for µC. It also generates hex files to load it in the µC. Simulation with ATMEL AVRStudio or HIDBootFlash v.1.0 is used to load the hex file generated in programmer’s notepad into the µC. For Obstacle Detection with IR sensor the basic concept of IR obstacle detection is to transmit the IR signal in a direction and a signal is received at the IRR when the IR radiation bounces back from a surface of the object. The main ‘advantage’ of using an IR transceiver is that it can work in dark as well as light and cover more range of area for obstacle detection. The color sensor is also an LDR used in conjunction with an ultra bright LED that is of the color which has to be detected. 63
  • International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 2, February (2014), pp. 57-68 © IAEME Testing for the Random Search: - require building robots which imitates ants’ behavior. A very basic task that an ant does is the random search for the food. The locomotion of a real ant is done by its six legs, whereas we are using 4 wheels attached with 2 DC motors (2 rear wheels with 2 DC motors and 2 front wheels are used for balancing of the robot). Hence the entire motion of the robot is controlled with 2 DC motors. This control is done using a motor driver IC L293D. To make the robot move randomly, programming is done in such a manner so as to control the motion of both the motors randomly. A random variable is used, which generates random values in a register. These set of values are given the appropriate values for robot’s motion, i.e. forward, backward, right turn, left turn etc. Hence according to the random values generated, robot will move in the specified direction. To avoid undesirable rotations of the robot in one direction, the programming is done such that the forward motion has more probability than any other motion. The speed of the motors can be controlled by generating appropriate delay. Testing for the Obstacle Detection: - We can observe that when interrupted by an obstacle, a real ant will try to avoid the obstacle that comes in its path and will find a new obstacle free path. The same obstacle avoidance is implemented in the ant robot for improved search for food. Ants’ use their antennae to detect obstacles, whereas our ant robot will use their IR antenna to detect obstacle and whenever its IR antenna gives positive o/p, controller will execute a program to avoid the obstacle and continue its search for food. This IR antenna is nothing but a pair of IRT and IRR. This IR transceiver pair is made using an IRT, IRR (TSOP1738) and a few passive components. Mentioned IRR is designed to receive only IR signal transmitted in 38 kHz frequency. Most remote control appliances use a near infrared diode to emit a beam of light that reaches the device to be controlled. This emission is done using a carrier frequency of 38 kHz. Same carrier frequency is used by the IRR TSOP1738. The Application circuit provided in the datasheet was mounted on bread-board with little modification and was tested by transmitting the IR wave of 38 kHz from a TV remote control and it worked as expected. Figure 6: IR Transceiver To generate pulse train of IR signal on a simple IR LED, timer is required. The pulse train was provided from the in-built timer of the controller. To control rang and directivity of the IR signal a NPN switching transistor (2N2222) is used. After successfully generating IR signal using µC, both the circuits were combined to make a full IR transceiver. 64
  • International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 2, February (2014), pp. 57-68 © IAEME Testing for the Color Sensor: - Ants have their natural sensor to detect their food. Most of the ants digest sweet foods only hence whenever their sensors tell them about presence of a sweet food while searching randomly; they will be attracted towards the sweet food. For the ant robot, food is nothing but tiny boxes of blue color. Hence, they need to detect blue color boxes as their food. For the purpose of blue color detection, a circuit is made using photo-resistor (LDR) and an NPN transistor. Photo-resistor is a resistor whose resistor decreases with increasing incident light intensity. Using this concept, with modifications in LDR’s o/p, it is possible to make a simple circuit to detect color. It will work like a human eye to detect different shades of colors. But we require only single color (blue) to be detected; it is easy to make one using photo-resistor, a transistor, an ultra bright LED (of color to be detected) and resistors. The principle behind the circuit is when we shine a blue light on an object; it will reflect much more light than the objects other than blue color. Hence, the sensor will find blue color brightest than any other color. Sufficient darkness should be provided to the LDR for desired output. Following images explains the working of the color sensor circuit tested to detect blue color. Figure 7: Green color Figure 8: Green color is not detected As shown in above images, a green color when comes over LDR, does not give output, i.e. it is not detecting the green color. Similarly, the following images show that when blue color is passed over the LDR, it glows the red LED. This shows that the circuit is detecting only blue color and not any other colors. Figure 9: Blue color Figure 10: Blue color is not present (Ultra bright blue LED is glowing) 65
