Strategies for Patrolling Missions with Multiple UAVs

  • Kristofer S. KappelEmail author
  • Tauã M. Cabreira
  • João L. Marins
  • Lisane B. de Brisolara
  • Paulo R. FerreiraJr.


This paper proposes a set of strategies for the patrolling problem using multiple UAVs and as a result, improving our original NC-Drone algorithm. We present four strategies: Watershed Strategy, Time-based Strategies, Evaporation Strategy, and Communication-Frequency Strategy. The novel strategies consider important aspects of the patrolling movement, such as time, uncertainty, and communication. Results point out that these strategies improve the centralized version of the NC-Drone considering the uniform distribution of visits and drastically reduce in 76% the standard deviation, making the algorithm more stable. Based on the results, we found that there is a trade-off between the evaluated metrics, making it necessary to perform a large number of turns to obtain a more spatially distributed patrolling. We also present a series of strategy combinations, achieving slight improvements as more combinations are adopted. The resulting algorithm from the combination of all strategies reduces the communication frequency in 50 times and outperforms the original version of the NC-Drone in 4.5%.


Patrolling problem Unmanned aerial vehicles Watershed Evaporation Communication 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



  1. 1.
    Lottes, P., Khanna, R., Pfeifer, J., Siegwart, R., Stachniss, C.: UAV-Based crop and weed classification for smart farming. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 3024–3031. IEEE (2017)Google Scholar
  2. 2.
    Barrientos, A., Colorado, J., del Cerro, J., Martinez, A., Rossi, C., Sanz, D., Valente, J.: Aerial remote sensing in agriculture: A practical approach to area coverage and path planning for fleets of mini aerial robots. J. Field Robot. 28(5), 667–689 (2011). ISSN 1556-4959CrossRefGoogle Scholar
  3. 3.
    Maza, I., Caballero, F., Capitán, J., Martínez-de Dios, J.R., Ollero, A.: Experimental results in multi-UAV coordination for disaster management and civil security applications. J. Intell. Robot. Syst. 61(1-4), 563–585 (2011)CrossRefGoogle Scholar
  4. 4.
    Pham, H.X., La, H.M., Feil-Seifer, D., Deans, M.: A distributed control framework for a team of unmanned aerial vehicles for dynamic wildfire tracking. In: 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 6648–6653, IEEE (2017)Google Scholar
  5. 5.
    Casbeer, D.W., Kingston, D.B., Beard, R.W., McLain, T.W.: Cooperative forest fire surveillance using a team of small unmanned air vehicles. Int. J. Syst. Sci. 37(6), 351–360 (2006)CrossRefzbMATHGoogle Scholar
  6. 6.
    Renzaglia, A., Reymann, C., Lacroix, S.: Monitoring the Evolution of Clouds with UAVs./ In: 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 278–283. IEEE (2016)Google Scholar
  7. 7.
    Hefferan, B., Cliff, O.M., Fitch, R.: Adversarial patrolling with reactive point processes. In: Proceedings of the Australasian Conference on Robotics and Automation (ACRA), Brisbane, Australia, pp. 5–7 (2016)Google Scholar
  8. 8.
    Basilico, N., Carpin, S.: Deploying teams of heterogeneous UAVs in cooperative two-level surveillance missions. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 610–615. IEEE (2015)Google Scholar
  9. 9.
    Andersen, H.L.: Path planning for search and rescue mission using multicopters, Master’s Thesis, Institutt for teknisk kybernetikk, Norway (2014)Google Scholar
  10. 10.
    Nattero, C., Recchiuto, C. T., Sgorbissa, A., Wanderlingh, F.: Coverage Algorithms for Search and Rescue with UAV Drones. In: Workshop of the XIII AI*IA Symposium on Artificial Intelligence (2014)Google Scholar
  11. 11.
    Cesetti, A., Frontoni, E., Mancini, A., Ascani, A., Zingaretti, P., Longhi, S.: A visual global positioning system for unmanned aerial vehicles used in photogrammetric applications. J. Intell. Robot. Syst. 61 (1-4), 157–168 (2011)CrossRefGoogle Scholar
  12. 12.
