Hostname: page-component-78c5997874-v9fdk Total loading time: 0 Render date: 2024-11-10T15:36:59.098Z Has data issue: false hasContentIssue false

A Novel Framework for Multi-Agent Systems Using a Decentralized Strategy

Published online by Cambridge University Press:  04 December 2018

Mehmet Serdar Güzel*
Affiliation:
Computer Engineering Department, Ankara University, Ankara, Turkey. E-mails: vahid.babaei@ankara.edu.tr, emir.cem.gezer@ogrenci.ankara.edu.tr, serhat.can@ogrenci.ankara.edu.tr
Vahid Babaei Ajabshir
Affiliation:
Computer Engineering Department, Ankara University, Ankara, Turkey. E-mails: vahid.babaei@ankara.edu.tr, emir.cem.gezer@ogrenci.ankara.edu.tr, serhat.can@ogrenci.ankara.edu.tr
Panus Nattharith
Affiliation:
Faculty of Engineering, Naresuan University, Phitsanulok 65000, Thailand. E-mail: panusn@nu.ac.th
Emir Cem Gezer
Affiliation:
Computer Engineering Department, Ankara University, Ankara, Turkey. E-mails: vahid.babaei@ankara.edu.tr, emir.cem.gezer@ogrenci.ankara.edu.tr, serhat.can@ogrenci.ankara.edu.tr
Serhat Can
Affiliation:
Computer Engineering Department, Ankara University, Ankara, Turkey. E-mails: vahid.babaei@ankara.edu.tr, emir.cem.gezer@ogrenci.ankara.edu.tr, serhat.can@ogrenci.ankara.edu.tr
*
*Corresponding author. E-mail: mguzel@ankara.edu.tr

Summary

This work addresses a new framework that proposes a decentralized strategy for collective and collaborative behaviours of multi-agent systems. This framework includes a new clustering behaviour that causes agents in the swarm to agree on attending a group and allocating a leader for each group, in a decentralized and local manner. The leader of each group employs a vision-based goal detection algorithm to find and acquire the goal in a cluttered environment. As soon as the leader starts moving, each member is enabled to move in the same direction by staying coordinated with the leader and maintaining the desired formation pattern. In addition, an exploration algorithm is designed and integrated into the framework so as to allow each group to be able to explore goals in a collaborative and efficient manner. A series of comprehensive experiments are conducted in order to verify the overall performance of the proposed framework.

