Skip to main content
SpringerLink
Log in

Efficient complete coverage of a known arbitrary environment with applications to aerial operations

  • Published:
Autonomous Robots Aims and scope Submit manuscript

Abstract

The problem of coverage of known space arises in a multitude of domains, including search and rescue, mapping, and surveillance. In many of these applications, it is desirable or even necessary for the solution to guarantee both the complete coverage of the free space, as well as the efficiency of the generated trajectory in terms of distance traveled. A novel algorithm is introduced, based on the boustrophedon cellular decomposition technique, for computing an efficient complete coverage path for a known environment populated with arbitrary obstacles. This hierarchical approach first partitions the space to be covered into non-overlapping cells, then solves the Chinese postman problem to compute an Eulerian circuit traversing through these cells, and finally concatenates per-cell seed spreader motion patterns into a complete coverage path. Practical considerations of the coverage system are also explored for operations with a non-holonomic aerial vehicle. The effects of various system parameters are evaluated in controlled environments using a high-fidelity flight simulator, in addition to over 200 km of in-field flight sessions with a fixed-wing unmanned aerial vehicle.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Notes

  1. www.procerusuav.com.

References

  • Acar, E. U., Choset, H., Rizzi A. A., Atkar P. N., & Hull D. (2002). Morse decompositions for coverage tasks. The International Journal of Robotics Research (IJRR ’02), 21(4), 331–344 (2002).

    Google Scholar 

  • Acar, E. U., & Choset, H. (2002). Sensor-based coverage of unknown environments: Incremental construction of morse decompositions. The International Journal of Robotics Research (IJRR ’02), 21(4), 345–366.

    Article  Google Scholar 

  • Acar, E. U., Choset, H., Zhang, Y., & Schervish, M. (2003). Path planning for robotic demining: Robust sensor-based coverage of unstructured environments and probabilistic methods. The International Journal of Robotics Research (IJRR ’03), 22, 441–466.

    Article  Google Scholar 

  • Agarwal, A., Hiot, L., Nghia, N., & Joo, E. (2006). Parallel region coverage using multiple UAVs. In IEEE Aerospace Conference (p. 8). Big Sky, MT.

  • Agmon, N., Hazon, N., & Kaminka, G. (2008). The giving tree: Constructing trees for efficient offline and online multi-robot coverage. Annals of Mathematics and Artificial Intelligence, 52, 143–168.

    Article  MATH  MathSciNet  Google Scholar 

  • Ahmadzadeh, A., Jadbabaie, A., Kumar, V., & Pappas, G. (2006a). Multi-UAV cooperative surveillance with spatio-temporal specifications (pp. 5293–5298). San Diego, CA: Proceedings of the 45th IEEE Conference on Decision and Control.

  • Ahmadzadeh, A., Keller, J., Jadbabaie, A., & Kumar, V. (2006b). An optimization-based approach to time critical cooperative surveillance and coverage with unmanned aerial vehicles. Rio de Janeiro: International Symposium on Experimental Robotics.

    Google Scholar 

  • Aviones. (2013). UAV Flight Simulator. Retrieved June 6, 2013 from http://aviones.sourceforge.net.

  • Brightwell, G., & Winkler, P. (2004). Note on counting Eulerian circuits. CoRR cs.CC/0405067.

  • Butler, Z. (1998). CC R : A complete algorithm for contact-sensor based coverage of rectilinear environments. Technical Report. CMU-RI-TR-98-27. Pittsburgh, PA: The Robotics Institute, Carnegie Mellon University.

  • Cheng, P., Keller, J., & Kumar, V. (2008). Time-optimal UAV trajectory planning for 3D urban structure coverage (pp. 2750–2757). Nice: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS ’08).

  • Choset, H. (2000). Coverage of known spaces: The boustrophedon cellular decomposition. Autonomous Robots, 9, 247–253.

    Article  Google Scholar 

  • Choset, H. (2001). Coverage for robotics—a survey of recent results. Annals of Mathematics and Artificial Intelligence, 31, 113–126.

    Article  Google Scholar 

  • Choset, H., & Burdick, J. (1995). Sensor based planning, part ii: Incremental construction of the generalized voronoi graph. In Proceedings of the IEEE conference on robotics and automation (ICRA ’95) (pp. 1643–1648). Los Alamitos, CA: IEEE Computer Society Press.

