3D multi-robot patrolling with a two-level coordination strategy

  • Luigi FredaEmail author
  • Mario Gianni
  • Fiora Pirri
  • Abel Gawel
  • Renaud Dubé
  • Roland Siegwart
  • Cesar Cadena


Teams of UGVs patrolling harsh and complex 3D environments can experience interference and spatial conflicts with one another. Neglecting the occurrence of these events crucially hinders both soundness and reliability of a patrolling process. This work presents a distributed multi-robot patrolling technique, which uses a two-level coordination strategy to minimize and explicitly manage the occurrence of conflicts and interference. The first level guides the agents to single out exclusive target nodes on a topological map. This target selection relies on a shared idleness representation and a coordination mechanism preventing topological conflicts. The second level hosts coordination strategies based on a metric representation of space and is supported by a 3D SLAM system. Here, each robot path planner negotiates spatial conflicts by applying a multi-robot traversability function. Continuous interactions between these two levels ensure coordination and conflicts resolution. Both simulations and real-world experiments are presented to validate the performances of the proposed patrolling strategy in 3D environments. Results show this is a promising solution for managing spatial conflicts and preventing deadlocks.


3D patrolling 3D multi-robot systems Distributed multi-robot coordination UGVs 



This work was supported by the European Union’s Seventh Framework Programme for research, technological development and demonstration under the TRADR Project No. FP7-ICT-609763.


  1. Acevedo, J. J., Arrue, B. C., Daz-Bez, J. M., Ventura, I., Maza, I., & Ollero, A. (2013). Decentralized strategy to ensure information propagation in area monitoring missions with a team of UAVs under limited communications. In 2013 International Conference on Unmanned Aircraft Systems (ICUAS) (pp. 565–574).Google Scholar
  2. Acevedo, J. J., Arrue, B. C., Maza, I., & Ollero, A. (2016). A distributed algorithm for area partitioning in grid-shape and vector-shape configurations with multiple aerial robots. Journal of Intelligent & Robotic Systems, 84(1), 543–557.CrossRefGoogle Scholar
  3. Agmon, N., Kaminka, G. A., & Kraus, S. (2014). Multi-robot adversarial patrolling: Facing a full-knowledge opponent. CoRR abs/1401.3903.Google Scholar
  4. Agmon, N., Kraus, S., & Kaminka, G. A. (2008a). Multi-robot perimeter patrol in adversarial settings. In ICRA (pp. 2339–2345).Google Scholar
  5. Agmon, N., Sadov, V., Kaminka, G. A., & Kraus, S. (2008b). The impact of adversarial knowledge on adversarial planning in perimeter patrol. In Proceedings of the 7th international joint conference on autonomous agents and multiagent systems—Volume 1, AAMAS’08 (pp. 55–62). International Foundation for Autonomous Agents and Multiagent Systems.Google Scholar
  6. Ahmadi, M., & Stone, P. (2006). A multi-robot system for continuous area sweeping tasks. In ICRA (pp. 1724–1729).Google Scholar
  7. Aksaray, D., Leahy, K., & Belta, C. (2015). Distributed multi-agent persistent surveillance under temporal logic constraints. IFAC-PapersOnLine, 48(22), 174–179.CrossRefGoogle Scholar
  8. Andrade, R. D. C., Macedo, H. T., Ramalho, G. L., & Ferraz, C. A. (2001). Distributed mobile autonomous agents in network management. In Proceedings of international conference on parallel and distributed processing techniques and applications.Google Scholar
  9. Baran, P. (1964). On distributed communications networks. IEEE Transactions on Communications Systems, 12(1), 1–9.MathSciNetCrossRefGoogle Scholar
