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Risk Areas Determination for Autonomous- and Semi-autonomous Aerial Systems Considering Run-Time Technical Reliability Assessment

Requirements, Concept, and Tests

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Abstract

Autonomous or semi-autonomous aerial systems are used in different application domains to simplify or assist humans tasks. In this context, their behavior has to be verifiably safe. Here safe behavior denotes system’s interaction with the environment with freedom from unacceptable risk for the environment and the system itself. Traditionally, run-time technical reliability assessment is not considered for risk areas determination. Run-time definition and online control of risk areas based on current technical reliability may be used as a function to assure safe behavior of autonomous or semi-autonomous aerial systems. This contribution introduces a novel technique for the definition and control of risk areas considering system’s behavior as well as it’s technical reliability during run-time. The technique is used to separate the space around an autonomous aerial system into risk-related areas. On this basis, the safety unit can realize emergency actions to ensure system’s safe behavior if necessary. The introduction of the novel technique is realized in the context of the safety unit description and run-time technical reliability assessment. For the run-time technical reliability assessment, a novel technique is introduced, inspired by the functional safety standard IEC 61508. Simulation results demonstrate the successful use of the introduced approach.

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References

  1. Aven, T.: What is safety science? Saf. Sci. 67, 15–20 (2014)

    Article  Google Scholar 

  2. United States Department of Defense: MIL-STD-882E Department of Defence Standard Practice: System Safety (2011)

  3. International Electrotechnical Commission: Iec 61508: Functional safety of electrical/electronic/programmable electronic safety-related systems (2010)

  4. Saleh, J.H., Marais, K.B., Favaró, F.M.: System safety principles: A multidisciplinary engineering perspective. J. Loss Prev. Process Ind. 29, 283–294 (2014)

    Article  Google Scholar 

  5. Sorokos, I., Azevedo, L.P., Papadopoulos, Y., Walker, M., Parker, D.J.: Comparing automatic allocation of safety integrity levels in the aerospace and automotive domains. IFAC-PapersOnLine 49(3), 184–190 (2016)

    Article  Google Scholar 

  6. Ericson, C.A.: Concise Encyclopedia of System Safety. Wiley, Hoboken (2011)

    Book  Google Scholar 

  7. US Air Force: Air Force System Safety Handbook. Air Force Safety Agency, Kirtland (2000)

    Google Scholar 

  8. Jones-Lee, M., Aven, T.: ALARP–what does it really mean? Reliab. Eng. Syst. Safety 96(8), 877–882 (2011)

    Article  Google Scholar 

  9. Ale, B., Hartford, D., Slater, D.: ALARP and CBA all in the same game. Saf. Sci. 76, 90–100 (2015)

    Article  Google Scholar 

  10. Saxena, A., Roychoudhury, I., Lin, W., Goebel, K.: Towards requirements in systems engineering for aerospace IVHM design. In: Proceedings of the AIAA Infotech@ Aerospace (2013)

  11. Roemer, M., Tang, L.: Integrated vehicle health and fault contingency management for UAVs. In: Valavanis, K.P., Vachtsevanos, G.J. (eds.) Handbook of Unmanned Aerial Vehicles, pp 999–1025. Springer, Netherlands (2015)

    Google Scholar 

  12. Tang, L., Kacprzynski, G., Goebel, K., Saxena, A., Saha, B., Vachtsevanos, G.: Prognostics-enhanced automated contingency management for advanced autonomous systems. In: International Conference on Prognostics and Health Management, 2008. PHM 2008, pp. 1–9 (2008)

  13. Federal Aviation Administration: Sense and avoid (SAA) for unmanned aircraft systems (UAS). In: Second Caucus Workshop Repor, Federal Aviation Administration (2013)

  14. Yu, X., Zhang, Y.: Sense and avoid technologies with applications to unmanned aircraft systems: Review and prospects. Progress Aerosp. Sci. 74, 152–166 (2015)

    Article  Google Scholar 

  15. Clothier, R., Williams, B., Washington, A.: Development of a template safety case for unmanned aircraft operations over populous areas. In: SAE Technical Paper. Number 2015-01-2469, SAE International (09) (2015)

  16. Lum, C.W., Waggoner, B.: A risk based paradigm and model for unmanned aerial vehicles in the national airspace. Infotech@ Aerospace 2011, 2011–1424 (2011)

    Google Scholar 

  17. Clothier, R.A., Williams, B.P., Fulton, N.L.: Structuring the safety case for unmanned aircraft system operations in non-segregated airspace. Saf. Sci. 79, 213–228 (2015)