  • Choset, H., & Pignon, P. (1997). Coverage path planning: The boustrophedon cellular decomposition. Leuven: Proceedings of the International Conference on Field and Service Robotics.

    Google Scholar 

  • Choset, H., Lynch, K. M., Hutchinson, S., Kantor, G., Burgard, W., Kavraki, L. E., et al. (2005). Principles of robot motion: Theory, algorithms, and implementations. Boston: MIT Press.

    Google Scholar 

  • Cortes, J., Martinez, S., Karatas, T., & Bullo, F. (2004). Coverage control for mobile sensing networks. IEEE Transactions on Robotics, 20(2), 243–255.

    Article  Google Scholar 

  • DasGupta, B., Hespanha, J., Riehl, J., & Sontag, E. (2006). Honey-pot constrained searching with local sensory information. Nonlinear Analysis, 65(9), 1773–1793.

    Article  MATH  MathSciNet  Google Scholar 

  • Easton, K., & Burdick, J. (2005). A coverage algorithm for multi-robot boundary inspection (pp. 727–734). Barcelona: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA ’05).

  • Edmonds, J., & Johnson, E. L. (1973). Matching, Euler tours and the Chinese postman. Mathematical Programming, 5, 88–124.

    Article  MATH  MathSciNet  Google Scholar 

  • Fazli, P., Davoodi, A., Pasquier, P., & Mackworth, A. (2010). Complete and robust cooperative robot area coverage with limited range (pp. 5577–5582). Taipei: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS ’10).

  • Fomenko, A., & Kunii, T. L. (1997). Topological modeling for visualization. Tokyo: Springer-Verlag.

    Book  MATH  Google Scholar 

  • Forman, R. (1998). Morse theory for cell complexes. Advances in Mathematics, 134, 90145.

    Article  MathSciNet  Google Scholar 

  • Furukawa, T., Bourgault, F., Lavis, B., & Durrant-Whyte, H. (2006). Recursive Bayesian search-and-tracking using coordinated UAVs for lost targets. Orlando, FL: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA ’06).

  • Gabriely, Y., & Rimon, E. (2001). Spanning-tree based coverage of continuous areas by a mobile robot. Seoul: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA ’01).

  • Gabriely, Y., & Rimon, E. (2002). Spiral-stc: An on-line coverage algorithm of grid environments by a mobile robot (pp. 954–960). Washington, D.C: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA ’02).

    Google Scholar 

  • Gaudiano, P., Shargel, B., Bonabeau, E., & Clough, B. T. (2003). Swarm intelligence: A new C2 paradigm with an application to control of swarms of UAVs. Copenhagen: ICCRTS Command and Control Symposium.

    Google Scholar 

  • Girdhar, Y., Xu, A., Dey, B. B., Meghjani, M., Shkurti, F., Rekleitis, I., & Dudek, G. (2011). MARE: Marine Autonomous Robotic Explorer (pp. 5048–5053). Algarve: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS ’11).

  • Guan, M.-K. (1962). Graphic programming using odd or even points. Chinese Mathematics, 1(3), 273–277.

    Google Scholar 

  • Howard, A., Matarić, M. J., & Sukhatme, G. S. (2002). Mobile sensor network deployment using potential fields: A distributed, scalable solution to the area coverage problem (pp. 299–308). Fukuoka: Proceedings of the International Symposium on Distributed Autonomous Robotic Systems.

  • Huang, W. (2001). Optimal line-sweep-based decompositions for coverage algorithms (pp. 27–32). Seoul: Proceedings the IEEE International Conference on Robotics and Automation (ICRA ’01).

  • Jimenez, P., Shirinzadeh, B., Nicholson, A., & Alici, G. (2007). Optimal area covering using genetic algorithms (pp. 1–5). Zurich: Proceedings of the IEEE/ASME International Conference on Advanced Intelligent, Mechatronics.

  • Kang, J. W., Kim, S. J., Chung, M. J., Myung, H., Park, J. H., & Bang, S. W. (2007). Path planning for complete and efficient coverage operation of mobile robots (pp. 2126–2131). Harbin: Proceedings of the IEEE International Conference on Mechatronics and Automation (ICMA ’07).