  10. Barraquand, J., Langlois, B., & Latombe, J. C. (1992). Numerical potential field techniques for robot path planning. IEEE Transactions on Systems, Man, and Cybernetics, 22(2), 224–241.MathSciNetCrossRefGoogle Scholar
  11. Bereg, S., Caraballo, L. E., Díaz-Báñez, J. M., & Lopez, M. A. (2016). Resilience of a synchronized multi-agent system. ArXiv e-prints.Google Scholar
  12. Cabrita, G., Sousa, P., Marques, L., & De Almeida, A. (2010). Infrastructure monitoring with multi-robot teams. In IROS (pp. 18–22).Google Scholar
  13. Caccamo, S., Parasuraman, R., Freda, L., Gianni, M., & Ögren, P. (2017). Rcamp: A resilient communication-aware motion planner for mobile robots with autonomous repair of wireless connectivity. In 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE.Google Scholar
  14. Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., et al. (2016). Past, present, and future of simultaneous localization and mapping: Towards the robust-perception age. IEEE Transactions on Robotics, 32(6), 1309–1332.CrossRefGoogle Scholar
  15. Chen, H., Cheng, T., & Wise, S. (2017). Developing an online cooperative police patrol routing strategy. Computers, Environment and Urban Systems, 62, 19–29.CrossRefGoogle Scholar
  16. Chevaleyre, Y. (2004). Theoretical analysis of the multi-agent patrolling problem. In Proceedings of the IEEE/WIC/ACM international conference on intelligent agent technology (pp. 302–308).Google Scholar
  17. Colas, F., Mahesh, S., Pomerleau, F., Liu, M., & Siegwart, R. (2013). 3D path planning and execution for search and rescue ground robots. In 2013 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 722–727). IEEE.Google Scholar
  18. Diankov, R., Kuffner, J. (2007). Randomized statistical path planning. In IEEE/RSJ international conference on intelligent robots and systems. IROS 2007 (pp. 1–6). IEEE.Google Scholar
  19. Douillard, B., Underwood, J., Kuntz, N., Vlaskine, V., Quadros, A., Morton, P., et al. (2011). On the segmentation of 3D lidar point clouds. In ICRA.Google Scholar
  20. Du, T. C., Li, E. Y., & Chang, A. P. (2003). Mobile agents in distributed network management. Communications of the ACM, 46(7), 127–132.CrossRefGoogle Scholar
  21. Dubé, R., Dugas, D., Stumm, E., Nieto, J., Siegwart, R., & Cadena, C. (2017a). Segmatch: Segment based place recognition in 3D point clouds. In ICRA (pp. 5266–5272). IEEE.Google Scholar
  22. Dubé, R., Gawel, A., Sommer, H., Nieto, J., Siegwart, R., & Cadena, C. (2017b). An online multi-robot slam system for 3D lidars. In IROS.Google Scholar
  23. Elmaliach, Y., Agmon, N., & Kaminka, G. A. (2007). Multi-robot area patrol under frequency constraints. In ICRA (pp. 385–390).Google Scholar
  24. Elmaliach, Y., Agmon, N., & Kaminka, G. A. (2009a). Multi-robot area patrol under frequency constraints. Annals of Mathematics and Artificial Intelligence, 57(3), 293–320.MathSciNetCrossRefzbMATHGoogle Scholar
  25. Elmaliach, Y., Agmon, N., & Kaminka, G. A. (2009b). Multi-robot area patrol under frequency constraints. Annals of Mathematics and Artificial Intelligence, 57(3–4), 293–320.MathSciNetCrossRefzbMATHGoogle Scholar
  26. Farinelli, A., Iocchi, L., & Nardi, D. (2004). Multirobot systems: A classification focused on coordination. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 34(5), 2015–2028.CrossRefGoogle Scholar
  27. Farinelli, A., Iocchi, L., & Nardi, D. (2017). Distributed on-line dynamic task assignment for multi-robot patrolling. Autonomous Robots, 41(6), 1321–1345.CrossRefGoogle Scholar
