Cooperative Avoidance Behaviorfor Swarm Robots

TELKOMNIKA ISSN: 1693-6930  Cooperative Avoidance Control based Interval Fuzzy Kohonen Networks .... Siti Nurmaini 805 Table 2.Comparison of FKNand IFKN algorithm No. Description FKN IFKN 1 Environmental pattern use 21 15 2 Software resources byte 3794 3376 3 Time of computation ms 42.33 54.60 4 Pattern recognition 85.71 86.66 5 Number of data ouputprocess 939 975 From the experiment, the proposed IFKN algorithm successfully to recognize the environmental patterns, and produce pattern recognition percentage is 86.66, while FKN algorithm produce 85.71. The results show that cooperative learning behavior by using IFKN algorithm can achieve fast behavioral adaptations compare to FKN algorithm, even if the single robot have no intrinsic behavioral intelligence to begin with. From the single mobile robot experiments, thegeneral comparison ofFKN algorithmandIFKN algorithm can beseen in Table 2. Based on Table 2, it can be concluded that single robot uses FKN algorithm has the advantage in terms of processing speed. While the single robots using IFKN algorithm has the advantage in terms of pattern recognition accuracy and small resources.

4.2. Cooperative Avoidance Behaviorfor Swarm Robots

The experimental set-up utilizes 5 sensors from the right side to the left side of the mobile robot to determine their distance to the obstacle. Swarm robots mustrecognize their environment in order to perform it’s tasks in the dynamic world [31]. Therefore the environmental recognition problem must be addressed in order to have robust performance of the swarm controller. The problem of identifying the environmental pattern is often hard, due to the sensor readings which are usually uninformative and produce large amounts of noise [30]. In this work IFKN algorithm is utilized in order to recognize the environmental pattern and identify the near by swarm robots. Figure 7a-d showthe starting positions of the 5 smaller robots with different positions of square-shape obstacles. It is observed that the swarm robots have managed to navigate successfully. a No obstacle bTwo obstacles cThree obstacles d Four obstacles Figure 7. Five 5 swarm robots in real environment  ISSN: 1693-6930 TELKOMNIKA Vol. 12, No. 4, December 2014: 795 – 810 806 The main difficulty when trying to achieve a goal, collision avoidance and flocking is that prioritizing the sometimes contradictory requirements, therefore this issue is explored. However, collisions between two or more robots must be prohibited. This can be treated as a dynamic situation of the obstacle avoidance problem. The highest priority is given to collision avoidance, because if the collision safety than the robots will reach the goal. Finally, the swarm robots should stay together in a flock while moving. For flocking behavior structure composed is proposed corresponding which consist of three components: cohesion-separation, velocity matching, and obstacle avoidance behaviors. As demonstrated in Figure 8a and b, two mobile robots approach each other. Once the relative distance is within the threshold, both robots will make decisions to avoid collisions based on the obstacle avoidance use IFKN algorithm addressed above. The real environment situations are tested individually to see how quickly the robots would learn the appropriate behavior. Measurements are taken of robot’s performance every 30 ms and produce smooth paths during flocking. The results produce safe paths in the tracking the reference path and avoiding obstacles in the unknownnvironment. As demonstrated by the graph in Figure 8 a, the robots became successful in surviving the obstacle they encountered. Figure8 bshows how, over time, by using the IFKN algorithm mobile robot is able to “learn” to make the best actions to overcome a ny given obstacle.In this work, swarm robots communicate using an X-bee interface. a No obstacle b Two obstacles Figure 8. Cooperative avoidance with flocking behavior TELKOMNIKA ISSN: 1693-6930  Cooperative Avoidance Control based Interval Fuzzy Kohonen Networks .... Siti Nurmaini 807 Figsure 9a-c shows the initial random mobile robot movement. IFKN algorithm is used in cooperative obstacle avoidance strategy to navigate the robots around square shape obstacles. The robots’ paths are traced by a line with each line representing one iteration’s position. The nearest corner of the obstacle is decided based on the calculated Euclidean distance of the pattern’s current position relative to each obstacle corner whereby the corner with the smallest value or distance is chosen.The results show, there are