Applied Intelligence

, Volume 42, Issue 1, pp 49–62 | Cite as

Aerial service vehicles for industrial inspection: task decomposition and plan execution

  • Jonathan Cacace
  • Alberto Finzi
  • Vincenzo Lippiello
  • Giuseppe Loianno
  • Dario Sanzone


This work proposes a high-level control system designed for an Aerial Service Vehicle capable of performing complex tasks in close and physical interaction with the environment in an autonomous manner. We designed a hybrid control architecture which integrates task, path, motion planning/replanning, and execution monitoring. The high-level system relies on a continuous monitoring and planning cycle to suitably react to events, user interventions, and failures, communicating with the low level control layers. The system has been assessed on real-world and simulated scenarios representing an industrial environment.


Unmanned air vehicles Planning systems Autonomous robots Aerial service robotics 



The research leading to these results has been supported by the SHERPA and ARCAS collaborative projects, which have received funding from the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreements ICT-600958 and ICT-287617, respectively. The authors are solely responsible for its content. It does not represent the opinion of the European Community and the Community is not responsible for any use that might be made of the information contained therein.


  1. 1.
    Ai-Chang M, Bresina J, Charest L, Chase A, Cheng-jung Hsu J, Jonsson A, Kanefsky B, Morris P, Rajan K, Yglesias , Chafin BG, Dias WB, Maldague PF (2004) MAPGEN: mixed-initiative planning and scheduling for the mars exploration rover mission. IEEE Intell Syst 19(1): 8–12CrossRefGoogle Scholar
  2. 2.
    EU Collaborative Project ICT-248669, “AIRobots”.
  3. 3.
    Allen J, Ferguson G (2002) Human-machine collaborative planning. In: NASA workshop on planning and scheduling for spaceGoogle Scholar
  4. 4.
    Antonelli G, Marino A (2010) Smooth 3-dimensional path generation with guaranteed maximum distance from viapoints. In: 7th IFAC symposium on intelligent autonomous vehicles. pp 1–6Google Scholar
  5. 5.
    Blöesch M, Weiss S, Scaramuzza D, Siegwart R (2010) Vision based MAV navigation in unknown and unstructured environments. In: ICRA, vol 2010. 21–28Google Scholar
  6. 6.
    Carbone A, Finzi A, Orlandini A, Pirri F (2008) Model-based control architecture for attentive robots in rescue scenarios. Auton Robot 24(1): 87–120CrossRefGoogle Scholar
  7. 7.
    Erol K, Hendler J, Nau D (1994) HTN planning: complexity and expressivity. In: Proceedings of AAAI-94. AAAI Press, pp 1123–1128Google Scholar
  8. 8.
    Cacace J, Finzi A, Lippiello V, Loianno G, Sanzone D (2013) Aerial service vehicles for industrial inspection: task decomposition and plan execution. In: 26th international conference on industrial engineering and other applications of applied intelligent systems. pp 302–311Google Scholar
  9. 9.
    Cacace J, Finzi A, Lippiello V, Loianno G, Sanzone D (2013) Integrated planning and execution for an aerial service vehicle. In: 23th international conference on automated planning and scheduling, workshop on planning and roboticsGoogle Scholar
  10. 10.
    Carloni R, Lippiello V, D’Auria M, Fumagalli M, Mersha AY, Stramigioli S, Siciliano B (2013) Robot vision: obstacle-avoidance techniques for unmanned aerial vehicles. IEEE Robot Autom Mag 20(4): 22–31CrossRefGoogle Scholar
  11. 11.
    Doherty P, Granlund G, Kuchcinski KSE, Nordberg K, Skarman E, Wiklund J (2000) The WITAS unmanned aerial vehicle project. In: Proceedings of the 14th European conference on artificial intelligence. pp 747–755Google Scholar
  12. 12.
    Doherty P, Kvarnström J, Fredrik H (2009) A temporal logic-based planning and execution monitoring framework for unmanned aircraft systems. In: AAMAS. pp 332–377Google Scholar
  13. 13.
