Skip to main content
Log in

Optimizing coverage performance of multiple random path-planning robots

  • Research Article
  • Published:
Paladyn

Abstract

This paper presents a new approach to the multi-agent coverage path-planning problem. An efficient multi-robot coverage algorithm yields a coverage path for each robot, such that the union of all paths generates an almost full coverage of the terrain and the total coverage time is minimized. The proposed algorithm enables multiple robots with limited sensor capabilities to perform efficient coverage on a shared territory. Each robot is assigned to an exclusive route which enables it to carry out its tasks simultaneously, e.g., cleaning assigned floor area with minimal path overlapping. It is very difficult to cover all free space without visiting some locations more than once, but the occurrence of such events can be minimized with efficient algorithms. The proposed multi-robot coverage strategy directs a number of simple robots to cover an unknown area in a systematic manner. This is based on footprint data left by the randomized path-planning robots previously operated on that area. The developed path-planning algorithm has been applied to a simulated environment and robots to verify its effectiveness and performance in such an application.

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

  1. H. Choset. “Coverage for robotics — A survey of recent results”. In Annals of Mathematics and Artificial Inteligence, vol. 31, no. 1–4, pp. 113–126, Oct. 2001.

    Article  Google Scholar 

  2. D. Latimer-IV, S. Srinivasa, V. Lee-Shue, S. S. Sonne, H. Choset and A. Hurst. “Toward sensor based coverage with robot teams”. In Proc. 2002 IEEE International conference on Robotics & Automation.

  3. C. Luo and S. Yang. “A real-time cooperative sweeping strategy for multiple cleaning robots”. In IEEE International Symposium on Inteligent Control, 660–667, 2002

  4. D. J. Bruemmer, D. D. Dudenhoe er, M. O. Anderson, and M.D. Mckay. ”A robotic swarm for spill finding and perimeter formation”. In Spectrum, 2002

  5. Z. J. Butler; A.A. Rizzi; R.L. Hollis. “Cooperative coverage of rectilinear environments”. In Procedings of IEEE International Conference on Robotics and Automation, Vol. 3, pp. 2722–2727, 24–28 April, 2000.

    Google Scholar 

  6. M. A. Batalin and G.S. Sukhatme. ”Spreading out: A local approach to multi-robot coverage”. In 6th International symposium on Distributed Autonomous Robotics Systems, Fukuoka, country-region Japan.

  7. J. Cortes; S. Martinez; T. Karatas; F. Bullo. “Coverage control for mobile sensing networks”. In IEEE Transactions on Robotics and Automation, Vol. 20,Issue 2, pp. 243–255, 2004.

    Article  Google Scholar 

  8. O. Burchan Bayazit, Jyh-Ming Lien, and Nancy M. Amato. “Better flocking behaviors in complex environments using global roadmaps”. In Procedings of the Workshop on Algorithmic Foundations of Robotics, December 2002.

  9. M. Jager and B. Nebel. “Dynamic decentralized area partitioning for cooperating cleaning robots”. In Procedings IEEE International Conference on Robotics and Automation, pp. 3577–3582, May 2002

  10. S. X. Yang, C. Luo, and Q. H. M. Meng. “Area-covering operation of a cleaning robot in a dynamic environment with unforeseen obstacles”. In Proc. of the IEEE Int. Symp. on Computational Inteligence in Robotics and Automation, July 2003, vol. 2, pp. 1034–1039.

    Article  Google Scholar 

  11. E. Acar and H. Choset. “Robust sensor-based coverage of unstructured environments”. In Proc. of the IEEE International Conference on Inteligent Robots and Systems (IROS’2001), Maui, Hawaii, 29–3 Oct./Nov. 2001, pp. 61–68

  12. H. Choset. ”Coverage of Known Spaces: The Boustrophedon Cellular Decomposition”. In Autonomous Robots, Volume 9, Number 3, 247–253.

  13. D.W. Gage. “Randomized search strategies with imperfect sensors”. In Procedings of Mobile Robots VII, Boston, State Mass., SPIE, 270–279, 1993

  14. IROBOT CORP. iRobot Create Owner’s Guide. 2006.

  15. T.E. Kurt. “Hacking Roomba”. Wiley Publishing, Indianapolis, Ind. 2006.

  16. C. Brom. “Hierarchical Reactive Planning: Where is its limit?” In Procedings of MNAS workshop. Edinburgh, Scotland, 2005.

