Skip to main content
Log in

Search and rescue with sparsely connected swarms

  • Published:
Autonomous Robots Aims and scope Submit manuscript

Abstract

Designing and deploying autonomous swarms capable of performing collective tasks in real-world is extremely challenging. One drawback of getting out of the lab is that realistic tasks involve long distances with limited numbers of robots, leading to sparse and intermittent connectivity. As an example, search and rescue requires robots to coordinate in their search, and relay the information of found targets. The search’s effectiveness is greatly reduced if robots must stay close to maintain connectivity. This paper proposes a decentralized search system that only requires sporadic connectivity and allows information diffusion through the swarm whenever possible. Our robots share and update a distributed belief map, to coordinate the search. Once a target is detected, the robots form a communication relay between a base station and the target’s position. We show the applicability of our system both in simulation and with real-world experiments with a small swarm of drones.

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.

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

References

  • Alotaibi, E. T., Alqefari, S. S., & Koubaa, A. (2019). LSAR: Multi-UAV collaboration for search and rescue missions. IEEE Access, 7, 55817–55832.

    Article  Google Scholar 

  • Andries, M., & Charpillet, F. (2013). Multi-robot exploration of unknown environments with identification of exploration completion and post-exploration rendezvous using ant algorithms. In 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 5571–5578). IEEE.

  • Andries, M., & Charpillet, F. (2015). Multi-robot taboo-list exploration of unknown structured environments. In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 5195–5201). IEEE.

  • Apvrille, L., Tanzi, T., & Dugelay, J. L. (2014). Autonomous drones for assisting rescue services within the context of natural disasters. In 2014 XXXIth URSI General Assembly and Scientific Symposium (URSI GASS) (pp. 1–4). IEEE.

  • Banfi, J., Quattrini Li, A., Rekleitis, I., et al. (2018). Strategies for coordinated multirobot exploration with recurrent connectivity constraints. Autonomous Robots, 42(4), 875–894.

    Article  Google Scholar 

  • Belkadi, A., Ciarletta, L., & Theilliol, D. (2016). UAVS fleet control design using distributed particle swarm optimization: A leaderless approach. In 2016 International Conference on Unmanned Aircraft Systems (ICUAS) (pp. 364–371). IEEE.

  • Brambilla, M., Ferrante, E., Birattari, M., et al. (2013). Swarm robotics: A review from the swarm engineering perspective. Swarm Intelligence, 7(1), 1–41.

    Article  Google Scholar 

  • Çeltek, S. A., Durdu, A., & Kurnaz, E. (2018). Design and simulation of the hierarchical tree topology based wireless drone networks. In 2018 International Conference on Artificial Intelligence and Data Processing (IDAP) (pp. 1–5). IEEE, https://doi.org/10.1109/IDAP.2018.8620755

  • Cesare, K., Skeele, R., Yoo, S. H., et al. (2015). Multi-uav exploration with limited communication and battery. In 2015 IEEE International Conference on Robotics and Automation (ICRA) (pp. 2230–2235). IEEE.

  • De Hoog, J., Cameron, S., & Visser, A. (2009). Role-based autonomous multi-robot exploration. In 2009 Computation World: Future Computing, Service Computation, Cognitive, Adaptive, Content, Patterns (pp. 482–487). IEEE.

  • Dimidov, C., Oriolo, G., & Trianni, V. (2016). Random walks in swarm robotics: an experiment with Kilobots. In International Conference on Swarm Intelligence (pp. 185–196). Springer.

  • Dorigo, M., Floreano, D., Gambardella, L. M., et al. (2013). Swarmanoid: A novel concept for the study of heterogeneous robotic swarms. IEEE Robotics & Automation Magazine, 20(4), 60–71.

    Article  Google Scholar 

  • Gerkey, B. P., & Mataric, M. J. (2002). Sold!: Auction methods for multirobot coordination. IEEE Transactions on Robotics and Automation, 18(5), 758–768.

    Article  Google Scholar 

  • Hentati, A. I., & Fourati, L. C. (2020). Comprehensive survey of UAVS communication networks. Computer Standards & Interfaces, 72, 103,451.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Hourani, H., Hauck, E., & Jeschke, S. (2013). Serendipity rendezvous as a mitigation of exploration’s interruptibility for a team of robots. In 2013 IEEE International Conference on Robotics and Automation (pp. 2984–2991). IEEE.