a small collisions b etween the robots, also between the robots and the obstacles. It is clear from Figure 9 a–c, the two robots move through the search space, gaining one new position for every iteration, a conditional statement checks to see if the next position of the robots will fall within the boundaries of the obstacle. When two robots collide with the obstacle, they will not converge to the target. a No obstacle b Two obstacles  ISSN: 1693-6930 TELKOMNIKA Vol. 12, No. 4, December 2014: 795 – 810 808 c Three obstacles Figure 9. Collision avoidance results with 2 robots The navigation coordination of the swarm robots is addressed through a complex environment without hitting the obstacles and being trapped in dead-end passageways. This work is motivated by the observation that swarm robots exhibit emergent group behavior a s shown in Figure 10 a –d. IFKN algorithm is used in cooverative obstacle avoidance strategy to navigate swarm robots around square shape obstacles. a No obstacle b One obstacle cTwo obstacles d Three obstacles Figure 10 . Cooperative avoidance results TELKOMNIKA ISSN: 1693-6930  Cooperative Avoidance Control based Interval Fuzzy Kohonen Networks .... Siti Nurmaini 809 The swarm robots’ paths are traced by a dotted line with each dot representing one iteration’s position. The nearest corner of the obstacle is decided based on the calculated Euclidean distance of the pattern’s current position relative to each obstacle corner whereby the corner with the smallest value or distance is chosen. For instance, when two robots face the obstacles, they split into a plurality of smaller groups to avoid collision and then merge into a single group after passing around the obstacle. It is also worth noting that when this group of robots facing a dead end they can even get out of the area. Every moment the swarm robots communicate with their neighbors and all individuals adjust their movement strategy by communicating to exchange position and speed with their neighbors. 5. Conclusion and Future Works In this paper the proposed IFKN algorithm has been exploited in order to control the cooperative avoidance behaviour of swarm robots formations in presence of obstacles in the environment. Each robotrecognize the environment through sensing device and decide the cooperative behavior oneself through communication.The proposed algorithm have the advantages in system robustness, scalability, and individualsimplicity.This strategy can be easily modified in order to allow the robots to successfully consider the risks of the environment and avoid possible obstacles.While still continuing on an efficient trajectory leading towards total swarm convergence to the target. Provided that all the robots are able to keep their desired relative positions to their respective leaders, avoidance of collision is guaranteed. The results show that swarm robots based on proposedIFKN algorithm have the ability to perform cooperativebehavior, produces minimum collision and capable to navigate around obstacles. Proposed IFKN algorithm has 86.66 of pattern recogition level compare to FKN only 85.71.This algorithm produce only 3376 bytes resource of code, due to small pattern are used. In the future, the performance of this swarm robots in both the absence and presence of obstacles are analyzed. Experimental results demonstrate the effectiveness and reliability of the proposed control strategy. There exist various potential future research directions. A major one is real-time experimentation of the proposed control strategy, whose results may lead to some modifications and enhancements in the proposed scheme. Acknowledgments Authorsthank to Higher Education General Director DIKTI, the Ministry of National Education Department Indonesia and Sriwijaya University under International Research Collaboration for their financial support in Competitive Grants Project. Our earnest gratitude also goes to all researchers in Robotic and Control Laboratory, Computer Engineering, Sriwijaya University who provided companionship and sharing of their knowledge. References [1] Li, W., Shen, W. Swarm behavior control of mobile multi-robots with wireless sensor networks. Journal of Network and Computer Applications. 2011; 34:1398–1407. [2] Takemura, Y., Sato, M., shii, K. Toward realization of swarm intelligence mobile robots. International Congress Series. 2006; 1291: 273– 276. [3] Bonabeau, E., Dorigo, M., Theraulaz, G. Swarm intelligence: From natural to artificial systems. In Santa Fe Institute Studies in the Sciences of Complexity . 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