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Review of the Methods and Algorithms of a Robot Swarm Aggregation

https://doi.org/10.17587/mau.18.22-29

Abstract

The considered problems of aggregation of the swarm robots are mainly connected with the simplest computing, sensors and built-in actuators, as well as limited resources of the homogeneous swarm robots. In the area of the swarm robotics the multi-agent technologies are used to simulate the interaction of big groups of simple homogeneous robots. The limited resources of the individual robots have a significant effect on the configuration and capabilities of the whole system, however, the distributed swarm intelligence based on the data obtained during the mass pair interactions of the robots ensures the existence of a swarm and, due to it, solving of the set tasks. A review of the main methods for solving of the problem of aggregation in a swarm of robots (the method of the virtual forces, the probabilistic and evolutionary methods) showed that the choice of the used method, first of all, depends on the sensor, computing and network resources ofthe robots. Examples of different aggregation algorithms for the robot swarms, as well as limitations on the design solutions during implementation of these algorithms are presented. For estimation of the effectiveness of the robot aggregation the spatial and temporal methods are mainly used. The specific choice ofthe metrics depends on the parameters ofthe form, which should be achieved, as well as the aggregation method which is technically possible to implement to ensure control of a given robot swarm. By the results of the analysis, a conclusion was made that the systems, which use the neural networks for the swarm control are the most promising in terms of the further improvement of the aggregation algorithms, however, their implementation requires large onboard computational resources. During the preliminary research concerning the swarm robotics the principles and conceptual model were developed for the process of reconfiguration of the spatial position of the group of robots. They take into account the restrictions on the geometrical dimensions of a set of homogeneous robots and the occupied area of the initial position of the robots, the spatial characteristics, density of the robots' locations, as well as the ways of defining of the target position coordinates of the robots in a new spatial configuration. The further research will be aimed to solve the tasks of interaction within the robot swarm during construction of more complex forms, taking into account a bigger number of the physical parameters.

About the Authors

N. E. Shlyakhov
St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences
Russian Federation


I. V. Vatamaniuk
St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences
Russian Federation


A. L. Ronzhin
St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences
Russian Federation


References

1. Dudek G., Jenkin M., Milios E., Wilkes D. A taxonomy for swarm robots // Proceedings of the 1993 IEEE/RSJ International Conference on Intelligent Robots and Systems '93, IROS'93. 1993. Vol. 1. P. 441-447.

2. Городецкий В. И., Самойлов В. В., Троцкий Д. В. Искусственный интеллект - базовая онтология коллективного поведения автономных агентов и ее расширения // Известия РАН. Теория и системы управления. 2015. № 5, C. 102-121.

3. Каляев А. И., Каляев И. А. Метод децентрализованного управления группой роботов при выполнении потока заданий // Робототехника и техническая кибернетика. 2015. № 1 (6). С. 26-35.

4. Павловский В. Е., Павловский В. В. Масштабируемая система управления роботами робокон-1 // Информационно-измерительные и управляющие системы. 2013. Т. 11. № 4. С. 80-92.

5. Ishikawa T., Locsei J. T., Pedley T. J. Development of coherent structures in concentrated suspensions of swimming model micro-organisms // Journal of Fluid Mechanics. 2008. Vol. 615. P. 401-431.

6. Fetecau R. C. Collective behavior of biological aggregations in two dimensions: anon local kinetic model // Math. Models Methods Appl. Sci. 2011. Vol. 21 (7). P. 1539-1569.

7. Kernbach S., Thenius R., Kernbach O., Schmickl T. Re-embodiment of honeybee aggregation behavior in an artificial micro-robotics system // Adapt. Behav. 2009. Vol. 17 (3). P. 237-259.

8. Bayindir L. A probabilistic geometric model of self-organized aggregation in swarm robotic systems: Ph. D. thesis // Middle East Technical University. 2012.

9. Vanualailai J., Sharma B. A Lagrangian-based swarming behavior in the absence of obstacles // Workshop on Mathematical Control Theory, Kobe University. 2010. P. 8-10.

10. Hackett-Jones E. J., Landman K. A., Fellner K. Aggregation patterns from nonlocal interactions: discrete stochastic and continuum modeling // Phys. Rev. 2012. Vol. 85 (4). P. 041912.

11. Francesca G., Brambilla M., Brutschy A., Trianni V., Birat-tari M. Auto Mo De: a novel approach to the automatic design of control software for robot swarms // Swarm Intell. 2014. Vol. 8 (2). P. 89-112.

