Movatterモバイル変換


[0]ホーム

URL:


Skip to main content

Advertisement

Springer Nature Link
Log in

Strategies for coordinated multirobot exploration with recurrent connectivity constraints

  • Published:
Autonomous Robots Aims and scope Submit manuscript

Abstract

During several applications, such as search and rescue, robots must discover new information about the environment and, at the same time, share operational knowledge with a base station through anad hoc network. In this paper, we design exploration strategies that allow robots to coordinate with teammates to form such a network in order to satisfyrecurrent connectivity constraints—that is, data must be shared with the base station when making new observations at the assigned locations. Current approaches lack in flexibility due to the assumptions made about the communication model. Furthermore, they are sometimes inefficient because of the synchronous way they work: new plans are issued only once all robots have reached their goals. This paper introduces two novelasynchronous strategies that work with arbitrary communication models. In this paper, ‘asynchronous’ means that it is possible to issue new plans to subgroups of robots, when they are ready to receive them. First, we propose a single-stage strategy based on Integer Linear Programming for selecting and assigning robots to locations. Second, we design a two-stage strategy to improve computational efficiency, by separating the problem of locations’ selection from that of robot-location assignments. Extensive testing both in simulation and with real robots show that the proposed strategies provide good situation awareness at the base station while efficiently exploring the environment.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

Notes

References

  • Álvarez-Miranda, E., Ljubić, I., & Mutzel, P. (2013). The rooted maximum node-weight connected subgraph problem. InProceedings of CPAIOR (pp. 300–315). Springer.

  • Arkin, R., Diaz, J. (2002). Line-of-sight constrained exploration for reactive multiagent robotic teams. InProceedings of AMC, (pp. 455–461).

  • Banfi, J., Quattrini, Li, A., Basilico, N., Amigoni, F. (2015). Communication-constrained multirobot exploration: Short taxonomy and comparative results. InIROS Workshop on On-line decision-making in multi-robot coordination.

  • Banfi, J., Quattrini Li, A., Basilico, N., Amigoni, F., Rekleitis, I. (2016). Asynchronous multirobot exploration under recurrent connectivity constraints. InProceedings of ICRA, pp. 5491–5498.

  • Banfi, J., Quattrini Li, A., Basilico, N., Amigoni, F., Rekleitis, I. (2017). Multirobot online construction of communication maps. InProceedings of ICRA, pp. 2577–2583.

  • Basilico, N., & Amigoni, F. (2011). Exploration strategies based on multi-criteria decision making for searching environments in rescue operations.Autonomous Robots,31(4), 401–417.

    Article  Google Scholar 

  • Burkard, R., Dell’Amico, M., & Martello, S. (2009).Assignment problems (pp. 79–87). Society for Industrial and Applied Mathematics.

  • Chekuri, C., Korula, N., & Pál, M. (2012). Improved algorithms for orienteering and related problems.ACM Transactions on Algorithms,8(3), 23.

    Article MathSciNet MATH  Google Scholar 

  • Cheng, X., Du, D. Z., Wang, L., & Xu, B. (2008). Relay sensor placement in wireless sensor networks.Wireless Networks,14(3), 347–355.

    Article  Google Scholar 

  • Clausen, T., & Jacquet, P. (2003).Optimized link state routing protocol (OLSR) (p. 3626). RFC: Technical Report.

  • Garey, M., & Johnson, D. (1979).Computers and intractability: A guide to the theory of NP-completeness. W.H. Freeman.

  • Gurobi (2015). Gurobi optimizer reference manual.http://www.gurobi.com.

  • Hochbaum, D., Pathria, A. (1994). Node-optimal connected k-subgraphs. Manuscript, UC Berkeley, 1994.http://goo.gl/QoB8IB.

  • Hollinger, G., & Singh, S. (2012). Multirobot coordination with periodic connectivity: Theory and experiments.IEEE Transactions on Robotics,28(4), 967–973.

    Article  Google Scholar 

  • de Hoog, J., Cameron, S., Visser, A. (2009). Role-based autonomous multi-robot exploration. InProceedings of COGNITIVE, pp. 482–487.

  • Howard, A., Roy, N. (2003). The robotics data set repository (Radish).http://radish.sourceforge.net/.

  • Howard, A., Matarić, M., & Sukhatme, G. (2002). An incremental self-deployment algorithm for mobile sensor networks.Autonomous Robotots,13(2), 113–126.

    Article MATH  Google Scholar 

  • Jensen, EA., Lowmanstone, L., Gini, M. (2016). Communication-restricted exploration for search teams. InProceedings of DARS, (to appear).

  • Johnson, D. S., Minkoff, M., & Phillips, S. (2000). The prize collecting seiner tree problem: Theory and practice.Proceedings of SODA,1, 760–769.

    MATH  Google Scholar 

  • Julia, M., Gil, A., & Reinoso, Ó. (2012). A comparison of path planning strategies for autonomous exploration and mapping of unknown environments.Autonomous Robots,33(4), 427–444.

    Article  Google Scholar 

  • Keidar, M., & Kaminka, G. A. (2014). Efficient frontier detection for robot exploration.The International Journal of Robotics Research,33(2), 215–236.

    Article  Google Scholar 

  • Kimelfeld, B., Sagiv, Y. (2006). New algorithms for computing Steiner trees for a fixed number of terminals.http://researcher.ibm.com/researcher/files/us-kimelfeld/papers-steiner06.pdf

  • Lee, H. F., & Dooly, D. R. (1996). Algorithms for the constrained maximum-weight connected graph problem.Naval Research Logistics,43(7), 985–1008.