  • Lumelsky, V. J., Mukhopadhyay, S., & Sun, K. (1990). Dynamic path planning in sensor-based terrain acquisition. IEEE Transactions on Robotics and Automation, 6(4), 462–472.

    Article  Google Scholar 

  • Mannadiar, R., & Rekleitis, I. (2010). Optimal coverage of a known arbitrary environment (pp. 5525–5530). Anchorage: Proceedings of IEEE International Conference on Robotics and Automation (ICRA ’10).

  • Martinez, S., Cortes, J., & Bullo, F. (2007). Motion coordination with distributed information. IEEE Control Systems Magazine, 27(4), 75–88.

    Article  Google Scholar 

  • Maza, I., & Ollero, A. (2007). Multiple UAV cooperative searching operation using polygon area decomposition and efficient coverage algorithms. In Distributed Autonomous Robotic Systems 6 (pp. 221–230). Japan: Springer.

  • Meger, D., Rekleitis, I., & Dudek, G. (2008). Heuristic search planning to reduce exploration uncertainty (pp. 3382–3399). Nice: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS ’08).

  • Paull, L., Saeedi, S., Li, H., & Myers, V. (2010). An information gain based adaptive path planning method for an autonomous underwater vehicle using sidescan sonar (pp. 835–840). Toronto, ON: Proceedings of the IEEE International Conference on Automation Science and Engineering (CASE ’10).

  • Rekleitis, I. M., Dudek, G., & Milios, E. (2001). Multi-robot collaboration for robust exploration. Annals of Mathematics and Artificial Intelligence, 31(1–4), 7–40.

    Article  Google Scholar 

  • Rekleitis, I. M., New, A. P., Rankin, E. S., & Choset, H. (2008). Efficient multi-robot coverage: An algorithmic approach. Annals of Mathematics and Artificial Intelligence, 52(2–4), 109–142.

    Article  MATH  MathSciNet  Google Scholar 

  • Schwager, M., Slotine, J. J., & Rus, D. (2009). Unifying geometric, probabilistic, and potential field approaches to multi-robot coverage control. Lucerne: Proceedings of the IEEE International Symposium on Robotics Research (ISRR ’09) (2009).

  • Shkurti, F., Xu, A., Meghjani, M., Higuera, J. C. G., Girdhar, Y., Giguère, P., Dey, B. B., Li, J., Kalmbach, A., Prahacs, C., Turgeon, K., Rekleitis, I., & Dudek, G. (2012). Multi-domain monitoring of marine environments using a heterogeneous robot team. Algarve: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS ’12).

  • Weiss-Cohen, M., Sirotin, I., & Rave, E. (2008). Lawn mowing system for known areas (pp. 539–544). Vienna: Proceedings of the International Conference on Computational Intelligence for Modelling Control and Automation.

  • Xu, A., & Dudek, G. (2010). A vision-based boundary following framework for aerial vehicles (pp. 81–86). Algarve: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS ’10).

  • Xu, A., Viriyasuthee, C., & Rekleitis, I. (2011). Optimal complete terrain coverage using an unmanned aerial vehicle (pp. 2513–2519). Anchorage: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA ’11).

  • Yao, Z. (2006). Finding efficient robot path for the complete coverage of a known space (pp. 3369–3374). Orlando, FL: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA ’06).

  • Zheng, X., Jain, S., Koenig, S., & Kempe, D. (2005). Multi-robot forest coverage (pp. 3852–3857). Edmonton, AB: Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS ’05).

Download references

Acknowledgments

We would like to thank both Microsoft Research and the National Science and Engineering Research Council of Canada (NSERC) for their generous financial support towards this work. We also would like to thank Prof. D. Avis for the useful discussions on graph theory and the CPP.

Author information

Authors and Affiliations

  1. School of Computer Science, McGill University, Montreal, QC, Canada

    Anqi Xu, Chatavut Viriyasuthee & Ioannis Rekleitis

Authors
  1. Anqi Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chatavut Viriyasuthee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ioannis Rekleitis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ioannis Rekleitis.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Xu, A., Viriyasuthee, C. & Rekleitis, I. Efficient complete coverage of a known arbitrary environment with applications to aerial operations. Auton Robot 36, 365–381 (2014). https://doi.org/10.1007/s10514-013-9364-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10514-013-9364-x

Keywords

Search

Navigation