    Choset, H.: Coverage for robotics – a survey of recent results. Ann. Math. Artif. Intell. 31(1), 113–126 (2001). ISSN 1573-7470CrossRefzbMATHGoogle Scholar
  13. 13.
    Chevaleyre, Y.: Theoretical analysis of the multi-agent patrolling problem. In: Intelligent Agent Technology, 2004. Proceedings. IEEE/WIC/ACM International Conference on (IAT 2004), pp. 302–308. IEEE (2004)Google Scholar
  14. 14.
    Vincent, P., Rubin, I.: A framework and analysis for cooperative search using UAV swarms. In: Proceedings of the 2004 ACM Symposium on Applied Computing, SAC ’04, pp. 79–86, New York, ACM. ISBN 1-58113-812-1Google Scholar
  15. 15.
    Stalmakou, A.: UAV/UAS path planning for ice management information gathering. Master’s Thesis, Institutt for teknisk kybernetikk, Norway (2011)Google Scholar
  16. 16.
    Acevedo, J.J., Arrue, B.C., Maza, I., Ollero, A.: Cooperative large area surveillance with a team of aerial mobile robots for long endurance missions. J. Intell. Robot. Syst. 70(1), 329–345 (2013). ISSN 1573-0409CrossRefGoogle Scholar
  17. 17.
    Lim, S., Bang, H.: Waypoint guidance of cooperative UAVs for intelligence, surveillance, and reconnaissance. In: 2009 IEEE International Conference on Control and Automation, Pages 291–296. IEEE (2009)Google Scholar
  18. 18.
    Lim, S., Bang, H.: Waypoint planning algorithm using cost functions for surveillance. Int. J Aeronaut. Space Sci. 11(2), 136–144 (2010)CrossRefGoogle Scholar
  19. 19.
    Sampaio, P., Sousa, R., Rocha, A.: New patrolling strategies with short-range perception. In: Proceedings of the 2016 XIII Latin American Robotics Symposium and IV Brazilian Robotics Symposium (2016)Google Scholar
  20. 20.
    Cabreira, T.M., Kappel, K.S., Ferreira, P.R., de Brisolara, L.B.: An energy-aware real-time search approach for cooperative patrolling missions with multi-UAVs. In: 2018 Latin American Robotic Symposium, 2018 Brazilian Symposium on Robotics (SBR) and 2018 Workshop on Robotics in Education (WRE), pp. 254–259. IEEE (2018)Google Scholar
  21. 21.
    Araujo, J.F., Sujit, P.B., Sousa, J.B.: Multiple UAV area decomposition and coverage. In: 2013 IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA), pp. 30–37. IEEE (2013)Google Scholar
  22. 22.
    Ramasamy, M., Ghose, D.: A heuristic learning algorithm for preferential area surveillance by unmanned aerial vehicles. J. Intell. Robot. Syst. 88(2-4), 655–681 (2017)CrossRefGoogle Scholar
  23. 23.
    Maza, I., Ollero, A.: Multiple UAV cooperative searching operation using polygon area decomposition and efficient coverage algorithms. In: Distributed Autonomous Robotic Systems 6, pp. 221–230. Springer, Japan (2007)Google Scholar
  24. 24.
    Torres, M., Pelta, D.A., Verdegay, J.L., Torres, J.C.: Coverage path planning with unmanned aerial vehicles for 3D terrain reconstruction. Expert Syst. Appl., pp. 441–451, 2016. ISSN 0957-4174Google Scholar
  25. 25.
    Jiao, Y.-S., Wang, X.-M., Chen, H., Li, Y.: Research on the coverage path planning of UAVs for polygon areas. In: 2010 5Th IEEE Conference on Industrial Electronics and Applications, pp. 1467–1472. IEEE (2010)Google Scholar
  26. 26.