12. Burger M., Haskovec J., Wolfram M. T. Individual based and mean-field modeling of direct aggregation // Physica D: Nonlinear Phenom. 2013. Vol. 260. P. 145-158.

13. Blickle T., Thiele L. A Comparison of Selection Schemes used in Genetic Algorithm, 2 Edition // TIK-Report. 1995. 67 p.

14. Батищев Д. И., Исаев С. А. Оптимизация многоэкстремальных функций с помощью генетических алгоритмов // Межвузовский сборник научных трудов "Высокие технологии в технике, медицине и образовании". Воронеж: ВГТУ, 1997. C. 4-17.

15. Gomes J., Urbano P., Christensen A. L. Evolution of swarm robotics systems with novelty search // Swarm Intell. 2013. Vol. 7 (2-3). P. 115-144.

16. Gomes J., Christensen A. L. Generic behavior similarity measures for evolutionary swarm robotics // Proceeding of the Fifteenth Annual Conference on Geneticand Evolutionary Computation, ACM, NewYork. 2013. P. 199-206.

17. Correll N., Martinoli A. Modeling Self-Organized Aggregation in a Swarm of Miniature Robots // IEEE 2007 International Conference on Robotics and Automation Workshop on Collective Behaviors inspired by Biological and Biochemical Systems, 2007. P. 1.

18. Schmickl T., Moslinger C., Crailsheim K. Collective perception in a robot swarm // Swarm Robotics, Springer, Berlin. 2007. P. 144-157.

19. Bayindir L. A probabilistic geometric model of self-organized aggregation in swarm robotic systems: Ph. D. thesis // Middle East Technical University. 2012.

20. Soysal O., Sahin E. Probabilistic aggregation strategies in swarm robotic systems // Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005, IEEE Press, Piscataway. 2005. P. 325-332.

21. Trianni V., Labella T. H., GroB R., Sahin E., Dorigo M., Deneubourg J. L. Modeling Pattern Formation in a Swarm of Self-assembling Robots // Technical Report, IRIDIA, Universite Librede Bruxelles. 2002.

22. Mermoud G., Brugger J., Martinoli A. Towards multi-level modeling of self-assembling intelligent micro-systems // Proceedings of the 8th International Conference on Autonomous Agents and Multiagent Systems. 2009. Vol. 1. P. 89-96.

23. Gauci M., Chen J., Dodd T. J., GroB R. Evolving aggregation behaviors in multi-robot systems with binary sensors // Distributed Autonomous Robotic Systems. 2014. P. 355-367.

24. Gauci M., Chen J., Li W., Dodd T. J., GroB R. Self-organized aggregation without computation // Int. J. Robot. Res. 2014.

25. Trianni V., GroB R., Labella T. H., Sahin E., Dorigo M. Evolving aggregation behaviors in a swarm of robots // Advances in Artificial Life, Springer, Berlin. 2003. P. 865-874.

26. Garnier S., Jost C., Jeanson R., Gautrais J., Asadpour M., Caprari G., Theraulaz G. Aggregation behavior as a source of collective decision in a group of cockroach-like-robots // Advances in Artificial Life, Springer, Berlin. 2005. P. 169-178.

27. Soysal O., Sahin E. Probabilistic aggregation strategies in swarm robotic systems // Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005, IEEE Press, Piscataway. 2005. P. 325-332.

28. Fates N. Solving the decentralized gathering problem with a reaction- diffusion-chemotaxis scheme // Swarm Intell. 2010. Vol. 4 (2). P. 91-115.

29. Arvin F., Turgut A. E., Bellotto N., Yue S. Comparison of different cue-based swarm aggregation strategies // Advances in Swarm Intelligence, Springer, Cham. 2014. P. 1-8.

30. Ватаманюк И. В., Панина Г. Ю., Ронжин А. Л. Реконфигурация пространственного положения роя роботов // Управление большими системами. М.: ИПУ РАН, 2015. Вып. 58. С. 285-305.

31. Ватаманюк И. В., Панина Г. Ю., Ронжин А. Л. Моделирование траекторий перемещения робототехнических комплексов при реконфигурации пространственного положения роя // Робототехника и техническая кибернетика. 2015. № 3 (8). С. 52-57.


Review

For citations:


Shlyakhov N.E., Vatamaniuk I.V., Ronzhin A.L. Review of the Methods and Algorithms of a Robot Swarm Aggregation. Mekhatronika, Avtomatizatsiya, Upravlenie. 2017;18(1):22-29. (In Russ.) https://doi.org/10.17587/mau.18.22-29

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