    Article  Google Scholar 

  18. Weibel, R.E., Edwards, M.W., Fernandes, C.S.: Establishing a risk-based separation standard for unmanned aircraft self separation. In: Proceedings of the ninth USA/Europe air traffic management research & development seminar. Eurocontrol/FAA, Berlin (2011)

  19. Grzonka, S., Grisetti, G., Burgard, W.: A fully autonomous indoor quadrotor. IEEE Trans. Robot. 28 (1), 90–100 (2012)

    Article  Google Scholar 

  20. Wang, F., Cui, J., Phang, S.K., Chen, B., Lee, T.: A mono-camera and scanning laser range finder based UAV indoor navigation system. In: 2013 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 694–701 (2013)

  21. Tomic, T., Schmid, K., Lutz, P., Domel, A., Kassecker, M., Mair, E., Grixa, I., Ruess, F., Suppa, M., Burschka, D.: Toward a fully autonomous UAV: Research platform for indoor and outdoor urban search and rescue. IEEE Robot. Autom. Mag. 19(3), 46–56 (2012)

    Article  Google Scholar 

  22. Lippiello, V., Ruggiero, F., Serra, D.: Emergency landing for a quadrotor in case of a propeller failure: A PID based approach. In: 2014 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pp. 1–7 (2014)

  23. Rudnick-Cohen, E., Herrmann, J.W., Azarm, S.: Risk-based path planning optimization methods for UAVs over inhabited areas. In: 35th Computers and Information in Engineering Conference. Number 57045 (2015) V01AT02A004

  24. Cour-Harbo, A.: Quantifying risk of ground impact fatalities of power line inspection bvlos flight with small unmanned aircraft. In: 2017 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1352–1360 (2017)

  25. Sylvain, B., Raballand, N., Viguier, F., Muller, F.: Ground risk assessment for long-range inspection missions of railways by UAVs. In: 2017 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1343–1351 (2017)

  26. De Filippis, L., Guglieri, G., Quagliotti, F.: A minimum risk approach for path planning of UAVs. J. Intell. Robot. Syst. 61(1), 203–219 (2011)

    Article  Google Scholar 

  27. Hägele, G., Söffker, D.: A simplified situational environment risk and system reliability assessment for behavior assurance of autonomous and semi-autonomous aerial systems: A simulation study. In: The 2017 International Conference on Unmanned Aircraft Systems, pp. 951–960 (2017)

  28. Hägele, G., Söffker, D.: Strictly formalized situation-operator-modeling technique for fall-back layer modeling for autonomous or semi-autonomous systems requiring software-based fail-safe behavior. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 886–891 (2016)

  29. Hägele, G., Söffker, D.: Fall-back layer concept for autonomous or semi-autonomous systems and processes: requirements, concepts, and first tests. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 916–921 (2016)

  30. Hägele, G.: Contribution to realization and test of a fall-back layer for safe, autonomous, and action-flexible systems. PhD thesis, University of Duisburg-Essen (2017)

  31. Giese, S., Carr, D., Chahl, J.: Implications for unmanned systems research of military UAV mishap statistics. In: Intelligent Vehicles Symposium (IV), 2013 IEEE, pp. 1191–1196 (2013)

  32. Belcastro, C.M., Klyde, D.H., Logan, M.J., Newman, R.L., Foster, J.V.: Experimental flight testing for assessing the safety of unmanned aircraft system safety-critical operations. AIAA AVIATION Forum. American Institute of Aeronautics and Astronautics, pp. 3274–3328 (2017)

  33. Prats, X., Delgado, L., Ramirez, J., Royo, P., Pastor, E.: Requirements, issues, and challenges for sense and avoid in unmanned aircraft systems. J. Aircraft 49(3), 677–687 (2012)

    Article  Google Scholar 

  34. Cook, S.P., Brooks, D., Cole, R., Hackenberg, M.D., Raska, M.V.: Defining well clear for unmanned aircraft systems. In: Proceedings of AIAA Infotech@ Aerospace. AIAA, 481 (2015)

  35. Rajamani, R., Saxena, A., Kramer, F., Augustin, M., Schroeder, J.B., Goebel, K., Shao, G., Roychoudhury, I., Lin, W.: Developing IVHM requirements for aerospace systems. Technical report, SAE Technical Paper (2013)

  36. Benedettini, O., Baines, T.S., Lightfoot, H.W., Greenough, R.M.: State-of-the-art in integrated vehicle health management. Proc. Institut. Mech. Eng. Part G: J. Aerosp. Eng. 223(2), 157–170 (2009)

    Article  Google Scholar 

  37. Endsley, M.R.: Toward a theory of situation awareness in dynamic systems. Human Factors: J. Human Factors Ergon. Soc. 37(1), 32–64 (1995)