    Li, D., Wang, X., Sun, T.: Energy-optimal coverage path planning on topographic map for environment survey with unmanned aerial vehicles. Electron. Lett. 52(9), 699–701 (2016)CrossRefGoogle Scholar
  27. 27.
    Di Franco, C., Buttazzo, G.: Coverage path planning for UAVs photogrammetry with energy and resolution constraints. Journal of Intelligent & Robotic Systems, pp. 1–18 (2016)Google Scholar
  28. 28.
    Milech Cabreira, T., Di Franco, C., Ferreira, P.R. Jr, Buttazzo, G.C.: Energy-aware spiral coverage path planning for UAV photogrammetric applications. IEEE Robot. Autom. Lett. 3(4), 3662–3668 (2018). ISSN 2377-3766. CrossRefGoogle Scholar
  29. 29.
    Balampanis, F., Maza, I., Ollero, A.: Area decomposition, partition and coverage with multiple remotely piloted aircraft systems operating in coastal regions. In: 2016 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 275–283. IEEE (2016)Google Scholar
  30. 30.
    Balampanis, F., Maza, I., Ollero, A.: Spiral-like coverage path planning for multiple heterogeneous UAS operating in coastal regions. In: 2017 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 617–624. IEEE (2017)Google Scholar
  31. 31.
    Balampanis, F., Maza, I., Ollero, A.: Coastal areas division and coverage with multiple UAVs for remote sensing. Sensors 17(4), 808 (2017)CrossRefGoogle Scholar
  32. 32.
    Balampanis, F., Maza, I., Ollero, A.: Area partition for coastal regions with multiple UAS. J. Intell. Robot. Syst. 88(2-4), 751–766 (2017)CrossRefGoogle Scholar
  33. 33.
    Koenig, S., Liu, Y.: Terrain coverage with ant robots: a simulation study. In: Proceedings of the Fifth International Conference on Autonomous Agents, pp. 600–607 (2001)Google Scholar
  34. 34.
    Cabreira, T., Ferreira, P.R. Jr: Terrain coverage with UAVs: Real-time search and geometric approaches applied to an abstract model of random events. In: Proceedings of the 13rd Latin American Robotics Symposium. IEEE (2016)Google Scholar
  35. 35.
    Zelenka, J., Kasanickỳ, T.: Insect pheromone strategy for the robots coordination. Appl. Mech. Mater. 613, 163–171 (2014)CrossRefGoogle Scholar
  36. 36.
    Zelenka, J., Kasanickỳ, T.: Outdoor UAV Control and Coordination System Supported by Biological Inspired Method. In: 2014 23Rd International Conference on Robotics in Alpe-Adria-Danube Region (RAAD), pp. 1–7 (2014)Google Scholar
  37. 37.
    Floreano, D., Mattiussi, C.: Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies. MIT Press (2008)Google Scholar
  38. 38.
    Zelenka, J., Kasanickỳ, T.: Insect pheromone strategy for the robots coordination - reaction on loss communication. In: 2014 IEEE 15Th International Symposium on Computational Intelligence and Informatics (CINTI), pp. 79–83. IEEE (2014)Google Scholar
  39. 39.
    Albani, D., Nardi, D., Trianni, V.: Field coverage and weed mapping by UAV swarms. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4319–4325. IEEE (2017)Google Scholar
  40. 40.
    Pfeifer, R., Lungarella, M., Iida, F.: Self-organization, embodiment, and biologically inspired robotics. Science 318(5853), 1088–1093 (2007)CrossRefGoogle Scholar
  41. 41.
    Pirzadeh, A., Snyder, W.: A Unified Solution to Coverage and Search in Explored and Unexplored Terrains Using Indirect Control. In: Proceedings., 1990 IEEE International Conference on Robotics and Automation, 1990, pp. 2113–2119. IEEE (1990)Google Scholar
  42. 42.
    Korf, R.E.: Real-time heuristic search. Artif. Intell. 42(2-3), 189–211 (1990)CrossRefzbMATHGoogle Scholar
  43. 43.