  • JASP Team (2021). JASP (Version )[Computer software]. URL https://jasp-stats.org/.

  • Khan, A., Yanmaz, E., & Rinner, B. (2014). Information merging in multi-uav cooperative search. In 014 IEEE international conference on robotics and automation (ICRA) (pp. 3122–3129). IEEE.

  • Kim, J., Ladosz, P., & Oh, H. (2020). Optimal communication relay positioning in mobile multi-node networks. Robotics and Autonomous Systems, 129, 103517. https://doi.org/10.1016/j.robot.2020.103517, URL www.sciencedirect.com/science/article/pii/S0921889019309145.

  • Kiran, K., Kaushik, N., Sharath, S., et al. (2018). Experimental evaluation of batman and batman-adv routing protocols in a mobile testbed. In TENCON 2018-2018 IEEE Region 10 Conference (pp. 1538–1543). IEEE.

  • Kobayashi, F., Sakai, S., & Kojima, F. (2002). Sharing of exploring information using belief measure for multi robot exploration. In 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE’02. Proceedings (Cat. No. 02CH37291) (pp. 1544–1549). IEEE.

  • Kobayashi, F., Sakai, S., & Kojima, F. (2003). Determination of exploration target based on belief measure in multi-robot exploration. In Proceedings 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation. Computational Intelligence in Robotics and Automation for the New Millennium (Cat. No. 03EX694) (pp. 1545–1550). IEEE.

  • Li, J. (2019). Throughput-aware flying communication relay network for disaster area search and rescue. In Proceedings of the 2019 8th International Conference on Networks, Communication and Computing (pp. 138–141), https://doi.org/10.1145/3375998.3376038.

  • Majcherczyk, N., Jayabalan, A., Beltrame, G., et al. (2018). Decentralized connectivity-preserving deployment of large-scale robot swarms. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 4295–4302). IEEE.

  • McGuire, K., De Wagter, C., Tuyls, K., et al. (2019). Minimal navigation solution for a swarm of tiny flying robots to explore an unknown environment. Science Robotics, 4(35), eaaw9710.

    Article  Google Scholar 

  • Meghjani, M., & Dudek, G. (2012). Multi-robot exploration and rendezvous on graphs. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 5270–5276). IEEE.

  • Nickerson, J. V. (2004). Robots and humans reconvening. In 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No. 04CH37583) (pp. 2803–2808). IEEE.

  • Nouyan, S., & Dorigo, M. (2006). Chain based path formation in swarms of robots. In International Workshop on Ant Colony Optimization and Swarm Intelligence (pp. 120–131). Springer.

  • Pei, Y., Mutka, M. W., & 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 

  • Pinciroli, C., & Beltrame, G. (2016). Buzz: An extensible programming language for heterogeneous swarm robotics. In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 3794–3800). IEEE.

  • Pinciroli, C., Trianni, V., O’Grady, R., et al. (2012). Argos: A modular, parallel, multi-engine simulator for multi-robot systems. Swarm intelligence, 6(4), 271–295.

    Article  Google Scholar 

  • Pinciroli, C., Lee-Brown, A., & Beltrame, G. (2016). A tuple space for data sharing in robot swarms. In Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS) (pp. 287–294).

  • Quigley, M., Conley, K., Gerkey, B., et al. (2009). Ros: an open-source robot operating system. In ICRA Workshop on Open Source Software, (pp. 5). Kobe, Japan.

  • Rouček, T., Pecka, M., Čížek, P., et al. (2021). System for multi-robotic exploration of underground environments ctu-cras-norlab in the darpa subterranean challenge. arXiv preprint arXiv:2110.05911.

  • Ruetten, L., Regis, P. A., Feil-Seifer, D., et al. (2020). Area-optimized uav swarm network for search and rescue operations. In 2020 10th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 0613–0618). IEEE, https://doi.org/10.1109/CCWC47524.2020.9031197.

  • Shirsat, A., Elamvazhuthi, K., & Berman, S. (2020). Multi-robot target search using probabilistic consensus on discrete markov chains. In 2020 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) (pp. 108–115). IEEE.