    Article MathSciNet MATH  Google Scholar 

  • Moss, A., & Rabani, Y. (2007). Approximation algorithms for constrained node weighted Steiner tree problems.SIAM Journal on Computing,37(2), 460–481.

    Article MathSciNet MATH  Google Scholar 

  • Mukhija, P., Krishna, K., & Krishna, V. (2010). A two phase recursive tree propagation based multi-robotic exploration framework with fixed base station constraint. InProceedings of IROS, pp. 4806–4811.

  • Nam, C., & Shell, D. A. (2015). Assignment algorithms for modeling resource contention in multirobot task allocation.IEEE Transactions on Automation Science and Engineering,12(3), 889–900.

    Article  Google Scholar 

  • Ochoa, S., & Santos, R. (2015). Human-centric wireless sensor networks to improve information availability during urban search and rescue activities.Information Fusion,22, 71–84.

    Article  Google Scholar 

  • O’Kane, J.M. (2013). A Gentle Introduction to ROS. Independently published, available athttp://www.cse.sc.edu/~jokane/agitr/.

  • Pei, Y., Mutka, M., & Xi, N. (2013). Connectivity and bandwidth-aware real-time exploration in mobile robot networks.Wireless Communications and Mobile Computing,13(9), 847–863.

    Article  Google Scholar 

  • Quattrini Li, A., Cipolleschi, R., Giusto, M., & Amigoni, F. (2016). A semantically-informed multirobot system for exploration of relevant areas in search and rescue settings.Autonomous Robots,40(4), 581–597.

    Article  Google Scholar 

  • Quigley, M., Gerkey, B., Conley, K., Faust, J., Foote, T., Leibs, J., Berger, E., Wheeler, R., & Ng, A. (2009). ROS: an open-source robot operating system. InICRA Workshop on Open Source Software.

  • Reich, J., Misra, V., Rubenstein, D., & Zussman, G. (2012). Connectivity maintenance in mobile wireless networks via constrained mobility.IEEE Journal of Selected Area in Communications,30(5), 935–950.

    Article  Google Scholar 

  • Rekleitis, I .(2013). Multi-robot simultaneous localization and uncertainty reduction on maps (MR-SLURM). InProceedings of ROBIO, pp .1216–1221.

  • Rooker, M., & Birk, A. (2007). Multi-robot exploration under the constraints of wireless networking.Control Engineering Practice,15(4), 435–445.

    Article  Google Scholar 

  • Simmons, R. G., Apfelbaum, D., Burgard, W., Fox ,D., Moors, M., Thrun, S., & Younes, H. L. S .(2000). Coordination for multi-robot exploration and mapping. InProceedings of AAAI, pp. 852–858.

  • Spirin, V., Cameron. S., & de Hoog, J. (2013). Time preference for information in multiagent exploration with limited communication. InProceedings of TAROS, pp. 34–45.

  • Stump, E., Michal, N., Kumar, V., & Isler, V. (2011). Visibility-based deployment of robot formations for communication maintenance. InProceedings of ICRA, pp. 4498–4505.

  • Tadokoro, S. (2010).Rescue Robotics. Springer.

  • Thrun, S. (2002). Robotic mapping: A survey. In: Exploring Artificial Intelligence in the New Millenium, Morgan Kaufmann, pp. 1–35.

  • Thrun, S., Burgard, W., & Fox, D. (2005).Probabilistic robotics. The MIT Press.

  • Tuna, G., Gulez, K., & Gungor, V. (2013). The effects of exploration strategies and communication models on the performance of cooperation exploration.Ad Hoc Networks,11(7), 1931–1941.

    Article  Google Scholar 

  • Visser, A., & Slamet, B. (2008). Including communication success in the estimation of information gain for multi-robot exploration. InProceedings of WiOpt, pp. 680–687.

  • Wurm, K. M., Stachniss, C., & Burgard, W. (2008). Coordinated multi-robot exploration using a segmentation of the environment. InProceedings of IROS, pp. 1160–1165.

  • Yamauchi, B. (1998) Frontier-based exploration using multiple robots. InProceedings of AGENTS, pp. 47–53.

  • Yu, J. (2016). Intractability of optimal multirobot path planning on planar graphs.IEEE RA-L,1(1), 33–40.

    MathSciNet  Google Scholar 

  • Zhang, Q., Whitney, D., Shkurti, F., & Rekleitis, I. (2014). Ear-based exploration on hybrid metric/topological maps. In Proceedings of IROS, pp 3081–3088.

Download references

Author information

Authors and Affiliations

  1. Artificial Intelligence and Robotics Laboratory, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milano, Italy

    Jacopo Banfi & Francesco Amigoni

  2. Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, USA

    Alberto Quattrini Li & Ioannis Rekleitis

  3. Department of Computer Science, University of Milan, Milano, Italy

    Nicola Basilico

Authors
  1. Jacopo Banfi

    You can also search for this author inPubMed Google Scholar

  2. Alberto Quattrini Li

    You can also search for this author inPubMed Google Scholar

  3. Ioannis Rekleitis

    You can also search for this author inPubMed Google Scholar

  4. Francesco Amigoni

    You can also search for this author inPubMed Google Scholar

  5. Nicola Basilico

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence toNicola Basilico.

Additional information

This is one of several papers published in Autonomous Robots comprising the Special Issue on Online Decision Making in Multi-Robot Coordination.

Rights and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Banfi, J., Quattrini Li, A., Rekleitis, I.et al. Strategies for coordinated multirobot exploration with recurrent connectivity constraints.Auton Robot42, 875–894 (2018). https://doi.org/10.1007/s10514-017-9652-y

Download citation

Keywords

Access this article

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Advertisement


[8]ページ先頭

©2009-2025 Movatter.jp