  28. Ferri, F., Gianni, M., Menna, M., & Pirri, F. (2014). Point cloud segmentation and 3D path planning for tracked vehicles in cluttered and dynamic environments. In Proceedings of the 3rd IROS Workshop on Robots in Clutter: Perception and Interaction in Clutter.Google Scholar
  29. Franchi, A., Freda, L., Oriolo, G., & Vendittelli, M. (2009). The sensor-based random graph method for cooperative robot exploration. IEEE/ASME Transaction on Mechatronics, 14(2), 163–175.CrossRefGoogle Scholar
  30. Grisetti, G., Kümmerle, R., Stachniss, C., & Burgard, W. (2010). A tutorial on graph-based slam. Intelligent Transportation Systems Magazine, IEEE, 2(4), 31–43.CrossRefGoogle Scholar
  31. Haït, A., Simeon, T., & Taïx, M. (2002). Algorithms for rough terrain trajectory planning. Advanced Robotics, 16(8), 673–699.CrossRefGoogle Scholar
  32. Hernández, E., Barrientos, A., & Cerro, J. D. (2014). Selective smooth fictitious play: An approach based on game theory for patrolling infrastructures with a multi-robot system. Expert Systems With Applications, 41(6), 2897–2913.CrossRefGoogle Scholar
  33. Hornung, A., Wurm, K. M., Bennewitz, M., Stachniss, C., & Burgard, W. (2013). OctoMap: An efficient probabilistic 3D mapping framework based on octrees. Autonomous Robots, 34(3), 189–206.CrossRefGoogle Scholar
  34. Iocchi, L., Marchetti, L., & Nardi, D. (2011). Multi-robot patrolling with coordinated behaviours in realistic environments. In IROS (pp. 2796–2801).Google Scholar
  35. Jung, M. F., Beane, M., Forlizzi, J., Murphy, R., & Vertesi, J. (2017). Robots in group context: Rethinking design, development and deployment. In Proceedings of the 2017 CHI conference extended abstracts on human factors in computing systems (pp. 1283–1288). ACM.Google Scholar
  36. Karaman, S., & Frazzoli, E. (2010). Incremental sampling-based algorithms for optimal motion planning. Robotics Science and Systems VI, 104, 2.Google Scholar
  37. Kleiner, A., Heintz, F., & Tadokoro, S. (2016). Special issue on safety, security, and rescue robotics (SSRR), part 2. Journal of Field Robotics, 33(4), 409–410.CrossRefGoogle Scholar
  38. Kruijff, G. J. M., Kruijff-Korbayová, I., Keshavdas, S., Larochelle, B., Janíček, M., Colas, F., et al. (2014). Designing, developing, and deploying systems to support human-robot teams in disaster response. Advanced Robotics, 28(23), 1547–1570.CrossRefGoogle Scholar
  39. Kruijff, G. J. M., Pirri, F., Gianni, M., Papadakis, P., Pizzoli, M., Sinha, A., et al. (2012). Rescue robots at earthquake-hit Mirandola, Italy: A field report. In 2012 IEEE international symposium on safety, security, and rescue robotics (SSRR) (pp. 1–8). IEEE.Google Scholar
  40. Kruijff-Korbayová, I., Colas, F., Gianni, M., Pirri, F., Greeff, J., Hindriks, K., et al. (2015). Tradr project: Long-term human-robot teaming for robot assisted disaster response. KI-Künstliche Intelligenz, 29(2), 193–201.CrossRefGoogle Scholar
  41. Kruijff-Korbayová, I., Freda, L., Gianni, M., Ntouskos, V., Hlaváč, V., Kubelka, V., et al. (2016). Deployment of ground and aerial robots in earthquake-struck amatrice in Italy (brief report). In 2016 IEEE international symposium on safety, security, and rescue robotics (SSRR) (pp. 278–279). IEEE.Google Scholar
  42. Krüsi, P., Furgale, P., Bosse, M., & Siegwart, R. (2017). Driving on point clouds: Motion planning, trajectory optimization, and terrain assessment in generic nonplanar environments. Journal of Field Robotics, 34(5), 940–984.CrossRefGoogle Scholar
  43. Kubelka, V., Oswald, L., Pomerleau, F., Colas, F., Svoboda, T., & Reinstein, M. (2015). Robust data fusion of multimodal sensory information for mobile robots. Journal of Field Robotics, 32(4), 447–473.CrossRefGoogle Scholar
