Introducing Intelligence and Autonomy into Industrial Robots to Address Operations into Dangerous Area

  • Agostino G. BruzzoneEmail author
  • Marina Massei
  • Riccardo Di Matteo
  • Libor Kutej
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11472)


The paper addresses the issue to use new generation robotic systems inside industrial facilities in order to complete operations in dangerous area. The new robotic systems are currently adopting the autonomous approach already in use in military sector; however, in this context the intensity of operations and the necessity to interact with high productivity systems introduce different challenges. Despite the problems, it is evident that this approach could provide very interesting improvements in terms of safety for humans especially in relations to dangerous area. For instance, in confined spaces, Oil & Gas or Hot Metal Industry these new autonomous systems could reduce the number of injures and casualties. In addition, these systems could increase the operation efficiency in this complex frameworks as well as the possibility to carry out inspections systematically; in this sense, this could result in improving the overall reliability, productivity and safety of the whole Industrial Plant. Therefore, it is important to consider that these systems could be used to address also security aspects such as access control, however they could result vulnerable to new threats such as the cyber ones and need to be properly designed in terms of single entities, algorithms, infrastructure and architecture. From this point of view, it is evident that Modeling and Simulation represent the main approach to design properly these new systems. In this paper, the authors present the use of autonomous systems introducing advanced capabilities supported by Artificial Intelligence to deal with complex operations in dangerous industrial frameworks. The proposed examples in oil and gas and hot metal industry confirm the potential of these systems and demonstrate as simulation supports their introduction in terms of engineering, testing, installation, ramp up and training.


Artificial Intelligence Autonomous systems Safety Industrial plants Security Modeling and Simulation 


