Skip to main content
SpringerLink
Log in

Tracking and sensor coverage of spatio-temporal quantities using a swarm of artificial foraging agents

  • Published:
Journal of Bionic Engineering Aims and scope Submit manuscript

Abstract

Using a network of mobile sensors to track and map a dynamic spatio-temporal process in the environment is one of the current challenges in multi-agent systems. In this work, a distributed probabilistic multi-agent algorithm inspired by the bacterium foraging behavior is presented. The novelty of the algorithm lies in being capable of tracking and mapping a spatio-temporal quantity without the need of machine learning, estimation algorithms or future planning. This is unlike most current techniques that rely heavily on machine learning to estimate the distribution as well as the profile of spatio-temporal quantities. The experimental studies carried out in this work show that the algorithm works well by following the concentration gradient of a dynamic plume created under diffusive conditions. Furthermore, the algorithm is inherently capable of finding the source of a diffusive spatio-temporal quantity as well as performing environmental exploration. It is computationally tractable for simple agents, shown to adapt to its environment and can deal successfully with noise in sensor readings as well as in robot dynamics.

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

Access this article

Log in via an institution

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

  1. Marjovi A, Marques L. Swarm robotic plume tracking for intermittent and time-variant odor dispersion. Proceedings of the IEEE European Conference on Mobile Robots (ECMR), Barcelona, Spain, 2013, 379–384.

    Google Scholar 

  2. He Y Y, Song C, Yang P P, Lei X K. Bio-inspired guiding strategy for robot seeking intermittent information source. Proceedings of the IEEE 13th International Bhurban Conference on Applied Sciences and Technology (IBCAST), Islamabad, Pakistan, 2016, 161–166.

    Google Scholar 

  3. Kang X, Li W. Moth-inspired plume tracing via multiple autonomous vehicles under formation control. Adaptive Behavior, 2012, 20, 131–142.

    Article  Google Scholar 

  4. Johnson B R. Pattern formation on the combs of honeybees: Increasing fitness by coupling self-organization with templates. Proceedings of the Royal Society: Biological Sciences, 2009, 276, 255–261.

    Article  Google Scholar 

  5. Jost C, Verret J, Casellas E, Gautrais J, Challet M, Lluc J, Blanco S, Clifton M J, Theraulaz G. The interplay between a self-organized process and an environmental template: Corpse clustering under the influence of air currents in ants. Journal of the Royal Society: Interface, 2007, 4, 107–116.

    Google Scholar 

  6. Oyekan J, Gu D, Hu H. Visual imaging of invisible hazardous substances using bacterial inspiration. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2013, 43, 1105–1115.

    Article  Google Scholar 

  7. Kwok A, Martinez S. A distributed deterministic annealing algorithm for limited-range sensor coverage. IEEE Transactions on Control Systems Technology, 2011, 19, 792–804.

    Article  Google Scholar 

  8. Shucker B, Murphey T, Bennett J K. Convergence preserving switching for topology dependent decentralized systems. IEEE Transactions on Robotics, 2008, 24, 1–11.

    Article  Google Scholar 

  9. Cortes J, Martinez S, Karatas T, Bullo F. Coverage control for mobile sensing networks. IEEE Transactions on Robotics and Automation, 2004, 20, 243–255.

    Article  Google Scholar 

  10. Schwager M, Bullo F, Skelly D, Rus D. A ladybug exploration strategy for distributed adaptive coverage control. Proceedings of the IEEE International Conference on Robotics and Automation, Pasadena, USA, 2008, 2346–2353.

    Google Scholar 

  11. Lu B, Gu D, Hu H. Tracking and modeling of spatio-temporal fields with a mobile sensor network. 11th World Congress on Intelligent Control and Automation (WCICA), Shenyang, China, 2014, 2711–2716.