  44. LaValle, S. M. (2006). Planning algorithms. Cambridge: Cambridge University Press, Accessed Dec 2018.
  45. Machado, A., Ramalho, G., Zucker, J. D., & Drogoul, A. (2002). Multi-agent patrolling: An empirical analysis of alternative architectures. In International workshop on multi-agent systems and agent-based simulation (pp. 155–170). Springer.Google Scholar
  46. Menna, M., Gianni, M., Ferri, F., & Pirri, F. (2014). Real-time autonomous 3D navigation for tracked vehicles in rescue environments. In IROS (pp. 696–702).Google Scholar
  47. Murphy, R. R. (2004). Trial by fire [rescue robots]. IEEE Robotics & Automation Magazine, 11(3), 50–61.MathSciNetCrossRefGoogle Scholar
  48. Nagatani, K., Kiribayashi, S., Okada, Y., Otake, K., Yoshida, K., Tadokoro, S., et al. (2013). Emergency response to the nuclear accident at the fukushima daiichi nuclear power plants using mobile rescue robots. Journal of Field Robotics, 30(1), 44–63.CrossRefGoogle Scholar
  49. Panagou, D., Stipanovi, D. M., & Voulgaris, P. G. (2016). Distributed coordination control for multi-robot networks using lyapunov-like barrier functions. IEEE Transactions on Automatic Control, 61(3), 617–632.MathSciNetCrossRefzbMATHGoogle Scholar
  50. Park, C. H., Kim, Y. D., & Jeong, B. (2012). Heuristics for determining a patrol path of an unmanned combat vehicle. Computers & Industrial Engineering, 63(1), 150–160.CrossRefGoogle Scholar
  51. Pasqualetti, F., Durham, J. W., & Bullo, F. (2012). Cooperative patrolling via weighted tours: Performance analysis and distributed algorithms. IEEE Transactions on Robotics, 28(5), 1181–1188.CrossRefGoogle Scholar
  52. Pippin, C., & Christensen, H. (2014). Trust modeling in multi-robot patrolling. In ICRA (pp. 59–66).Google Scholar
  53. Portugal, D. (2017). patrolling\(\_\)sim - Multi-Robot Patrolling Stage/ROS Simulation Package, Accessed February 20, 2017.
  54. Portugal, D., & Rocha, R. (2010). Msp algorithm: Multi-robot patrolling based on territory allocation using balanced graph partitioning. In Proceedings of the 2010 ACM symposium on applied computing (pp. 1271–1276). New York, NY, USA: ACM.Google Scholar
  55. Portugal, D., & Rocha, R. (2011). A survey on multi-robot patrolling algorithms. In Technological Innovation for Sustainability (pp. 139–146).Google Scholar
  56. Portugal, D., & Rocha, R. P. (2013a). Distributed multi-robot patrol: A scalable and fault-tolerant framework. Robotics and Autonomous Systems, 61(12), 1572–1587.CrossRefGoogle Scholar
  57. Portugal, D., & Rocha, R. P. (2013b). Multi-robot patrolling algorithms: Examining performance and scalability. Advanced Robotics, 27(5), 325–336.CrossRefGoogle Scholar
  58. Portugal, D., & Rocha, R. P. (2013c). Retrieving topological information for mobile robots provided with grid maps (pp. 204–217). Berlin: Springer.Google Scholar
  59. Portugal, D., & Rocha, R. P. (2013d). Scalable, fault-tolerant and distributed multi-robot patrol in real world environments. In IROS (pp. 4759–4764).Google Scholar
  60. Portugal, D., & Rocha, R. P. (2016). Cooperative multi-robot patrol with bayesian learning. Autonomous Robots, 40(5), 929–953.CrossRefGoogle Scholar
  61. Robin, C., & Lacroix, S. (2016). Multi-robot target detection and tracking: Taxonomy and survey. Autonomous Robots, 40(4), 729–760.CrossRefGoogle Scholar
  62. Rohmer, E., Singh, S. P. N., & Freese, M. (2013). V-rep: A versatile and scalable robot simulation framework. In Proceedings of The International Conference on Intelligent Robots and Systems (IROS).Google Scholar
  63. Sak, T., Wainer, J., & Goldenstein, S. K. (2008). Probabilistic multiagent patrolling (pp. 124–133). Berlin: Springer.Google Scholar
  64. Santana, H., Ramalho, G., Corruble, V., & Ratitch, B. (2004). Multi-agent patrolling with reinforcement learning. In Proceedings of the 3rd international joint conference on autonomous agents and multiagent systems—Volume 3, AAMAS’04 (pp. 1122–1129). IEEE Computer Society.Google Scholar
  65. Schwarz, M. (2017). nimbro\(\_\)network - ROS transport for high-latency, low-quality networks, Accessed February 20, 2017.