  1. 1.
    Altawy, R., Youssef, A.M.: Security, privacy, and safety aspects of civilian drones: a survey. ACM Trans. Cyber-Phys. Syst. 1(2), 7 (2016)CrossRefGoogle Scholar
  2. 2.
    Apvrille, L., Roudier, Y., Tanzi, T.J.: Autonomous drones for disasters management: safety and security verifications. In: Proceedings of 1st IEEE URSI Atlantic, May 2015Google Scholar
  3. 3.
    Bass, T.: Intrusion detection systems and multisensor data fusion. Commun. ACM 43(4), 99–105 (2000)CrossRefGoogle Scholar
  4. 4.
    Bruzzone, A.G., et al.: Autonomous systems & safety issues: the roadmap to enable new advances in industrial applications. In: Proceedings of I3 M, Barcellona, Spain, September 2017Google Scholar
  5. 5.
    Bruzzone, A.G.: Information security: threats and opportunities in a safegurading perspective. Lectio Magistralis as Keynote Speech at World Engineering Forum, Rome, November 2017Google Scholar
  6. 6.
    Bruzzone, A., et al.: Disasters and emergency management in chemical and industrial plants: drones simulation for education and training. In: Hodicky, J. (ed.) MESAS 2016. LNCS, vol. 9991, pp. 301–308. Springer, Cham (2016). Scholar
  7. 7.
    Bruzzone, A.G., et al.: Simulation models for hybrid warfare and population simulation. In: Proceedings of NATO Symposium on Ready for the Predictable, Prepared for the Unexpected, M&S for Collective Defence in Hybrid Environments and Hybrid Conflicts, Bucharest, Romania, 17–21 October 2016Google Scholar
  8. 8.
    Bruzzone, A.G., Massei, M., Maglione, G.L., Di Matteo, R., Franzinetti, G.: Simulation of manned & autonomous systems for critical infrastructure protection. In: Proceedings of I3 M, Larnaca, Cyprus, September 2016Google Scholar
  9. 9.
    Bruzzone, A.G., Massei, M., Agresta, M., Poggi, S., Camponeschi, F., Camponesch, M.: Addressing strategic challenges on mega cities through MS2G. In: Proceedings of MAS, Bordeaux, France, 12–14 September (2014)Google Scholar
  10. 10.
    Bruzzone, A.G., et al.: Virtual framework for testing/experiencing potential of collaborative autonomous systems. In: Proceedings of I/ITSEC, Orlando, FL, USA (2013)Google Scholar
  11. 11.
    Bürkle, A., Segor, F., Kollmann, M.: Towards autonomous micro uav swarms. J. Intell. Robot. Syst. 61(1–4), 339–353 (2011)CrossRefGoogle Scholar
  12. 12.
    Cárdenas, A.A., Amin, S., Lin, Z.S., Huang, Y.L., Huang, C.Y., Sastry, S.: Attacks against process control systems: risk assessment, detection, and response. In: Proceedings of the 6th ACM Symposium on Information, Computer and Communications Security, March, pp. 355–366 (2011)Google Scholar
  13. 13.
    Clarke, R., Moses, L.B.: The regulation of civilian drones’ impacts on public safety. Comput. Law Secur. Rev. 30(3), 263–285 (2014)CrossRefGoogle Scholar
  14. 14.
    Di Donato, L.: Intelligent systems for safety of industrial operators, the role of machines & equipment laboratories. In: SISOM Workshop, Rome (2017)Google Scholar
  15. 15.
    Djellal, F., Gallouj, F.: Services and the search for relevant innovation indicators: a review of national and international surveys. Sci. Public Policy 26(4), 218–232 (1999)CrossRefGoogle Scholar
  16. 16.
    Doherty, P., Rudol, P.: A UAV search and rescue scenario with human body detection and geolocalization. In: Orgun, M.A., Thornton, J. (eds.) AI 2007. LNCS (LNAI), vol. 4830, pp. 1–13. Springer, Heidelberg (2007). Scholar
  17. 17.
    Feddema, J.T., Lewis, C., Schoenwald, D.A.: Decentralized control of cooperative robotic vehicles: theory and application. IEEE Trans. Robot. Autom. 18(5), 852–864 (2002)CrossRefGoogle Scholar
  18. 18.
    Ferrandez, J.M., De Lope, H., De la Paz, F.: Social and collaborative robotics. Int. J. Robot. Auton. Syst. 61 (2013)Google Scholar
  19. 19.
    Floreano, D., Wood, R.J.: Science, technology and the future of small autonomous drones. Nature 521(7553), 460 (2015)CrossRefGoogle Scholar
  20. 20.
    Gardi, A., Sabatini, R., Ramasamy, S.: Stand-off measurement of industrial air pollutant emissions from unmanned aircraft. In: Proceedings of IEEE International Conference on Unmanned Aircraft Systems, June, pp. 1162–1171 (2016)Google Scholar
  21. 21.
    Grocholsky, B., Keller, J., Kumar, V., Pappas, G.: Cooperative air and ground surveillance. Robot. Autom. Mag. 13(3), 16–25 (2006)CrossRefGoogle Scholar
  22. 22.
    Ishiki, T., Kumon, M.: A microphone array configuration for an auditory quadrotor helicopter system. In: Proceedings IEEE International Symposium on Safety, Security, and Rescue Robotics, pp. 1–6 (2014)Google Scholar
  23. 23.
    Jans, W., Nissen, I., Gerdes, F., Sangfelt, E., Solberg, C.E., van Walree, P.: UUV covert acoustic communications – preliminary results of the first sea experiment. In: Techniques and technologies for unmanned autonomous underwater vehicles – a dual use view, RTO Workshop SCI-182/RWS-016, Eckernförde, Germany (2006)Google Scholar
  24. 24.
    Jones, D.: Power line inspection-a UAV concept. In: Proceedings of the IEEE Forum on Autonomous Systems, Ref. No. 11271, November 2005Google Scholar
  25. 25.
    Kastek, M., et al.: Multisensor system for the protection of critical infrastructure of seaport. In: Proceedings of SPIE, vol. 8288, May 2012Google Scholar
  26. 26.
    Kehoe, B., Patil, S., Abbeel, P., Goldberg, K.: A survey of research on cloud robotics and automation. IEEE Trans. Autom. Sci. Eng. 12(2), 398–409 (2015)CrossRefGoogle Scholar
  27. 27.
    Kim, D.H., Kwon, S.W., Jung, S.W., Park, S., Park, J.W., Seo, J.W.: A study on generation of 3D model and mesh image of excavation work using UAV. In: Proceedings of the International Symposium on Automation and Robotics in Construction, Vilnius, vol. 32, January 2015Google Scholar
  28. 28.
