Advertisement

Multi-robot online sensing strategies for the construction of communication maps

  • Alberto Quattrini LiEmail author
  • Phani Krishna Penumarthi
  • Jacopo Banfi
  • Nicola Basilico
  • Jason M. O’Kane
  • Ioannis Rekleitis
  • Srihari Nelakuditi
  • Francesco Amigoni
Article
  • 157 Downloads
Part of the following topical collections:
  1. Special Issue on Multi-Robot and Multi-Agent Systems

Abstract

This paper tackles the problem of constructing a communication map of a known environment using multiple robots. A communication map encodes information on whether two robots can communicate when they are at two arbitrary locations and plays a fundamental role for a multi-robot system deployment to reliably and effectively achieve a variety of tasks, such as environmental monitoring and exploration. Previous work on communication map building typically considered only scenarios with a fixed base station and designed offline methods, which did not exploit data collected online by the robots. This paper proposes Gaussian Process-based online methods to efficiently build a communication map with multiple robots. Such robots form a mesh network, where there is no fixed base station. Specifically, we provide two leader-follower online sensing strategies to coordinate and guide the robots while collecting data. Furthermore, we improve the performance and computational efficiency by exploiting prior communication models that can be built from the physical map of the environment. Extensive experimental results in simulation and with a team of TurtleBot 2 platforms validate the approach.

