Skip to main content
Log in

Ant-based swarming with positionless micro air vehicles for communication relay

  • Published:
Swarm Intelligence Aims and scope Submit manuscript

Abstract

Swarming without positioning information is interesting in application-oriented systems because it alleviates the need for sensors which are dependent on the environment, expensive in terms of energy, cost, size and weight, or unusable at useful ranges for real-life scenarios. This principle is applied to the development of a swarm of micro air vehicles (SMAVs) for the deployment of ad hoc wireless communication networks (SMAVNETs) between ground users in disaster areas. Rather than relying on positioning information, MAVs rely on local communication with immediate neighbors and proprioceptive sensors which provide heading, speed and altitude.

To solve the challenging task of designing agent controllers to achieve the swarm behavior of the SMAVNET, inspiration is taken from army ants which are capable of laying and maintaining pheromone paths leading from their nest to food sources in nature. This is analogous to the deployment of communication pathways between multiple ground users. However, instead of being physically deposited in the air or on a map, pheromone is virtually deposited on the MAVs using local communication. This approach is investigated in 3D simulation in a simplified scenario with two ground users.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Allred, J., Hasan, A. B., Panichsakul, S., Pisano, W., Gray, P., Huang, J., Han, R., Lawrence, D., & Mohseni, K. (2007). SensorFlock: an airborne wireless sensor network of micro-air vehicles. In Proceedings of the 5th international conference on embedded networked sensor systems (pp. 117–129). New York: ACM Press.

    Chapter  Google Scholar 

  • 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 

  • Burton, J. L., & Franks, N. R. (1985). The foraging ecology of the army ant Eciton rapax: an ergonomic enigma? Ecological Entomology, 10(2), 131–141.

    Article  Google Scholar 

  • Camazine, S., Crailsheim, K., Hrassnigg, N., Robinson, G. E., Leonhard, B., & Kropiunigg, H. (1998). Protein trophallaxis and the regulation of pollen foraging by honey bees (Apis mellifera L.). Apidologie, 29(1), 113–126.

    Article  Google Scholar 

  • Campo, A., & Dorigo, M. (2007). Efficient multi-foraging in swarm robotics. In Lecture notes in artificial intelligence : Vol. 4648. Advances in artificial life, proceedings of the ninth European conference on artificial life (pp. 696–705). Berlin: Springer.

    Google Scholar 

  • Crailsheim, K. (1998). Trophallactic interactions in the adult honeybee (Apis mellifera L.). Apidologie, 29(1), 97–112.

    Article  Google Scholar 

  • De Nardi, R., & Holland, O. (2007). UltraSwarm: a further step towards a flock of miniature helicopters. In Lecture notes in computer science : Vol. 4433. Swarm robotics (pp. 116–128). Berlin: Springer.

    Chapter  Google Scholar 

  • Deneubourg, J. L., Goss, S., Franks, N. R., & Pasteels, J. M. (1989). The blind leading the blind: modeling chemically mediated army ant raid patterns. Journal of Insect Behavior, 2(5), 719–725.

    Article  Google Scholar 

  • Dixon, C., & Frew, E. W. (2007). Maintaining optimal communication chains in robotic sensor networks using mobility control. In Proceedings of the 1st international conference on robot communication and coordination (pp. 1–8). Piscataway: IEEE 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 

  • Fenton, L. (1960). The sum of log-normal probability distributions in scatter transmission systems. Proceedings of the IEEE Transactions on Communications Systems, 8(1), 57–67.

    Article  MathSciNet  Google Scholar 

  • Flint, M., Polycarpou, M., & Fernández-Gaucherand, E. (2002). Cooperative control for multiple autonomous UAVs searching for targets. In Proceedings of the 41st IEEE conference on decision and control (Vol. 3, pp. 2823–2828). Piscataway: IEEE Press.

    Chapter  Google Scholar 

  • Franks, N. R., Gomez, N., Goss, S., & Deneubourg, J. L. (2001). The blind leading the blind in army ant raid patterns: testing a model of self-organization (Hymenoptera: Formicidae). Journal of Insect Behavior, 4(5), 583–607.

    Article  Google Scholar 

  • Gaudiano, P., Bonabeau, E., & Shargel, B. (2005). Evolving behaviors for a swarm of unmanned air vehicles. In Proceedings of the IEEE swarm intelligence symposium (pp. 317–324). Piscataway: IEEE Press.

    Chapter  Google Scholar 

  • Holland, O., Woods, J., De Nardi, R., & Clark, A. (2005). Beyond swarm intelligence: the UltraSwarm. In Proceedings of the IEEE swarm intelligence symposium (pp. 217–224). Piscataway: IEEE Press.

    Chapter  Google Scholar 

  • Hu, L., & Evans, D. (2004). Localization for mobile sensor networks. In Proceedings of the 10th annual international conference on mobile computing and networking (pp. 45–57). New York: ACM Press.