    Kovacevic, M.S., Gavin, K., Oslakovic, I.S., Bacic, M.: A new methodology for assessment of railway infrastructure condition. Transp. Res. Procedia 14, 1930–1939 (2016)CrossRefGoogle Scholar
  29. 29.
    Leão, D.T., Santos, M.B.G., Mello, M.C.A., Morais, S.F.A.: Consideration of occupational risks in construction confined spaces in a brewery. In: Occupational Safety & Hygiene III, vol. 343 (2015)Google Scholar
  30. 30.
    Magrassi, C.: Education and training: delivering cost effective readiness for tomorrow’s operations. ITEC Keynote Speech, Rome, May 2013Google Scholar
  31. 31.
    Maravall, D., de Lope, J., Domíngueza, R.: Coordination of communication in robot teams by reinforcement learning. Robot. Auton. Syst. 61, 661–666 (2013)CrossRefGoogle Scholar
  32. 32.
    McCurry, J.: Dying robots and failing hope: fukushima clean-up falters six years after Tsunami. The Guardian, 9 March 2017Google Scholar
  33. 33.
    Merabti, M., Kennedy, M., Hurst, W.: Critical infrastructure protection: A 21 st century challenge. In: Proceedings of IEEE International Conference on Communications and Information Technology, ICCIT, March, pp. 1–6 (2011)Google Scholar
  34. 34.
    Merwaday, A., Guvenc, I.: UAV assisted heterogeneous networks for public safety communications. In: Proceedings of IEEE Wireless Communications and Networking Conference Workshops, March, pp. 329–334 (2015)Google Scholar
  35. 35.
    Mobley, R.K.: Plant Engineer’s Handbook. Butterworth-Heinemann, Oxford (2001)Google Scholar
  36. 36.
    Nano, G., Derudi, M.: A critical analysis of techniques for the reconstruction of workers accidents. Chem. Eng. 31 (2013)Google Scholar
  37. 37.
    Palazzi, E., Caviglione, C., Reverberi, A.P., Fabiano, B.: A short-cut analytical model of hydrocarbon pool fire of different geometries, with enhanced view factor evaluation. Process Saf. Environ. Prot. 110, 89–101 (2017)CrossRefGoogle Scholar
  38. 38.
    Pizzella, L.A.E.: Contributions to the Configuration of Fleets of Robots for Precision Agriculture. Thesis, Universidad Complutense, Madrid, Spain, May 2014Google Scholar
  39. 39.
    Pulina, G., Canalis, C., Manni, C., Casula, A., Carta, L.A., Camarda, I.: Using a GIS technology to plan an agroforestry sustainable system in Sardinia. J. Agric. Eng. 47(s1), 23 (2016)Google Scholar
  40. 40.
    Richards, A., Bellingham, J., Tillerson, M., How, J.: Co-ordination and control of multiple UAVs. In: Proceedings of the AIAA Guidance, Navigation, and Control Conference, Monterey, CA, August 2002Google Scholar
  41. 41.
    Ross, S., Jacques, D., Pachter, M., Raquet, J.: A close formation flight test for automated air refueling. In: Proceedings of ION GNSS-2006, Fort Worth, TX, September 2006Google Scholar
  42. 42.
    Salvini, P.: Urban robotics: towards responsible innovations for our cities. Robot. Auton. Syst. 100, 278–286 (2017)CrossRefGoogle Scholar
  43. 43.
    Sanchez-Lopez, J.L., Pestana, J., de la Puente, P., Campoy, P.: A reliable open-source system architecture for the fast designing and prototyping of autonomous multi-uav systems: Simulation and experimentation. J. Intell. Robot. Syst. 84(1–4), 779–797 (2016)CrossRefGoogle Scholar
  44. 44.
    Shafer, A.J., Benjamin, M.R., Leonard, J.J., Curcio, J.: Autonomous cooperation of heterogeneous platforms for sea-based search tasks. Oceans, 15–18 September, pp. 1–10 (2008)Google Scholar
  45. 45.
    Shkurti, F., et al.: Multi-domain monitoring of marine environments using a heterogeneous robot team. In: Proceedings of IEEE Intelligent Robots and Systems (IROS), pp. 1747–1753, 7–12 October 2012Google Scholar
  46. 46.
    Siebert, S., Teizer, J.: Mobile 3D mapping for surveying earthwork projects using an Unmanned Aerial Vehicle (UAV) system. Autom. Constr. 41, 1–14 (2014)CrossRefGoogle Scholar
  47. 47.
    Spanu, S., et al.: Feasibility study of an Augmented Reality application to enhance the operators’ safety in the usage of a fruit extractor. In: Proceedings of FoodOPS, Larnaca, Cyprus, 26–28 September 2016Google Scholar
  48. 48.
    Spillane, J.P., Oyedele, L.O., Von Meding, J.: Confined site construction: an empirical analysis of factors impacting health and safety management. J. Eng. Des. Technol. 10(3), 397–420 (2012)Google Scholar
  49. 49.
    Stilwell, D.J., Gadre, A.S., Sylvester, C.A., Cannell, C.J.: Design elements of a small low-cost autonomous underwater vehicle for field experiments in multi-vehicle coordination. In: Proceedings of the IEEE/OES Autonomous Underwater Vehicles, June, pp. 1–6 (2004)Google Scholar
  50. 50.
    Sujit, P.B., Sousa, J., Pereira, F.L.: UAV and AUVs coordination for ocean exploration. In: Oceans - EUROPE, 11–14 May, pp. 1–7 (2009)Google Scholar
  51. 51.
    Tanner, H.G.: Switched UAV-UGV cooperation scheme for target detection. In: IEEE International Conference on Robotics and Automation, Roma, Italy, April, pp. 3457–3462 (2007)Google Scholar
  52. 52.
    Tanner, H.G., Christodoulakis, D.K.: Decentralized cooperative control of heterogeneous vehicle groups. Robot. Auton. Syst. 55, 811–823 (2007)CrossRefGoogle Scholar
  53. 53.
    Tether, T.: Darpa Strategic Plan. Technical Report DARPA, May 2009Google Scholar
  54. 54.
    Vail, D., Veloso, M.: Dynamic multi-robot coordination. In: Multi-Robot Systems: From Swarms to Intelligent Automata, vol. II, pp. 87–100 (2003)Google Scholar
  55. 55.
    Valavanis, K.P., Vachtsevanos, G.J.: Handbook of Unmanned Aerial Vehicles. Springer, New York (2014). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Agostino G. Bruzzone
    • 1
    Email author
  • Marina Massei
    • 1
  • Riccardo Di Matteo
    • 2
  • Libor Kutej
    • 3
  1. 1.Simulation Team, DIMEUniversity of GenoaGenoaItaly
  2. 2.Simulation TeamSIM4FutureGenoaItaly
  3. 3.University of Defence in BrnoBrnoCzech Republic

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