    Donnarumma F, Lippiello V, Saveriano M (2012) Fast incremental clustering and representation of a 3D point cloud sequence with planar regions. In: IEEE/RSJ international conference on intelligent robots and systems. pp 3475–3480Google Scholar
  14. 14.
    Finzi A, Orlandini A (2005) Human-robot interaction through mixed-initiative planning for rescue and search rovers. In: AI*IA-05. pp 483–494Google Scholar
  15. 15.
    Gancet J, Hattenberger G, Alami R, Lacroix S (2005) Task planning and control for a multi-UAV system: architecture and algorithms. In: IROS. pp 1017–1022Google Scholar
  16. 16.
    Geiger A, Ziegler J, Stiller C (2011) Stereoscan: dense 3D reconstruction in real-time. In: IEEE intelligent vehicles symposium. pp 963–968Google Scholar
  17. 17.
    Hrabar S (2006) Vision-based 3D navigation for an autonomous helicopter. Ph.D. Thesis, USC, Jan, 2006Google Scholar
  18. 18.
    Ingrand F, Georgeff M P, Rao A S (1992) An architecture for real-time reasoning and system control. IEEE Exp Intell Syst Appl: 34–44Google Scholar
  19. 19.
    Lavalle S M (1998) Rapidly-exploring random trees: A new tool for path planning. Computer Science Dept., Iowa State University, Technical ReportGoogle Scholar
  20. 20.
    Lippiello V, Loianno G, Siciliano B (2011) MAV indoor navigation based on a closed-form solution for absolute scale velocity estimation using optical flow and inertial data. In: 50th IEEE conference on decision and control and european control conference. pp 3566–3571Google Scholar
  21. 21.
    Lippiello V, Siciliano B (2012) Wall inspection control of a VTOL unmanned aerial vehicle based on a stereo optical flow. In: IEEE/RSJ international conference on intelligent robots and systems. pp 4296–4302Google Scholar
  22. 22.
    Macfarlane S E, Croft E A (2003) Jerk-bounded manipulator trajectory planning: design for real-time applications. IEEE Trans Robot 19: 42–52CrossRefGoogle Scholar
  23. 23.
    Marconi L, Basile L, Caprari G, Carloni R, Chiacchio P, Huerzeler C, Lippiello V, Naldi R, Janosch N, Siciliano B, Stramigioli S, Zwicker E (2012) Aerial service robotics: the AIRobots perspective. In: 2nd international conference on applied robotics for the power industry. Zurich Switzerland, pp 64–69Google Scholar
  24. 24.
    Marconi L, Naldi R, Torre A, Nikolic J, Huerzeler C, Caprari G, Zwicker E, Siciliano B, Lippiello V, Carloni R, Stramigioli S (2012) Aerial service robots: an overview of the AIRobots activity. In: 2nd international conference on applied robotics for the power industry. Zurich, Switzerland, pp 76–77Google Scholar
  25. 25.
    Marconi L, Melchiorri C, Beetz M, Pangercic D, Siegwart R, Leutenegger S, Carloni R, Stramigioli S, Bruyninckx H, Doherty P, Kleiner A, Lippiello V, Finzi A, Siciliano B, Sala A, Tomatis N (2012) The SHERPA project: smart collaboration between humans and ground-aerial robots for improving rescuing activities in alpine environments. In: Proceedings of the IEEE international workshop on safety security and rescue robotics (SSRR).pp 1–4Google Scholar
  26. 26.
    Naldi R, Marconi M, Gentili L (2011) Modelling and control of a flying robot interacting with the environment. J IFAC 4(12): 2571–2583MathSciNetGoogle Scholar
  27. 27.
    Nikolic J, Burri M, Rehder J, Leutenegger S, Hurzeler C, Siegwart R (2013) A UAV system for inspection of industrial facilities. In: Proceedings of the IEEE aerospace conference (AeroConf). pp 1–8Google Scholar
  28. 28.
    Rao A S, Georgeff M P (1991) Deliberation and its role in the formation of intentions. In: UAI. pp 300–307Google Scholar
  29. 29.
    Stentz A (1995) Optimal and efficient path planning for unknown and dynamic environments. Int J Robot Autom 10(3): 89– 100Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Jonathan Cacace
    • 1
  • Alberto Finzi
    • 1
  • Vincenzo Lippiello
    • 1
  • Giuseppe Loianno
    • 1
  • Dario Sanzone
    • 1
  1. 1.DIETIUniversità degli Studi di Napoli Federico IINaplesItaly

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