Type
Articles
Copyright
Copyright © Cambridge University Press 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Brambilla, M., Ferrante, E., Birattari, M. and Dorigo, M., “Swarm robotics: A review from the swarm engineering,” Swarm Intell. 7(1), 141 (2013).10.1007/s11721-012-0075-2CrossRefGoogle Scholar
Chamanbaz, M., Mateo, D., Zoss, B. M., Tokic, G., Wilhelm, E., Bouffanais, R. and Yue, D. K. P., “Swarm-enabling technology for multi-robot systems,” Front. Rob. AI 4, 12 (2017).Google Scholar
Flocchini, P., Prencipe, G., Nicola, S. and Widmayer, P., “Arbitrary pattern formation by asynchronous, anonymous, oblivious robots,” Theor. Comput. Sci. 407(1–3), 412447 (2008).10.1016/j.tcs.2008.07.026CrossRefGoogle Scholar
Nouyan, S., Campo, A. and Dorigo, M., “Path formation in a robot swarm: self-organized strategies to find your way home,” Swarm Intell. 2(1), 123 (2008).10.1007/s11721-007-0009-6CrossRefGoogle Scholar
Soysal, O., Bahçeci, E. and Șahin, E., “Aggregation in swarm robotic systems: Evolution,” Turk. J. Electr. Eng. 15(2), 199225 (2005).Google Scholar
Soysal, O. and Bahçeci, E., “Probabilistic aggregation strategies in swarm robotic systems,” Proceedings 2005 IEEE Swarm Intelligence Symposium, SIS 2005, Pasadena, CA, USA (2005) pp. 325332.10.1109/SIS.2005.1501639CrossRefGoogle Scholar
Barca, J. C. and Sekercioglu, A., “Swarm robotics reviewed,” Robotica 31(3), 345359 (2012).10.1017/S026357471200032XCrossRefGoogle Scholar
Soysal, O. and Șahin, E., “A Macroscopic Model for Self-organized Aggregation in Swarm Robotic Systems,” In: Swarm Robotics (Springer, Berlin, Heidelberg, 2007) pp. 2742.10.1007/978-3-540-71541-2_3CrossRefGoogle Scholar
Balch, T. and Hybinette, M., “Social Potentials for Scalable Multi-robot Formations,” IEEE International Conference on Robotics and Automation, 2000. Proceedings. ICRA’00 (IEEE Press, Piscataway, 2000) pp. 7380.Google Scholar
Turgut, A. E., Çelikkanat, H., Gökçe, H. and Erol, Ş., “Self-organized flocking inmobile robot swarms,” Swarm Intell. 2(2), 97120 (2008).10.1007/s11721-008-0016-2CrossRefGoogle Scholar
Çelikkanat, H. andErol, Ş., “Steering self-organized robot flocks through externally guided individuals,” Neural Comput. Appl. 19(6), 849865 (2010).10.1007/s00521-010-0355-yCrossRefGoogle Scholar
Ferrante, E., Turgut, A. E., Huepe, C., Stranieri, A., Pinciroli, C. and Dorigo, M., “Self-organized flocking with a mobile robot swarm: a novel motion control method,” Adapt. Behav. 20(6), 460477 (2012).10.1177/1059712312462248CrossRefGoogle Scholar
Guzel, M. S., Gezer, E. C., Ajabshir, V. B. and Bostancı, E., “An Adaptive Pattern Formation Approach for Swarm Robots,” 2017 4th International Conference on Electrical and Electronic Engineering (ICEEE) (Ankara University, Ankara, TR, 2017) pp. 194198.10.1109/ICEEE2.2017.7935818CrossRefGoogle Scholar
Ton, C., Kan, Z. and Mehta, S. S., “Obstacle avoidance control of a human-in-the-loop mobile robot system using harmonic potential fields,” Robotica 36(4), 463483 (2018).10.1017/S0263574717000510CrossRefGoogle Scholar
Rodriguez-Angeles, A. and Vazquez, C. L., “Bio-inspired decentralized autonomous robot mobile navigation control for multi agent systems,” Kybernetika 54(1), 135154 (2018).Google Scholar
Wang, Y. Z., Wang, D. W. and Mihankhah, E., “Navigation of Multiple Robots in Unknown Environments Using a New Decentralized Navigation Function,” 2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV), Phuket, Thailand (IEEE Press, 2016).Google Scholar
Nazarzehi, V. and Savkin, A. V., “Decentralized Navigation of Nonholonomic Robots for 3D Formation Building,” 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014), Bali, Indonesia (IEEE Press, 2014) pp. 21332137.Google Scholar
Guzel, M. and Kayakökü, H., “A Collective Behavior Framework for Multi-agent Systems,” Mechatronics and Robotics Engineering for Advanced and Intelligent Manufacturing. Lecture Notes in Mechanical Engineering (Springer, Cham, 2016) pp. 6171.Google Scholar
Stranieri, A., Ferrante, E., Turgut, A. E., Trianni, V., Pinciroli, C., Birattari, M. and Dorigo, M., “Self-organized Flocking with a Heterogeneous Mobile Robot Swarm,” Advances in Artificial Life, ECAL (MIT Press, Cambridge, MA, 2011) pp. 789796.Google Scholar
Li, H., Feng, C., Ehrhard, H., Shen, Y., Cobos, B., Zhang, F., Elamvazhuthi, K., Berman, S., Haberland, M. and Bertozzi, A. L., “Decentralized Stochastic Control of Robotic Swarm Density: Theory, Simulation, and Experiment,” 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada (IEEE Press, 2017) pp. 43414347.10.1109/IROS.2017.8206299CrossRefGoogle Scholar
Amato, C., Konidaris, G., Cruz, G., Maynor, C. A., How, J. P. and Kaelbling, L. P., “Planning for Decentralized Control of Multiple Robots under Uncertainty,” Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA (IEEE Press, 2015) pp. 12411248.10.1109/ICRA.2015.7139350CrossRefGoogle Scholar
Maeda, R., Endo, T. and Matsuno, F., “Decentralized navigation for heterogeneous swarm robots with limited field of view,” IEEE Robot. Autom. Lett. 2(2), 904911 (2017).10.1109/LRA.2017.2654549CrossRefGoogle Scholar
Xin, B., Gao, G. Q., Ding, Y. L., Zhu, Y.G. and Fang, H., “Distributed Multi-robot Motion Planning for Cooperative Multi-area Coverage,” 2017 13th IEEE International Conference on Control & Automation (ICCA), Ohrid, Macedonia, (2017) pp. 361366.10.1109/ICCA.2017.8003087CrossRefGoogle Scholar
Roussos, G. and Kyriakopoulos, K. J., “Decentralized and Prioritized Navigation and Collision Avoidance for Multiple Mobile Robots,” In: Distributed Autonomous Robotic Systems. Springer Tracts in Advanced Robotics (Martinoli, A. et al., eds.), vol. 83 (Springer, Berlin, Heidelberg, 2013).Google Scholar
Jiménez, A. C., García-Díaz, V. and Bolaños, S., “A decentralized framework for multi-agent robotic systems,” Sensors 18(2), 417 (2018).10.3390/s18020417CrossRefGoogle ScholarPubMed
Nattharith, P. and Guzel, M. S., “Machine vision and fuzzy logic-based navigation control of a goal-oriented mobile robot,” Adapt. Behav. 24(3), 168180 (2016).10.1177/1059712316645845CrossRefGoogle Scholar
Kasuya, M., Ito, N., Inuzuka, N. and Wada, K., “A pattern formation algorithm for a set of autonomous distributed robots with agreement on orientation along one axis,” Syst. Comput. Jpn. 37(10), 747757 (2006).10.1002/scj.20331CrossRefGoogle Scholar