Advertisement

A Distributed Algorithm for Exploration of Unknown Environments with Multiple Robots

  • 167 Accesses

Abstract

In this paper, we present a complete algorithm for exploration of unknown environments containing disjoint obstacles with multiple robots. We propose a distributed approach considering several variants. The robots are modeled as points or discs, the obstacles are distinguishable or they are not distinguishable, the point robots only communicate at rendezvous, the disc-shaped robots can communicate if they are visible to each other, finally the free subset of the configuration space has one or several connected components. Two possible applications of our algorithms are: 1) Search of a static object in an unknown environment. 2) Damage verification in unknown environments composed by multiple elements (e.g. buildings). The main contributions of this work are the following: 1) The algorithms guarantee exploring the whole environment in finite time even though the robots are no capable of building an exact map of the environment, they cannot estimate their positions and each robot does not have full information about the part of the environment explored by other robots. 2) The method only requires limited communication between the robots. 3) We combine and extend the velocity obstacle method with our proposed approach to explore the environment using disc-shaped robots that are able to avoid collisions with both moving and static obstacles. 4) We propose an exploration strategy such that even if the configuration space has several connected components this strategy guarantees covering the largest possible portion of the environment with an omnidirectional sensor detecting the visibility regions. 5) The algorithm scales well to hundreds of robots and obstacles. We tested in several simulations the performance of our algorithms using different performance metrics.

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

Access options

Buy single article

Instant unlimited access to the full article PDF.

US$ 39.95

Price includes VAT for USA

Subscribe to journal

Immediate online access to all issues from 2019. Subscription will auto renew annually.

US$ 199

This is the net price. Taxes to be calculated in checkout.

References

  1. 1.

    Schwartz, J.T., Sharir, M.: On the piano movers’ problem: III. Coordinating the motion of several independent bodies: The special case of circular bodies moving amidst polygonal barriers. Int. J. Robot. Res. 2(3), 46–75 (1983)

  2. 2.

    van den Berg, J., Lin, M., Manocha, D.: Reciprocal velocity obstacles for real-time multi-agent navigation. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 1928–1935 (2008)

  3. 3.

    Snape, J., van den Berg, J., Guy, S.J., Manocha, D.: The hybrid reciprocal velocity obstacle. IEEE Trans. Robot. 27(4), 696–706 (2011)

  4. 4.

    Laguna, G., Murrieta-Cid, R., Becerra, H.M., Lopez-Padilla, R., LaValle, S.M.: Exploration of an unknown environment with a differential drive disc robot. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 2527–2533 (2014)

  5. 5.

    Burgard, W., Moors, M., Stachniss, C., Schneider, F.: Coordinated Multi-Robot exploration. IEEE Trans. Robot. 21(3), 376–386 (2005)

  6. 6.

    Howard, A., Matariċ, M., Sukhatme, G.: Mobile Sensor Network Deployment using Potential Fields: A Distributed, Scalable Solution to the Area Coverage Problem. In: Distributed Autonomous Robotic Systems, vol. 5, pp. 299–308 (2002)

  7. 7.

    Cortės, J., Martínez, S., Karatas, T., Bullo, F.: Coverage control for mobile sensing networks. IEEE Trans. Robot. Autom. 20(2), 243–255 (2004)

  8. 8.

    Taylor, C.J., Kriegman, D.: Vision-based motion planning and exploration algorithms for mobile robots. IEEE Trans. Robot. Autom. 14(3), 417–426 (1998)

  9. 9.

    Sarmiento, A., Murrieta-Cid, R., Hutchinson, S.: Planning Expected-time Optimal Paths for Searching Known Environments. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 872–878 (2004)

  10. 10.

    Tovar, B., Murrieta-Cid, R., LaValle, S.M.: Distance-Optimal Navigation in an unknown environment without sensing distances. IEEE Trans. Robot. 23(4), 506–518 (2007)

  11. 11.

    Park, H., Hutchinson, S.: An efficient algorithm for fault-tolerant rendezvous of multi-robot systems with controllable sensing range. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 358–365 (2016)

  12. 12.

    Yamauchi, B.: A frontier-based approach for autonomous exploration. In: Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, pp. 146–151 (1997)

  13. 13.

    Rekletis, I.M., Dudek, G., Milos, E.E.: Graph-based exploration using multiple robots. In: Distributed Autonomous Robotic Systems, vol. 4, pp. 241–250 (2000)

  14. 14.

    Wurm, K.M., Stachniss, C., Burgard, W.: Coordinated multi-robot exploration using a segmentation of the environment. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1160–1165 (2008)

  15. 15.

    Matignon, L., Laurent, J., Abdel-Illan, M.: Distributed value functions for multi-robot exploration. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 1544–1550 (2012)

  16. 16.

    Amigoni, F.: Experimental evaluation of some exploration strategies for mobile robots. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 2818–2823 (2008)

  17. 17.

    Lee, S.K., Fekete, S., McLurkin, J.: Structured triangulation in multi-robot systems Coverage, patrolling, Voronoi partitions, and geodesic centers. Int. J. Robot. Res. 35(10), 1234–1260 (2016)

  18. 18.

    Ng, J: Thomas bräunl. Performance Comparison of Bug Navigation Algorithms. J. Intell. Robot. Syst. 50, 73—84 (2007)

  19. 19.

    Fiorini, P., Shiller, Z.: Motion planning in dynamic environments using velocity obstacles. Int. J. Robot. Res. 17(7), 760–772 (1998)

  20. 20.

    Kluge, B.: Recursive agent modeling with probabilistic velocity obstacles for mobile robot navigation among humans. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 376–380 (2003)

  21. 21.

    van den Berg, J., Guy, S., Lin, M., Manocha, D.: Reciprocal n-Body Collision Avoidance. Robotics Research. In: Pradalier, C., Siegwart, R., Hirzinger, G. (eds.) Springer Tracts in Advanced Robotics, vol. 70, pp 3–19. Springer (2011)

  22. 22.

    Bravo, L., Ruiz, U., Murrieta-Cid, R., Aguilar, L., Chavez, E.: A distributed exploration algorithm for unknown environments with multiple obstacles by multiple robots. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4460–4466 (2017)

  23. 23.

    Cormen, T.H., Leiserson, C., Rivest, R.L., Stein, C.: Introduction to Algorithms, 2nd edn. MIT Press, Cambridge (2002)

  24. 24.

    Kleiner, A., Prediger, J., Nebel, B.: RFID Technology-based exploration and SLAM for search and rescue. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4054—4059 (2006)

Download references

Author information

Correspondence to Ubaldo Ruiz.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This work was partially funded by CONACYT projects 220796 and 264896. The authors would also like to acknowledge the financial support of Intel Corporation for the development of this work.

Electronic supplementary material

Below is the link to the electronic supplementary material.

(MP4 14.0 MB)

(MP4 14.0 MB)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Aguilar, G., Bravo, L., Ruiz, U. et al. A Distributed Algorithm for Exploration of Unknown Environments with Multiple Robots. J Intell Robot Syst 95, 1021–1040 (2019). https://doi.org/10.1007/s10846-018-0939-9

Download citation

Keywords

  • Multi-robot
  • Exploration
  • Distributed algorithm
  • Collision avoidance