  66. Sempé, F., & Drogoul, A. (2003). Adaptive patrol for a group of robots. In 2003 IEEE/RSJ international conference on intelligent robots and systems. (IROS 2003). Proceedings (Vol. 3, pp. 2865–2869). IEEE.Google Scholar
  67. Shahriari, M., & Biglarbegian, M. (2016). A new conflict resolution method for multiple mobile robots in cluttered environments with motion-liveness. IEEE Transactions on Cybernetics, PP(99), 1–12.Google Scholar
  68. Song, C., Liu, L., Feng, G., & Xu, S. (2014). Optimal control for multi-agent persistent monitoring. Automatica, 50(6), 1663–1668.MathSciNetCrossRefzbMATHGoogle Scholar
  69. Tardioli, D., Sicignano, D., Riazuelo, L., Romeo, A., Villarroel, J. L., & Montano, L. (2016). Robot teams for intervention in confined and structured environments. Journal of Field Robotics, 33(6), 765–801.CrossRefGoogle Scholar
  70. Walcott-Bryant, A., Kaess, M., Johannsson, H., & Leonard, J. J. (2012). Dynamic pose graph slam: Long-term mapping in low dynamic environments. In 2012 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 1871–1878). IEEE.Google Scholar
  71. Weinmann, M., Jutzi, B., & Mallet, C. (2014). Semantic 3d scene interpretation: A framework combining optimal neighborhood size selection with relevant features. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2(3), 181.CrossRefGoogle Scholar
  72. Worst, R., Dubé, R., Svoboda, T., Freda, L., et al. (2017). Dr 6.3: Multi-robot task adaptation, TRADR deliverable. Accessed April 15, 2018.
  73. Worst, R., Zimmermann, E., Reuter, D., et al. (2018). Dr 6.4: Persistence in long-term human-robot teaming for robot assisted disaster response, TRADR deliverable. Accessed October 13, 2018.
  74. Yan, C., & Zhang, T. (2016). Multi-robot patrol: A distributed algorithm based on expected idleness. International Journal of Advanced Robotic Systems, 13(6), 1729881416663,666.Google Scholar
  75. Yan, Z., Jouandeau, N., & Cherif, A. A. (2013). A survey and analysis of multi-robot coordination. International Journal of Advanced Robotic Systems, 10(12), 399.CrossRefGoogle Scholar
  76. Yehoshua, R., Agmon, N., & Kaminka, G. A. (2013). Robotic adversarial coverage: Introduction and preliminary results. In IROS (pp. 6000–6005).Google Scholar
  77. Zimmermann, K., Zuzanek, P., Reinstein, M., & Hlavac, V. (2014). Adaptive traversability of unknown complex terrain with obstacles for mobile robots. In 2014 IEEE international conference on robotics and automation (ICRA) (pp. 5177–5182). IEEE.Google Scholar

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Authors and Affiliations

  1. 1.ALCOR LabDIAG - Sapienza University of RomeRomeItaly
  2. 2.Autonomous Systems Lab - ETH ZurichZurichSwitzerland

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