    Thrun, S.B.: Efficient exploration in reinforcement learning (1992)Google Scholar
  44. 44.
    Wagner, I., Lindenbaum, M., Bruckstein, A.: On-line graph searching by a smell-oriented vertex process. In: Proceedings of the AAAI Workshop on On-Line Search, pp. 122–125 (1997)Google Scholar
  45. 45.
    Koenig, S., Simmons, R.G.: Easy and hard testbeds for real-time search algorithms. In: AAAI/IAAI, Vol. 1, pp. 279–285. Citeseer (1996)Google Scholar
  46. 46.
    Cannata, G., Sgorbissa, A.: A minimalist algorithm for multirobot continuous coverage. IEEE Trans. Robot. 27(2), 297–312 (2011)CrossRefGoogle Scholar
  47. 47.
    Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y.: ROS: an open-source robot operating system. In: ICRA Workshop on Open Source Software, Vol. 3, pp. 5. Kobe (2009)Google Scholar
  48. 48.
    Piaggio, M., Sgorbissa, A., Zaccaria, R.: Programming real-time distributed multiple robotic systems. In: Robot Soccer World Cup, pp. 412–423. Springer (1999)Google Scholar
  49. 49.
    Khan, A., Yanmaz, E., Rinner, B.: Information merging in multi-UAV cooperative search. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 3122–3129. IEEE (2014)Google Scholar
  50. 50.
    Ramasamy, M., Ghose, D.: Learning-based preferential surveillance algorithm for persistent surveillance by unmanned aerial vehicles. In: 2016 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1032–1040. IEEE (2016)Google Scholar
  51. 51.
    Kuiper, E., Nadjm-Tehrani, S.: Mobility models for UAV group reconnaissance applications. In: 2006 International Conference on Wireless and Mobile Communications (ICWMC’06), pp. 33–33. IEEE (2006)Google Scholar
  52. 52.
    Rosalie, M., Danoy, G., Chaumette, S., Bouvry, P.: From random process to chaotic behavior in swarms of UAVs. In: 6Th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications (2016)Google Scholar
  53. 53.
    Rosalie, M., Dentler, J.E., Danoy, G., Bouvry, P., Kannan, S., Olivares-Mendez, M.A., Voos, H.: Area exploration with a swarm of UAVs combining deterministic chaotic ant colony mobility with position MPC. In: 2017 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1392–1397. IEEE (2017)Google Scholar
  54. 54.
    Rohmer, E., Singh, S.P.N., Freese, M.: V-REP: a versatile and scalable robot simulation framework. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1321–1326. IEEE (2013)Google Scholar
  55. 55.
    Cheng, C.-T., Fallahi, K., Leung, H., Tse Chi, K.s: Cooperative path planner for UAVs using ACO algorithm with gaussian distribution functions. In: 2009 IEEE International Symposium on Circuits and Systems, pp. 173–176. IEEE (2009)Google Scholar
  56. 56.
    Paradzik, M., et al.: Multi-Agent search strategy based on digital pheromones for UAVs. In: Signal Processing and Communication Application Conference (SIU), 2016 24Th, pp. 233–236. IEEE (2016)Google Scholar
  57. 57.
    Beucher, S., Meyer, F.: The morphological approach to segmentation: the watershed transformation. Optical Engineering-New York-Marcel Dekker Incorporated- 34, 433–433 (1992)Google Scholar
  58. 58.
    Digi International Inc. (Digi). Digi xbee3 zigbee 3., 2019 [Online; accessed April-02-2019]
  59. 59.
    Digi International Inc. (Digi). Drone technologies disrupting industries, saving lives., 2019 [Online; accessed April-02-2019]
  60. 60.
    Sampaio, P.A.: Patrulha temporal: taxonomia, métricas e novas solucões. PhD thesis Universidade Federal de Pernambuco (2013)Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Programa de Pós-Graduação em Computação (PPGC), Centro de Desenvolvimento Tecnológico (CDTec)Universidade Federal de Pelotas (UFPel)PelotasBrazil

Personalised recommendations