Keywords

Multi-robot systems Sensing strategies Communication maps 

Notes

References

  1. Amigoni, F., Basilico, N., & Quattrini Li, A. (2013). How much worth is coordination of mobile robots for exploration in search and rescue? In Chen, X., Stone, P., Sucar, L. E., & Zant, T. (eds) RoboCup 2012: Robot soccer world cup XVI, No. 7500 in Lecture Notes in Computer Science, Springer (pp. 106–117).Google Scholar
  2. Amigoni, F., Banfi, J., & Basilico, N. (2017). Multirobot exploration of communication-restricted environments: A survey. IEEE Intelligent Systems, 32(6), 48–57.CrossRefGoogle Scholar
  3. Amigoni, F., Banfi, J., Basilico, N., Rekleitis, I., & Quattrini Li, A. (2018). Online update of communication maps for exploring multirobot systems under connectivity constraints. In Proceedings of the distributed autonomous robotic systems (DARS).Google Scholar
  4. Bahl, P., & Padmanabhan, V. N. (2000), RADAR: An in-building RF-based user location and tracking system. In Proceedings of the IEEE international conference on computer communications (INFOCOM) (vol. 2, pp. 775–784).Google Scholar
  5. Banfi, J., Quattrini Li, A., Basilico, N., Rekleitis, I, & Amigoni, F. (2016). Asynchronous multirobot exploration under recurrent connectivity constraints. In Proceedings of the IEEE international conference on robotics and automation (ICRA) (pp. 5491–5498).Google Scholar
  6. Banfi, J., Quattrini Li, A., Basilico, N., Rekleitis, I., & Amigoni, F. (2017). Multirobot online construction of communication maps. In Proceedings of the IEEE international conference on robotics and automation (ICRA) (pp. 2577–2583).Google Scholar
  7. Best, G., Cliff, O., Patten, T., Mettu, R., & Fitch, R.(2018). Dec-MCTS: Decentralized planning for multi-robot active perception. The International Journal of Robotics Research To appear.Google Scholar
  8. Carpin, S., Stoyanov, T., & Nevatia, Y. (2006). Quantitative assessments of USARSim accuracy. In Proceeeding of PerMIS.Google Scholar
  9. Das, J., Py, F., Harvey, J. B. J., Ryan, J. P., Gellene, A., Graham, R., et al. (2015). Data-driven robotic sampling for marine ecosystem monitoring. The International Journal of Robotics Research, 34(12), 1435–1452.CrossRefGoogle Scholar
  10. Deisenroth, M., & Ng, J. (2015). Distributed gaussian processes. In Proceedings of the international conference on international conference on machine learning ICML (pp. 1481–1490).Google Scholar
  11. Dunbabin, M., & Marques, L. (2012). Robots for environmental monitoring: Significant advancements and applications. IEEE Robotics Automation Magazine, 19(1), 24–39.CrossRefGoogle Scholar
  12. Ferris, B., Fox, D., Lawrence, N.D. (2007). WiFi-SLAM using Gaussian process latent variable models. In Proceedings of the international joint conference on artificial intelligence (IJCAI) (pp. 2480–2485).Google Scholar
  13. Fink, J., & Kumar, V. (2010). Online methods for radio signal mapping with mobile robots. In Proceedings of the IEEE international conference on robotics and automation (ICRA) (pp. 1940–1945).Google Scholar
  14. Fink, J., Ribeiro, A., & Kumar, V. (2013). Robust control of mobility and communications in autonomous robot teams. IEEE Access, 1, 290–309.CrossRefGoogle Scholar
  15. Goldsmith, A. (2005). Wireless communications. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  16. GPy (2012) GPy: A Gaussian process framework in python. http://github.com/SheffieldML/GPy.
  17. Gregory, J., Fink, J. R., Stump, E., Twigg, J. N., Rogers, J. G., Baran, D., Fung, N., & Young, S. (2015). Application of multi-robot systems to disaster-relief scenarios with limited communication. In Proceedings of the international conference on field and service robotics (FSR) (pp. 639–653).Google Scholar
  18. Grisetti, G., Stachniss, C., & Burgard, W. (2007). Improved techniques for grid mapping with Rao–Blackwellized particle filters. IEEE Transactions on Robotics, 23(1), 34–46.CrossRefGoogle Scholar
  19. Hernandez, M., Li, H. B., Dotlić, I., & Miura, R. (2012). Reference channel models for proposals evaluation to TG8. In IEEE P802.15 working group for wireless personal area networks (WPANs) (pp. 1–20).Google Scholar
  20. Heurtefeux, K., & Valois, F. (2012). Is RSSI a good choice for localization in wireless sensor network? In Proceedings of the IEEE international conference on advanced information networking and applications (pp. 732–739).Google Scholar
  21. Hollinger, G. A., & Singh, S. (2012). Multirobot coordination with periodic connectivity: Theory and experiments. IEEE Transactions on Robotics, 28(4), 967–973.CrossRefGoogle Scholar
  22. Howard, A., & Roy, N. (2003). The robotics data set repository (Radish). http://radish.sourceforge.net/.
  23. Hsieh, M. A., Cowley, A., Kumar, V., & Taylor, C. J. (2008). Maintaining network connectivity and performance in robot teams: Research articles. Journal of Field Robotics, 25(1–2), 111–131.CrossRefGoogle Scholar
  24. Im, H. J., Lee, C. E., & Cho, Y. J. (2014). Radio mapping scheme using collective intelligent robots for teleoperation in unstructured environments. In Proceedings of the IEEE international symposium on robot and human interactive communication (pp. 856–861).Google Scholar
  25. Kemppainen, A., Haverinen, J., Vallivaara, I., & Rning, J. (2010). Near-optimal SLAM exploration in Gaussian processes. In IEEE conference on multisensor fusion and integration (pp. 7–13).Google Scholar
  26. Ladd, A. M., Bekris, K. E., Rudys, A., Kavraki, L. E., & Wallach, D. S. (2005). Robotics-based location sensing using wireless ethernet. Wireless Networks, 11(1–2), 189–204.CrossRefGoogle Scholar
