Self-organized flocking in mobile robot swarms
In this paper, we study self-organized flocking in a swarm of mobile robots. We present Kobot, a mobile robot platform developed specifically for swarm robotic studies. We describe its infrared-based short range sensing system, capable of measuring the distance from obstacles and detecting kin robots, and a novel sensing system called the virtual heading system (VHS) which uses a digital compass and a wireless communication module for sensing the relative headings of neighboring robots.
We propose a behavior based on heading alignment and proximal control that is capable of generating self-organized flocking in a swarm of Kobots. By self-organized flocking we mean that a swarm of mobile robots, initially connected via proximal sensing, is able to wander in an environment by moving as a coherent group in open space and to avoid obstacles as if it were a “super-organism”. We propose a number of metrics to evaluate the quality of flocking. We use a default set of behavioral parameter values that can generate acceptable flocking in robots, and analyze the sensitivity of the flocking behavior against changes in each of the parameters using the metrics that were proposed. We show that the proposed behavior can generate flocking in a small group of physical robots in a closed arena as well as in a swarm of 1000 simulated robots in open space. We vary the three main characteristics of the VHS, namely: (1) the amount and nature of noise in the measurement of heading, (2) the number of VHS neighbors, and (3) the range of wireless communication. Our experiments show that the range of communication is the main factor that determines the maximum number of robots that can flock together and that the behavior is highly robust against the other two VHS characteristics. We conclude by discussing this result in the light of related theoretical studies in statistical physics.
KeywordsSwarm robotics Self-organization Flocking
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- Ballerini, M., Cabibbo, N., Candelier, R., Cavagna, A., Cisbani, E., Giardina, I., Lecomte, V., Orlandi, A., Parisi, G., Procaccini, A., Viale, M., & Zdravkovic, V. (2008). Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study. Proceedings of the National Academy of Sciences, 105(4), 1232–1237. CrossRefGoogle Scholar
- Camazine, S., Deneubourg, J.-L., Franks, N. R., Sneyd, J., Theraulaz, G., & Bonabeau, E. (2001). Self-Organization in Biological Systems. New Jersey: Princeton University Press. Google Scholar
- Campo, A., Nouyan, S., Birattari, M., Groß, R., & Dorigo, M. (2006). Negotiation of goal direction for cooperative transport. In M. Dorigo et al. (Eds.), Lecture notes in computer science: Vol. 4150. Ant colony optimization and swarm intelligence: 5th international workshop, ANTS 2006 (pp. 191–202). Berlin: Springer. CrossRefGoogle Scholar
- Çelikkanat, H., Turgut, A. E., Gökçe, F., & Şahin, E. (2007). Evaluation of robustness in self-organized flocking (Tech. Rep. METU-CENG-TR-2008-02). Dept. of Computer Eng., Middle East Tech. Univ., Ankara, Turkey. Google Scholar
- Correll, N., Sempo, G., de Meneses, Y. L., Halloy, J., Deneubourg, J.-L., & Martinoli, A. (2006). SwisTrack: A tracking tool for multi-unit robotic and biological systems. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (pp. 2185–2191). New Jersey: IEEE Press. CrossRefGoogle Scholar
- Dalgaard, P. (2004). Introductory statistics with R, 3rd edn. Statistics and computing. New York: Springer. Google Scholar
- Hayes, A., & Dormiani-Tabatabaei, P. (2002). Self-organized flocking with agent failure: Off-line optimization and demonstration with real robots. In Proceedings of the IEEE international conference on robotics and automation (pp. 3900–3905). New Jersey: IEEE Press. Google Scholar
- Kelly, I., & Keating, D. (1996). Flocking by the fusion of sonar and active infrared sensors on physical autonomous robots. In Proceedings of the third international conference on mechatronics and machine vision in practice (Vol. 1, pp. 14–17). Guimarães: Universidade do Minho. Google Scholar
- Kruszelnicki, K. S. (2008). Physics of flocks. http://www.abc.net.au/science/k2/moments/gmis9845.htm.
- Matarić, M. J. (1994). Interaction and intelligent behavior. Ph.D. thesis, MIT. Google Scholar
- Moshtagh, N., Jadbabaie, A., & Daniilidis, K. (2006). Vision-based control laws for distributed flocking of nonholonomic agents. In Proceedings of the IEEE international conference on robotics and automation (pp. 2769–2774). New Jersey: IEEE Press. Google Scholar
- Nembrini, J. (2005). Minimalist coherent swarming of wireless networked autonomous mobile robots. Google Scholar
- Nembrini, J., Winfield, A. F. T., & Melhuish, C. (2002). Minimalist coherent swarming of wireless networked autonomous mobile robots. In B. Hallam, D. Floreno, J. Hallam, G. Hayes, & J.-A. Meyer (Eds.), Proceedings of the 7th international conference on the simulation of adaptive behavior conference (Vol. 7, pp. 273–382). Cambridge: MIT Press. Google Scholar
- Pitcher, T. J., & Parrish, J. K. (1993). Functions of shoaling behavior in teleosts. In T. J. Pitcher (Ed.), Behaviour of teleost fishes (pp. 363–439). London: Chapman and Hall. Google Scholar
- Simon, G., Volgyesi, P., Maroti, M., & Ledeczi, A. (2003). Simulation-based optimization of communication protocols for large-scale wireless sensor networks. In IEEE aerospace conference (Vol. 3, pp. 1339–1346). New Jersey: IEEE Press. Google Scholar
- Turgut, A. E., Gökçe, F., Çelikkanat, H., Bayındır, L., & Şahin, E. (2007). Kobot: A mobile robot designed specifically for swarm robotics research (Tech. Rep. METU-CENG-TR-2007-05). Dept. of Computer Eng., Middle East Tech. Univ., Ankara, Turkey. Google Scholar
- Turgut, A. E., Çelikkanat, H., Gökçe, F., & Şahin, E. (2008). Self-organized flocking with a mobile robot swarm. In: Proceedings of the 7th international conference on autonomous agents and multiagent systems, AAMAS 2008 (pp. 39–46). International Foundation for Autonomous Agents and Multiagent Systems. Google Scholar
- Wallraff, H. G. (2005). Avian navigation: Pigeon homing as a paradigm. Berlin: Springer. Google Scholar