    Google Scholar 

  12. Mahboubi H, Aghdam A G, Sayrafian-Pour K. Area coverage in a fixed-obstacle environment using mobile sensor networks. In EI-Osery A, Prevost J eds., Control and Systems Engineering, Springer, 2015, 135–151.

    Google Scholar 

  13. Lu B, Oyekan J, Gu D, Hu H, Nia H F G. Mobile sensor networks for modelling environmental pollutant distribution. International Journal of Systems Science, 2011, 42, 1491–1505.

    Article  MathSciNet  MATH  Google Scholar 

  14. Schwager M, Slotine J, Rus D. Decentralized adaptive control for coverage with networked robots. Proceedings of the IEEE International Conference on Robotics and Automation, Roma, Italy, 2007, 3289–3294.

    Google Scholar 

  15. Schwager M, McLurkin J, Rus D. Distributed coverage control with sensory feedback for networked robots. The conference on Robotics: Science and Systems II, Philadelphia USA, 2006.

    Google Scholar 

  16. Williams R K, Sukhatme G S. Probabilistic spatial mapping and curve tracking in distributed multi-agent systems. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), St Paul, USA, 2012, 1125–1130.

    Google Scholar 

  17. Metia S, Oduro S D, Ha Q P, Duc H, Azzi M. Environmental time series analysis and estimation with extended kalman filtering. Proceedings of the IEEE 1st International Conference on Artificial Intelligence, Modelling and Simulation (AIMS), Kota Kinabalu, Malaysia, 2013, 235–240.

    Google Scholar 

  18. Carron A, Todescato M, Carli R, Schenato L, Pillonetto G. Multi-agents adaptive estimation and coverage control using Gaussian regression. Proceedings of the IEEE European Control Conference (ECC), Linz, Austria, 2015, 2490–2495.

    Google Scholar 

  19. Wikle C K, Hooten M B. A general science-based framework for dynamical spatio-temporal models. TEST, 2010, 19, 417–451.

    Article  MathSciNet  MATH  Google Scholar 

  20. Berg H C, Purcell E M. Physics of chemoreception. Journal of Biophysics, 1977, 20, 193–219.

    Article  Google Scholar 

  21. Brown D A, Berg H C. Temporal stimulation of chemotaxis in Escherichia coli. Proceedings of the National Academy of Sciences of the United States of America, 1974, 71, 1388–1392.

    Article  Google Scholar 

  22. Passino K M. Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Systems Magazine, 2002, 22, 52–67.

    Article  Google Scholar 

  23. Oyekan J, Hu H, Gu D. Exploiting bacterial swarms for optimal coverage of dynamic pollutant profiles. Proceedings of the IEEE International Conference on Robotics and Biomimetics, Tianjin, China, 2010, 1692–1697.

    Google Scholar 

  24. Oyekan J, Hu H. Towards autonomous patrol behaviours for UAVs. Proceedings of UK EPSRC Workshop on Human Adaptive Mechatronics, Stafford, UK, 2009, 15–16.

    Google Scholar 

  25. Kuznetsov S, Davis S N, Paulos E, Gross M D, Cheung J C. Red balloon, green balloon sensors in the sky. Proceedings of the ACM 13th International Conference on Ubiquitous Computing, Beijing, China, 2011, 237–246.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Aerospace, Transport and Manufacturing, Cranfield University, College Road, Cranfield, MK43 0AL, UK

    John Oluwagbemiga Oyekan

  2. School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK

    Dongbing Gu & Huosheng Hu

Authors
  1. John Oluwagbemiga Oyekan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dongbing Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Huosheng Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to John Oluwagbemiga Oyekan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Oyekan, J.O., Gu, D. & Hu, H. Tracking and sensor coverage of spatio-temporal quantities using a swarm of artificial foraging agents. J Bionic Eng 13, 679–689 (2016). https://doi.org/10.1016/S1672-6529(16)60339-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1016/S1672-6529(16)60339-6

Keyword

Search

Navigation