  • International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 2, February (2014), pp. 57-68 © IAEME Figure 11: Detecting Blue color Testing for the Light Follower: - The last but not the least task of a real ant is to pick their food and head over to the nest. The path to the nest is made as short as possible by mutual communication. We have used a different concept for nest finding, which is much faster than what real ants do. We need to make the nest a source of light source and the robot will try to find the place from where the light is coming and follow it. Light dependent resistor (LDR) is again a best choice as this is light detection. As explained earlier, the LDR will search for while light as no comparison is done as in previous case. Here, two LDR’s are used as light following is directly related to motion control. Two LDR’s for light following will work as two eyes of the robot. Left and Right LDR’s will control both the left and right motors respectively. Motors will drive the robot to follow the path having brightest light. Testing of simple light detection on bread board is shown in following figure12. Figure 12: Light ON Figure 12: Light OFF REAL ANT Vs ROBOT ANT The design of the robotic Ants was inspired by nature, hence the moniker, "Natural Design". This is an idea with two parts, the first being a design methodology, while the second is to base our expectations on observations from nature. The "hardware" and "software" of natural ants are inseparable; they have evolved together for hundreds of millions of years. In order to build a robot like this, the hardware must be designed with the software in mind, and the software must be written with the hardware in mind. When the hardware is designed for one purpose, it can provide a substantial amount of sensory filtering. For example, the food sensors (rgb sensors) do nothing else but detect food (blue boxes). Therefore, the software does not need to perform extensive processing 66
  • International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 2, February (2014), pp. 57-68 © IAEME on the data from these sensors, either they detect food, or they do not. This is similar to how many insect senses operate. There is a very select stimulus that excites a purpose-built sensor, eliminating the need for further processing. An example for the opposite case would be to use a camera to find food. The output of the camera has nothing to do with whether or not food is there, it reports the intensity and luminance of incident light. The software then has to sort through all this data to figure out if there is food present or not. Biomimetic ant based robot colony is a set of three autonomous machines which has functions similar to the ants. In other words, the functions that the robot performs are analogous to the ant specie. In this project, the robot performs mainly five functions, they are, foraging, exploiting, recruiting, following and retirement [8] [9]. The following table-1 shows a comparison between a real ant and the robotic ant. Table-1 Function Real Ant Locomotion Communication Legs Touch/ Secretions Food Finding Food Navigation Robot Motors CC2500 transceiver Sweet food generally Artificial food Chemical Senses, Food Sensors (Color Sensors) Sense Hairs, Eyes Eyes, Antenna, Leg Senses, LDR, IR Sensor (obstacle Pheromone trail detection), Random searching CONCLUSION Swarm robotics is relatively new approach for the coordination of multiple robots based on local interactions using simple individual robotic nodes. Originally inspired by the intriguing capabilities of natural swarms such as termites, wasps and ants which are capable of doing complex tasks such as nest building, brood sorting or routing for optimal foraging. Some of the potential areas of application of our project are discussed here. Very strong application of our project which can be used in Mars exploration or exploration of any asteroid or space bodies, Border Patrol and Turbine Inspection. REFERENCES [1] [2] [3] E. Bonabeau, M. Dorigo, and G. Theraulaz. Swarm Intelligence: From Natural to Artificial System. Oxford University Press, New York, 1999. S. Camazine, J. Deneubourg, N. Franks, J. Sneyd, E. Bonabeau, and G. Theraulaz, SelfOrganisation in Biological Systems. Princeton University Press, 2001. W. H. Lee and A. C. Sanderson, “Dynamic Rolling, Locomotion planning, and Control of an Icosahedral Modular Robot,” in Proceedings of the 2000 IEEE/RSJ International Conference on Intelligent Robot and Systems (IROS 2000), (Kagawa University, Takamatsu, Japan), pp. 2178–2183, 31st October–5th November 2000. 67
  • International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 2, February (2014), pp. 57-68 © IAEME [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] Palacin,J. Univ.deLleida,Spain Salse,J.A. ; Valganon,I. ; Clua, X. Building a mobile robot for a floor-cleaning operation in domestic environments Biomimicry ants [Online] available: http://www.biomimicry.net Jevtić, A.; Gazi, P.; Andina, D.; Jamshidi, M. Building a swarm of robotic bees, World Automation Congress (WAC), 2010. Baoding Zhang; Shulan Gao. The study of ZigBee technology's application in swarm robotics system, Artificial Intelligence, 2nd International Conference on Management Science and Electronic Commerce (AIMSEC), 2011. Crisp project: An improved particle swarm algorithm for vehicle routing (IEEE 2003) Tan Ying; Effects of algorithmic parameters on swarm robotic search, Information and Automation (ICIA), 2010 IEEE International Conference on. Chao Zhou, Min Tan, Nong Gu, Zhiqiang Cao, Shuo Wang and Long Wang The Design and Implementation of a Biomimetic Robot Fish, International journal of advanced robotic systems 2008. Lovendra Solanki, Dinesh Soni, S B Dandin and J L Raheja, “Feature-Based Head Pose Estimation for Controlling Movement of Robot Motion in Real Time”, International Journal of Electronics and Communication Engineering & Technology (IJECET), Volume 4, Issue 7, 2013, pp. 8 - 19, ISSN Print: 0976- 6464, ISSN Online: 0976 –6472. Kabeer Mohammed and Dr.Bhaskara Reddy, “Optimized Solution for Image Processing Through Mobile Robots Working as a Team with Designated Team Members and Team Leader”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 3, 2013, pp. 140 - 148, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. Sreekanth Reddy Kallem, “Artificial Intelligence in the Movement of Mobile Agent (Robotic)”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 6, 2013, pp. 394 - 402, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. 68