Evolved swarming without positioning information: an application in aerial communication relay
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In most swarm systems, agents are either aware of the position of their direct neighbors or they possess a substrate on which they can deposit information (stigmergy). However, such resources are not always obtainable in real-world applications because of hardware and environmental constraints. In this paper we study in 2D simulation the design of a swarm system which does not make use of positioning information or stigmergy.
This endeavor is motivated by an application whereby a large number of Swarming Micro Air Vehicles (SMAVs), of fixed-wing configuration, must organize autonomously to establish a wireless communication network (SMAVNET) between users located on ground. Rather than relative or absolute positioning, agents must rely only on their own heading measurements and local communication with neighbors.
Designing local interactions responsible for the emergence of the SMAVNET deployment and maintenance is a challenging task. For this reason, artificial evolution is used to automatically develop neuronal controllers for the swarm of homogenous agents. This approach has the advantage of yielding original and efficient swarming strategies. A detailed behavioral analysis is then performed on the fittest swarm to gain insight as to the behavior of the individual agents.
KeywordsSwarm intelligence Swarming without positioning Micro Air Vehicles (MAVs) Communication relay Artificial evolution Situated communication SMAVNET
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- Basu, P., Redi, J., & Shurbanov, V. (2004). Coordinated flocking of UAVs for improved connectivity of mobile ground nodes. In Proceedings of the IEEE military communications conference (Vol. 3, pp. 1628–1634). Piscataway: IEEE Press. Google Scholar
- Camazine, S., Deneubourg, J. L., Franks, N. R., Sneyd, J., Theraulaz, G., & Bonabeau, E. (2001). Self-organization in biological systems. Princeton: Princeton University Press. Google Scholar
- Elston, J., & Frew, E. W. (2008). Hierarchical distributed control for search and tracking by heterogeneous aerial robot networks. In Proceedings of the IEEE international conference on robotics and automation (pp. 170–175). Piscataway: IEEE Press. Google Scholar
- Kadrovach, B. A., & Lamont, G. B. (2001). Design and analysis of swarm-based sensor systems. In Proceedings of the 44th IEEE Midwest symposium on circuits and systems (Vol. 1, pp. 487–490). Piscataway: IEEE Press. Google Scholar
- Lawrence, D., Donahue, R., Mohseni, K., & Han, R. (2004). Information energy for sensor-reactive UAV flock control. In Proceedings of the AIAA 3rd “Unmanned unlimited” technical conference, AIAA paper 2004-6530. Reston: AIAA Press. Google Scholar
- Leven, S., Zufferey, J. C., & Floreano, D. (2007). A simple and robust fixed-wing platform for outdoor flying robot experiments. In Flying insects and robots symposium (p. 69). Google Scholar
- Lin, K., Huang, K., Li, G., & Qui, X. G. (2004). Control of swarming UAVs in collaborative missions. In TSI press series : Vol. 17. Proceedings of the World automation congress (pp. 25–30). Albuquerque: TSI Press. Google Scholar
- Nembrini, J., Winfield, A., & Melhuish, C. (2002). Minimalist coherent swarming of wireless networked autonomous mobile robots. In From animals to animats 7, proceedings of the 7th international conference on simulation of adaptive behavior (pp. 273–382). Cambridge: MIT Press. Google Scholar
- Nolfi, S., & Floreano, D. (2000). Evolutionary robotics: the biology, intelligence, and technology of self-organizing machines. Cambridge: MIT Press. Google Scholar
- Oh, E. S. (2003). Information and communication technology in the service of disaster mitigation and humanitarian relief. In Proceedings of the IEEE 9th Asia-Pacific conference on communications (Vol. 2, pp. 730–734). Piscataway: IEEE Press. Google Scholar
- Pack, D. J., & York, G. W. P. (2005). Developing a control architecture for multiple unmanned aerial vehicles to search and localize RF time-varying mobile targets: part I. In Proceedings of the IEEE international conference on robotics and automation (pp. 3954–3959). Piscataway: IEEE Press. CrossRefGoogle Scholar
- Parunak, H. V. D., Brueckner, S. A., & Sauter, J. (2005). Digital pheromones for coordination of unmanned vehicles. In Lecture notes in computer science : Vol. 3374. Environments for multi-agent systems (pp. 246–263). Berlin: Springer. Google Scholar
- Reynolds, C. W. (1987). Flocks, herds and schools: a distributed behavioral model. In SIGGRAPH computer graphics (Vol. 21, pp. 25–34). New York: ACM Press. Google Scholar
- Rodriguez, A., Andersen, E., Bradley, J., & Taylor, C. (2007). Wind estimation using an optical flow sensor on a miniature air vehicle. In Proceedings of the AIAA conference on guidance, navigation, and control, AIAA paper 2007-6614. Reston: AIAA Press. Google Scholar
- Şahin, E. (2005). Swarm robotics: from sources of inspiration to domains of application. In Lecture notes in computer science : Vol. 3342. Swarm robotics (pp. 10–20). Berlin: Springer. Google Scholar
- Sauter, J. A., Matthews, R., Parunak, H. V. D., & Brueckner, S. A. (2005). Performance of digital pheromones for swarming vehicle control. In Proceedings of the 4th international joint conference on autonomous agents and multi-agent systems (pp. 903–910). New York: ACM Press. Google Scholar
- Siegwart, R., & Nourbakhsh, I. R. (2004). Introduction to autonomous mobile robots. MIT Press, Cambridge: Bradford Book. Google Scholar
- Spears, W. M., Spears, D. F., Heil, R., Kerr, W., & Hettiarachchi, S. (2005). An overview of physicomimetics. In Lecture notes in computer science : Vol. 3342. Simulation of adaptive behaviour, workshop on swarm robotics (pp. 84–97). Berlin: Springer. Google Scholar
- Støy, K. (2001). Using situated communication in distributed autonomous mobile robots. In Proceedings of the 7th Scandinavian conference on artificial intelligence (pp. 44–52). Amsterdam: IOS Press. Google Scholar
- Winfield, A. (2000). Distributed sensing and data collection via broken ad hoc wireless connected networks of mobile robots. In Proceedings of distributed autonomous systems 4 (pp. 273–282). Berlin: Springer. Google Scholar
- Winfield, A. F. T., Harper, C. J., & Nembrini, J. (2005a). Towards dependable swarms and a new discipline of swarm engineering. In Lecture notes in computer science : Vol. 4433. Swarm robotics (pp. 126–142). Berlin: Springer. Google Scholar
- Winfield, A. F. T., Sa, J., Fernández-Gago, M. C., Dixon, C., & Fisher, M. (2005b). On formal specification of emergent behaviours in swarm robotic systems. International Journal of Advanced Robotic Systems, 2(4), 363–370. Google Scholar
- Wu, A. S., Schultz, A. C., & Agah, A. (1999). Evolving control for distributed micro air vehicles. In Proceedings of the IEEE international symposium on computational intelligence in robotics and automation (pp. 174–179). Piscataway: IEEE Press. Google Scholar