  17. J. Xiao and Z. Michalewicz. “An Evolutionary Computation Approach to Robot Planning and Navigation”. In Hirota, K. and Fukuda, T. (eds.), Soft Computingin Mechatronics, Springer-Verlag, Heidelberg, Germany, pp. 117–128, 2000.

    Google Scholar 

  18. M. S. Ajmal Deen Ali; N. Babu and K. Varghese. “O ine Path Planning of cooperative manipulators using Co-Evolutionary Genetic Algorithm”. In Procedings of the International Symposium on Automation and Robotics in Construction, 19th (ISARC), pp. 415–124, 2002.

  19. S. Farritor and S. Dubowsky. “A Genetic Planning Method and its Application to Planetary Exploration”. In ASME Journal of Dynamic Systems, Measurementand Control, 124(4), 698–701, 2002

    Article  Google Scholar 

  20. J.A. Sauter; R. Matthews; H. V. D. Parunak, and S. Brueckner. “Evolving Adaptive Pheromone Path Planning Mechanisms”. In First International Conference on Autonomous Agents and Multi-Agent Systems, Bologna, Italy, 434–440, 2002.

  21. C.A. Coello Coello; D.A. Van Veldhuizen and G.B. Lamont. ”Evolutionary Algorithms for Solving Multi-Objective Problems”. Norwell, MA: Kluwer, 2002.

    MATH  Google Scholar 

  22. A.J. Chipperfield; R. Purshouse; P.J. Fleming; H. Thompson, and I. Griffin. ”Multi-objective optimisation in control system design: an evolutionary computing approach. In IFAC World Congres, Barcelona, 2002.

  23. K. Deb ”Multi-objective optimization using evolutionary algorithms”. New York; Chichester: Wiley, 2001

    MATH  Google Scholar 

  24. C.M. Fonseca and P.J. Fleming. “Genetic algorithms for multi-objective optimization: formulation, discussion and generalization”. In Proceding of the Fifth International Conference, San Mateo, CA, pp. 416–423, 1993

  25. C.M. Fonseca and P.J. Fleming. “Multiobjective optimization and multiple constraint handling with evolutionary algorithms-part I: A unified formulation”. In IEEE Transaction on Systems, Man and Cybernetics-part A: Systems and Humans, 28(1), pp. 26–37, 1998

    Article  Google Scholar 

  26. A.J. Chipperfield, and P.J. Fleming. “Multiobjective gas turbine engine controller design using genetic algorithms”. In IEEE Transaction on Industrial Electronics43(??), 583–587, 1996

    Article  Google Scholar 

  27. N. V. Dakev, J. F. Whidborne, A. J. Chipperfield and P. J. Fleming. “Evolutionary Hinfin design of an electromagnetic suspension control system for a maglev vehicle”. In Procedings of the Institution of Mechanical Enginers, Part I: Journal of Systems and Control Enginering, 1997, pp. 211: 345

  28. placel. A. Griffin, P. Schroder, A. J. Chipperfield and P. J. Fleming. ”Multi-objective optimization approach to the ALSTOM gasifier problem”. In Procedings of the Institution of Mechanical Enginers, Part I: Journal of Systems and Control Enginering, 2000, pp. 214: 453

  29. M.S. Alam and M.O. Tokhi. ”Designing feedforward command shapers with multi-objective genetic optimisation for vibration control of a single-link flexible manipulator, Engineering Applications of Artificial Intelligence”, 21(??), pp. 229–246, 2008

    Google Scholar 

  30. D. Housten and W. Regli. “Low-Cost Localization for Educational Robotic Platforms via an External Fixed-Position Camera”. AAAI Al Education Coloquium, 2008.

  31. M. A. Habib and T.N. Upal. “A novel methodology for indoor positioning”. In World Congres on Nature and Biologicaly Inspired Computing (NABIC 2009), Coimbatore, India, December 09–11, 2009

  32. A. Fournier and D.Y. Montuno. ”Triangulating Simple Polygons and Equivalent Problems.” In ACM Trans. Graphics 3, 153–174, 1984.

    Article  MATH  Google Scholar 

  33. D. Whitley; S. Timothy; D’Ann Fuquay. “Scheduling problems and traveling salesman: The genetic edge recombination operator”. In International Conference on Genetic Algorithms: 133–140. ISBN:1-55860-066-3, 1983

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Md Ahsan Habib.

About this article

Cite this article

Habib, M.A., Alam, M.S. & Siddique, N.H. Optimizing coverage performance of multiple random path-planning robots. Paladyn 3, 11–22 (2012). https://doi.org/10.2478/s13230-012-0012-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.2478/s13230-012-0012-5

Keywords

Navigation