  27. Lilienthal, A., Zell, A., Wandel, M., & Weimar, U. (2001). Sensing odour sources in indoor environments without a constant airflow by a mobile robot. In Proceedings of the IEEE international conference on robotics and automation (ICRA) (vol. 4, pp. 4005–4010).Google Scholar
  28. Lindh, M., & Johansson, K. H. (2013). Exploiting multipath fading with a mobile robot. The International Journal of Robotics Research, 32(12), 1363–1380.CrossRefGoogle Scholar
  29. Liu, T., & Cerpa, A.E. (2011). Foresee (4C): Wireless link prediction using link features. In Proceedings of the conference on information processing in sensor networks IPSN (pp. 294–305).Google Scholar
  30. Manjanna, S., Hansen, J., Quattrini Li, A., Rekleitis, I., & Dudek, G. (2017). Collaborative sampling using heterogeneous marine robots driven by visual cues. In Proceedings of the conference on computer and robot vision.Google Scholar
  31. Manjanna, S., Quattrini Li, A., Smith, R., Rekleitis, I., & Dudek, G. (2018). Heterogeneous multi-robot system for exploration and strategic water sampling. In Proceedings of the IEEE international conference on robotics and automation (ICRA).Google Scholar
  32. Marchant, R., Ramos, F., & Sanner, S. (2014). Sequential Bayesian optimisation for spatial-temporal monitoring. In Proceedings of the conference on uncertainty in artificial intelligence (UAI) (pp. 553–562).Google Scholar
  33. Mirowski, P., Ho, T.K., & Whiting, P. (2014). Building optimal radio-frequency signal maps. In Proceedings of the international conference on pattern recognition (pp. 978–983).Google Scholar
  34. Murphy, R. R., Tadokoro, S., & Kleiner, A. (2016). Disaster robotics. In Springer handbook of robotics (pp. 1577–1604).Google Scholar
  35. 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.CrossRefGoogle Scholar
  36. Penumarthi, P.K., Quattrini Li, A., Banfi, J., Basilico, N., Amigoni, F., O’Kane, J., Rekleitis, I., & Nelakuditi, S. (2017). Multirobot exploration for building communication maps with prior from communication models. In Proceedings of the international symposium on multirobot systems (MRS) (pp. 90–96).Google Scholar
  37. 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.CrossRefGoogle Scholar
  38. 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. In ICRA workshop on open source software.Google Scholar
  39. Rappaport, T. S. (1996). Wireless communications: Principles and practice. New Jersey: Prentice Hall.zbMATHGoogle Scholar
  40. Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian processes for machine learning. Cambridge: MIT Press.zbMATHGoogle Scholar
  41. Rekleitis, I., New, A., Rankin, E., & Choset, H. (2008). Efficient boustrophedon multi-robot coverage: An algorithmic approach. Annals of Mathematics and Artificial Intelligence, 52(2), 109–142.MathSciNetCrossRefzbMATHGoogle Scholar
  42. Riva, A., Banfi, J., Fanton, C., Basilico, N.,&Amigoni, F. (2018). A journey among pairs of vertices: Computing robots’ paths for performing joint measurements. In Proceedings of the international conference on autonomous agents and multi-agent systems (AAMAS) (pp. 229–237).Google Scholar
  43. Scholl, P. M., Kohlbrecher, S., Sachidananda, V., & Laerhoven, K. V. (2012). Fast indoor radio-map building for RSSI-based localization systems. In Proceedings of the international conference on networked sensing (INSS) (pp. 1–2).Google Scholar
  44. Singh, A., Krause, A., Guestrin, C., & Kaiser, W. (2009). Efficient informative sensing using multiple robots. Journal of Artificial Intelligence Research, 34, 707–755.MathSciNetCrossRefzbMATHGoogle Scholar
  45. Spirin, V., de Hoog, J., Visser, A., & Cameron, S. (2014). MRESim, a multi-robot exploration simulator for the rescue simulation league. In Proceedings of the RoboCup (pp. 106–117).Google Scholar
  46. Tuna, G., Gulez, K., & Gungor, V. C. (2013). The effects of exploration strategies and communication models on the performance of cooperative exploration. Ad Hoc Networks, 11(7), 1931–1941.CrossRefGoogle Scholar
  47. Twigg, J. N., Fink, J. R., Paul, L. Y., & Sadler, B. M. (2012). RSS gradient-assisted frontier exploration and radio source localization. In Proceedings of the IEEE international conference on robotics and automation (ICRA) (pp. 889–895).Google Scholar
  48. Vaughan, R. (2008). Massively multiple robot simulations in stage. Swarm Intelligence, 2(2–4), 189–208.CrossRefGoogle Scholar
  49. Yamauchi, B. (1998). Frontier-based exploration using multiple robots. In Proceedings of the international conference on autonomous agents (pp. 47–53).Google Scholar
  50. Zlot, R., Stentz, A., Dias, M. B., & Thayer, S. (2002). Multi-robot exploration controlled by a market economy. In Proceedings of the IEEE international conference on robotics and automation (ICRA) (pp. 3016–3023).Google Scholar
  51. Zvanovec, S., Pechac, P., & Klepal, M. (2003). Wireless LAN networks design: Site survey or propagation modeling? Radio Engineering, 12(4), 42–49.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Alberto Quattrini Li
    • 1
    Email author
  • Phani Krishna Penumarthi
    • 2
  • Jacopo Banfi
    • 3
  • Nicola Basilico
    • 4
  • Jason M. O’Kane
    • 2
  • Ioannis Rekleitis
    • 2
  • Srihari Nelakuditi
    • 2
  • Francesco Amigoni
    • 5
  1. 1.Department of Computer ScienceDartmouth CollegeHanoverUSA
  2. 2.Department of Computer Science and EngineeringUniversity of South CarolinaColumbiaUSA
  3. 3.Sibley School of Mechanical and Aerospace EngineeringCornell UniversityIthacaUSA
  4. 4.Department of Computer ScienceUniversity of MilanMilanoItaly
  5. 5.Artificial Intelligence and Robotics LaboratoryMilanoItaly

Personalised recommendations