Skip to main content

Advertisement

Springer Nature Link
Log in

Strategies for Patrolling Missions with Multiple UAVs

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

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%.

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
$34.99 /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 excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  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)

  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 1556-4959

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  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)

  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)

  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)

  9. Andersen, H.L.: Path planning for search and rescue mission using multicopters, Master’s Thesis, Institutt for teknisk kybernetikk, Norway (2014)

  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)

  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)

    Article  Google Scholar 

  12. Choset, H.: Coverage for robotics – a survey of recent results. Ann. Math. Artif. Intell. 31(1), 113–126 (2001). ISSN 1573-7470

    Article  Google Scholar 

  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)

  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 1-58113-812-1

  15. Stalmakou, A.: UAV/UAS path planning for ice management information gathering. Master’s Thesis, Institutt for teknisk kybernetikk, Norway (2011)

  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 1573-0409

    Article  Google Scholar 

  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)

  18. Lim, S., Bang, H.: Waypoint planning algorithm using cost functions for surveillance. Int. J Aeronaut. Space Sci. 11(2), 136–144 (2010)

    Article  Google Scholar 

  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)

  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)

  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)

  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)

    Article  Google Scholar 

  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)

  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 0957-4174

  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)

  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)

    Article  Google Scholar 

  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)

  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. https://doi.org/10.1109/LRA.2018.2854967

    Article  Google Scholar 

  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)

  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)

  31. Balampanis, F., Maza, I., Ollero, A.: Coastal areas division and coverage with multiple UAVs for remote sensing. Sensors 17(4), 808 (2017)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  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)

  35. Zelenka, J., Kasanickỳ, T.: Insect pheromone strategy for the robots coordination. Appl. Mech. Mater. 613, 163–171 (2014)

    Article  Google Scholar 

  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)

  37. Floreano, D., Mattiussi, C.: Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies. MIT Press (2008)

  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)

  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)

  40. Pfeifer, R., Lungarella, M., Iida, F.: Self-organization, embodiment, and biologically inspired robotics. Science 318(5853), 1088–1093 (2007)

    Article  Google Scholar 

  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)

  42. Korf, R.E.: Real-time heuristic search. Artif. Intell. 42(2-3), 189–211 (1990)

    Article  Google Scholar 

  43. Thrun, S.B.: Efficient exploration in reinforcement learning (1992)

  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)

  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)

  46. Cannata, G., Sgorbissa, A.: A minimalist algorithm for multirobot continuous coverage. IEEE Trans. Robot. 27(2), 297–312 (2011)

    Article  Google Scholar 

  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)

  48. Piaggio, M., Sgorbissa, A., Zaccaria, R.: Programming real-time distributed multiple robotic systems. In: Robot Soccer World Cup, pp. 412–423. Springer (1999)

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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. Digi International Inc. (Digi). Digi xbee3 zigbee 3. https://www.digi.com/products/embedded-systems/rf-modules/2-4-ghz-modules/xbee3-zigbee-3, 2019 [Online; accessed April-02-2019]

  59. Digi International Inc. (Digi). Drone technologies disrupting industries, saving lives. https://www.digi.com/customersuccesses/draganfly, 2019 [Online; accessed April-02-2019]

  60. Sampaio, P.A.: Patrulha temporal: taxonomia, métricas e novas solucões. PhD thesis Universidade Federal de Pernambuco (2013)

Download references

Author information

Authors and Affiliations

  1. Programa de Pós-Graduação em Computação (PPGC), Centro de Desenvolvimento Tecnológico (CDTec), Universidade Federal de Pelotas (UFPel), 96010-610, Pelotas, Brazil

    Kristofer S. Kappel, Tauã M. Cabreira, João L. Marins, Lisane B. de Brisolara & Paulo R. Ferreira Jr.

Authors
  1. Kristofer S. Kappel
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Tauã M. Cabreira
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. João L. Marins
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Lisane B. de Brisolara
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Paulo R. Ferreira Jr.
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Kristofer S. Kappel.

Additional information

Publisher’s Note

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

The work of K. Kappel, L. B. de Brisolara, and P. R. Ferreira Jr. was financed in part by FAPERGS/CNPq under Grant 16/2551-0000/472-2. The work of P. R. Ferreira Jr. was also supported in part by CNPq under Grant 308487/2017-6.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kappel, K.S., Cabreira, T.M., Marins, J.L. et al. Strategies for Patrolling Missions with Multiple UAVs. J Intell Robot Syst 99, 499–515 (2020). https://doi.org/10.1007/s10846-019-01090-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-019-01090-2

Keywords