Skip to main content
Log in

Pheromone Robotics

  • Published:
Autonomous Robots Aims and scope Submit manuscript

Abstract

We describe techniques for coordinating the actions of large numbers of small-scale robots to achieve useful large-scale results in surveillance, reconnaissance, hazard detection, and path finding. We exploit the biologically inspired notion of a “virtual pheromone,” implemented using simple transceivers mounted atop each robot. Unlike the chemical markers used by insect colonies for communication and coordination, our virtual pheromones are symbolic messages tied to the robots themselves rather than to fixed locations in the environment. This enables our robot collective to become a distributed computing mesh embedded within the environment, while simultaneously acting as a physical embodiment of the user interface. This leads to notions of world-embedded computation and world-embedded displays that provide different ways to think about robot colonies and the types of distributed computations that such colonies might perform.

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.

Institutional subscriptions

Similar content being viewed by others

References

  • Arkin, R.C. 1998. Behavior-Based Robotics, MIT Press: Cambridge, MA.

    Google Scholar 

  • Arkin, R.C. and Bekey, G.A. (Eds.). 1997. Robot Colonies, Kluwer Academic Publishers: Boston, MA.

    Google Scholar 

  • Azuma, R., Hoff, B., Neely III, H., and Sarfaty, R. 1999. A motionstabilized outdoor augmented reality system. In Proc. IEEE VR '99, Houston, TX, pp. 252-259.

  • Bonabeau, E., Dorigo, M., and Theraulaz, G. 1999. Swarm Intelligence: From Natural to Artificial Systems, Oxford University Press: New York.

    Google Scholar 

  • Deneubourg, J. and Goss, S. 1984. Collective patterns and decision-making. Ethology, Ecology, and Evolution, 1:295-311.

    Google Scholar 

  • Dijkstra, E.W. 1959. A note on two problems in connection with graph theory, Numerische Mathematik, 1:269-271.

    Google Scholar 

  • Gage, D.W. 1992. Command and control for many-robot systems. Unmanned Systems Magazine, 10(4):28-34.

    Google Scholar 

  • Gage, D.W. 1993. How to communicate with zillions of robots. In Proc. SPIE Mobile Robots VIII, Boston, MA, vol. 2058, pp. 250-257.

    Google Scholar 

  • Goss, S., Beckers, R., Deneubourg, J., Aron, S., and Pasteels, J. 1990. How trail laying and trail following can solve foraging problems. In Behavioral Mechanisms of Food Selection, R. Hughes (Ed.), Springer-Verlag: Heidelberg, Germany, pp. 661-678.

    Google Scholar 

  • Holland, O. and Melhuish, C. 2000. Stigmergy, self-organization, and sorting in collective robotics. Artificial Life, 5:2.

    Google Scholar 

  • Intanagonwiwat, C., Govindan, R., and Estrin, D. 2000. Directed diffusion: A scalable and robust communication paradigm for sensor networks. In Proc. 6th Annual Int. Conf. Mobile Computing and Networks (MobiCOM 2000), Boston, MA.

  • Lewis, M.A. and Bekey, G.A. 1992. The behavioral self-organization of nanorobots using local rules. In Proc. 1992 IEEE/RSJ Int. Conf. Intelligent Robots and Systems, Raleigh, NC.

  • McLurkin, J. 1999. Algorithms for distributed sensor networks, Masters Thesis, University of California: Berkeley.

    Google Scholar 

  • Mitchell, J.S.B., Payton, D., and Keirsey, D. 1987. Planning and reasoning for autonomous vehicle control. Int J. Intelligent Systems, 2:129-189.

    Google Scholar 

  • Payton, D.W. 1990. Internalized plans: A representation for action resources. In Designing Autonomous Agents, Pattie Maes, (Ed.), MIT Press: Cambridge, MA, pp. 89-103.

    Google Scholar 

  • Ñnsal, C. and Bay, J. 1994. Spatial self-organization in large populations of mobile robots. In IEEE Int. Symp. on Intelligent Control, pp. 249-254.

  • Werger, B.B. and Mataric, M.J. 1996. Robotic food chains: Externalization of state and program for minimal-agent foraging. In Proc. 4th Int. Conf. Simulation of Adaptive Behavior: From Animals to Animats 4, MIT Press: Cambridge, MA, pp. 625-626.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Payton, D., Daily, M., Estowski, R. et al. Pheromone Robotics. Autonomous Robots 11, 319–324 (2001). https://doi.org/10.1023/A:1012411712038

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1012411712038

Navigation