    Chapter  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 

  • Kovacina, M., Palmer, D., Yang, G., & Vaidyanathan, R. (2002). Multi-agent control algorithms for chemical cloud detection and mapping using unmanned air vehicles. In Proceedings of the IEEE/RSJ international conference on intelligent robots and system (Vol. 3, pp. 2782–2788). Piscataway: IEEE Press.

    Chapter  Google Scholar 

  • Kuiper, E., & Nadjm-Tehrani, S. (2006). Mobility models for UAV group reconnaissance applications. In Proceedings of the IEEE international conference on wireless and mobile communications. Piscataway: IEEE Press. doi:10.1109/ICWMC.2006.63.

    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. Reston: AIAA Press, AIAA paper 2004-6530.

    Google Scholar 

  • Merino, L., Caballero, F., Martínez-de Dios, J. R., Ferruz, J., & Ollero, A. (2006). A cooperative perception system for multiple UAVs: application to automatic detection of forest fires. Journal of Field Robotics, 23, 165–184.

    Article  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 

  • Nembrini, J., Reeves, N., & Poncet, E. (2005). Mascarillons: flying swarm intelligence for architectural research. In Proceedings of the IEEE swarm intelligence symposium (pp. 225–232). Piscataway: IEEE Press.

    Chapter  Google Scholar 

  • Nouyan, S., Campo, A., & Dorigo, M. (2008). Path formation in a robot swarm: self-organized strategies to find your way home. Swarm Intelligence, 2(1), 1–23.

    Article  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.

    Chapter  Google 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 

  • Payton, D., Daily, M., Estowski, R., Howard, M., & Lee, C. (2001). Pheromone robotics. Autonomous Robots, 11, 319–324.

    Article  MATH  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 

  • Richards, M. D., Whitley, D., & Beveridge, J. R. (2005). Evolving cooperative strategies for UAV teams. In Proceedings of the genetic and evolutionary computation conference (Vol. 2, pp. 1721–1728). New York: ACM Press.

    Chapter  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. Reston: AIAA Press; AIAA paper 2007-6614.

    Google Scholar 

  • Ryan, A., Tisdale, J., Godwin, M., Coatta, D., Nguyen, D., Spry, S., Sengupta, R., & Hedrick, J. K. (2007). Decentralized control of unmanned aerial vehicle collaborative sensing missions. In Proceedings of the American control conference (pp. 4672–4677). Piscataway: IEEE Press.

    Chapter  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 

  • Schmickl, T., & Crailsheim, K. (2006). Trophallaxis within a robot swarm: bio-inspired communication among robots in a swarm. In Proceedings of the 1st IEEE/RAS-EMBS international conference on biomedical robotics and biomechanotronics (pp. 377–382). Piscataway: IEEE Press.

    Chapter  Google Scholar 

  • Schmickl, T., & Crailsheim, K. (2007). Trophallaxis within a robotic swarm: bio-inspired communication among robots in a swarm. Autonomous Robots, 25(1–2), 171–188.

    Google Scholar 

  • Siegwart, R., & Nourbakhsh, I. R. (2004). Introduction to autonomous mobile robots. Cambridge: Bradford Book/MIT Press.

    Google Scholar 

  • Solé, R., Bonabeau, E., Fernàndez, P., & Marìn, J. (2000). Pattern formation and optimization in army ant raids. Artificial Life, 6(3), 219–226.

    Article  Google Scholar 

  • Soto, J., & Lin, K. C. (2005). Using genetic algorithms to evolve the control rules of a swarm of UAVs. In Proceedings of the IEEE international symposium on collaborative technologies and systems (pp. 359–365). Piscataway: IEEE Press.

    Chapter  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 

  • Turgut, A., Çelikkanat, H., Gökçe, F., & Şahin, E. (2008). Self-organized flocking with a mobile robot swarm. In Proceedings of the 7th international joint conference on autonomous agents and multi-agent systems (pp. 39–46). New York: ACM Press.

    Google Scholar 

  • Vincent, P., & Rubin, I. (2004). A framework and analysis for cooperative search using UAV swarms. In Proceedings of the ACM symposium on applied computing (pp. 79–86). New York: ACM Press.

    Chapter  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 

  • Yang, Y., Minai, A. A., & Polycarpou, M. M. (2005). Evidential map-building approaches for multi-UAV cooperative search. In Proceedings of the IEEE American control conference (pp. 116–121). Piscataway: IEEE Press.

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sabine Hauert.

Electronic Supplementary Material

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hauert, S., Winkler, L., Zufferey, JC. et al. Ant-based swarming with positionless micro air vehicles for communication relay. Swarm Intell 2, 167–188 (2008). https://doi.org/10.1007/s11721-008-0013-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11721-008-0013-5

Keywords

Navigation