  • Sperati, V., Trianni, V., & Nolfi, S. (2011). Self-organised path formation in a swarm of robots. Swarm Intelligence, 5(2), 97–119.

    Article  Google Scholar 

  • Spirin, V., & Cameron, S. (2014). Rendezvous through obstacles in multi-agent exploration. In 2014 IEEE International Symposium on Safety, Security, and Rescue Robotics (2014) (pp. 1–6). IEEE.

  • Spirin, V., Cameron, S., & Hoog, J. d. (2013). Time preference for information in multi-agent exploration with limited communication. In Conference Towards Autonomous Robotic Systems (pp. 34–45). Springer.

  • St-Onge, D., Varadharajan, V. S., Li, G., et al. (2017). Ros and buzz: consensus-based behaviors for heterogeneous teams. arXiv preprint arXiv:1710.08843.

  • St-Onge, D., Kaufmann, M., Panerati, J., et al. (2019). Planetary exploration with robot teams: Implementing higher autonomy with swarm intelligence. IEEE Robotics & Automation Magazine, 27(2), 159–168. https://doi.org/10.1109/MRA.2019.2940413

    Article  Google Scholar 

  • Stirling, T., Wischmann, S., & Floreano, D. (2010). Energy-efficient indoor search by swarms of simulated flying robots without global information. Swarm Intelligence, 4(2), 117–143.

    Article  Google Scholar 

  • Tarapore, D., Groß, R., & Zauner, K. P. (2020). Sparse robot swarms: moving swarms to real-world applications. Frontiers in Robotics and AI, 7, 83.

    Article  Google Scholar 

  • Varadharajan, V. S., St-Onge, D., Adams, B., et al. (2020). Swarm relays: Distributed self-healing ground-and-air connectivity chains. IEEE Robotics and Automation Letters, 5(4), 5347–5354.

    Article  Google Scholar 

  • Vielfaure, D., Arseneault, S., Lajoie, P. Y., et al. (2021). Dora: Distributed online risk-aware explorer. arXiv preprint arXiv:2109.14551.

  • Wellman, B. L., Dawson, S., de Hoog, J., et al. (2011). Using rendezvous to overcome communication limitations in multirobot exploration. In 2011 IEEE International Conference on Systems, Man, and Cybernetics (pp. 2401–2406). IEEE.

  • Wubben, J., Aznar, P., Fabra, F., et al. (2020). Toward secure, efficient, and seamless reconfiguration of uav swarm formations. In 2020 IEEE/ACM 24th International Symposium on Distributed Simulation and Real Time Applications (DS-RT) (pp. 1–7). IEEE.

  • Yamaguchi, S. P., Karolonek, F., Emaru, T., et al. (2017). Autonomous position control of multi-unmanned aerial vehicle network designed for long range wireless data transmission. In 2017 IEEE/SICE International Symposium on System Integration (SII) (pp. 127–132). IEEE, https://doi.org/10.1109/SII.2017.8279200

  • Zhang, H. G., Jin, G. Y., Qu, Y. X., et al. (2021). Servo relays as distributed controllable-mobility network to maintain long-term stable links for mobile robot swarms. Ad Hoc Networks, 117, 102497. https://doi.org/10.1016/j.adhoc.2021.102497, URL www.sciencedirect.com/science/article/pii/S1570870521000597.

  • Zhou, B., Zhang, Y., Chen, X., et al. (2021). Fuel: Fast UAV exploration using incremental frontier structure and hierarchical planning. IEEE Robotics and Automation Letters, 6(2), 779–786.

    Article  Google Scholar 

Download references

Funding

This work was supported by the Fonds de recherche du Quebec—Nature et technologies (FRQNT) under Grant No. 296737 and by the National Research Council Canada (NRC).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Ulrich Dah-Achinanon or Seyed Ehsan Marjani Bajestani.

Ethics declarations

Conflict of interest

The authors declare no potential conflict of interest with respect to the research, authorship, and/or publication of this article.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dah-Achinanon, U., Marjani Bajestani, S.E., Lajoie, PY. et al. Search and rescue with sparsely connected swarms. Auton Robot 47, 849–863 (2023). https://doi.org/10.1007/s10514-022-10080-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10514-022-10080-7

Keywords

Navigation