    Article  Google Scholar 

  38. Wu, P.P., Clothier, R.A.: The development of ground impact models for the analysis of the risks associated with unmanned aircraft operations over inhabited areas. In: 11th Probabilistic Safety Assessment and Management Conference (PSAM11) and the Annual European Safety and Reliability Conference (ESREL 2012) (2012)

  39. Ancel, E., Capristan, F.M., Foster, J.V., Condotta, R.C.: Real-time risk assessment framework for unmanned aircraft system (UAS) traffic management (UTM). AIAA AVIATION Forum American Institute of Aeronautics and Astronautics (2017)

  40. Roberge, V., Tarbouchi, M., Labonte, G.: Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning. IEEE Trans. Indus. Inf. 9(1), 132–141 (2013)

    Article  Google Scholar 

  41. Goerzen, C., Kong, Z., Mettler, B.: A survey of motion planning algorithms from the perspective of autonomous UAV guidance. In: Valavanis, K., Beard, R., Oh, P., Ollero, A., Piegl, L., Shim, H. (eds.) Selected Papers from the 2nd International Symposium on UAVs, Reno, Nevada, U.S.A. June 8-10, 2009, pp 65–100. Springer, Netherlands (2010)

  42. Wang, F., Cui, J.Q., Chen, B.M., Lee, T.H.: A comprehensive UAV indoor navigation system based on vision optical flow and laser fastslam. Acta Automatica Sinica 39(11), 1889–1899 (2013)

    Google Scholar 

  43. Lippiello, V., Ruggiero, F., Serra, D.: Emergency landing for a quadrotor in case of a propeller failure: A backstepping approach. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), pp. 4782–4788 (2014)

  44. Fang, B., Chen, T.: Research on UAV collision avoidance strategy considering threat levels. In: Wen, Z., Li, T. (eds.) Practical Applications of Intelligent Systems. Volume 279 of Advances in Intelligent Systems and Computing, pp 887–897. Springer, Berlin (2014)

    Google Scholar 

  45. Lioulemes, A., Galatas, G., Metsis, V., Mariottini, G.L., Makedon, F.: Safety challenges in using AR.Drone to collaborate with humans in indoor environments. In: Proceedings of the 7th International Conference on Pervasive Technologies Related to Assistive Environments, vol. 33, pp. 1–33:4 (2014)

  46. Mori, T., Scherer, S.: First results in detecting and avoiding frontal obstacles from a monocular camera for micro unmanned aerial vehicles. In: 2013 IEEE International Conference on Robotics and Automation (ICRA), pp. 1750–1757 (2013)

  47. Gageik, N., Benz, P., Montenegro, S.: Obstacle detection and collision avoidance for a UAV with complementary low-cost sensors. Access, IEEE 3, 599–609 (2015)

    Article  Google Scholar 

  48. Gupta, N., Makkar, J., Pandey, P.: Obstacle detection and collision avoidance using ultrasonic sensors for RC multirotors. In: 2015 International Conference on Signal Processing and Communication (ICSC), pp. 419–423 (2015)

  49. Müller, J., Ruiz, A., Wieser, I.: Safe & sound: A robust collision avoidance layer for aerial robots based on acoustic sensors. In: 2014 IEEE/ION on Position, Location and Navigation Symposium - PLANS 2014, pp. 1197–1202 (2014)

  50. Nieuwenhuisen, M., Droeschel, D., Schneider, J., Holz, D., Labe, T., Behnke, S.: Multimodal obstacle detection and collision avoidance for micro aerial vehicles. In: 2013 European Conference on Mobile Robots (ECMR), pp. 7–12 (2013)

  51. Hägele, G., Söffker, D.: System safety surveillance and control system concept for autonomous or semi-autonomous systems fall-back layer realization. In: ASME 2016 Dynamic Systems and Control Conference. American Society of Mechanical Engineers (2016) V002T29A003

  52. Söffker, D.: Interaction of intelligent and autonomous systems - part i: Qualitative structuring of interaction. Math. Comput. Model. Dyn. Syst. 14(4), 303–318 (2008)

    Article  MathSciNet  Google Scholar 

  53. 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, pp. 1321–1326 (2013)

  54. Chamseddine, A., Theilliol, D., Zhang, Y., Join, C., Rabbath, C.: Active fault-tolerant control system design with trajectory re-planning against actuator faults and saturation: Application to a quadrotor unmanned aerial vehicle. Int. J. Adapt. Control Signal Process. 29(1), 1–23 (2015)

    Article  MathSciNet  Google Scholar 

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Hägele, G., Söffker, D. Risk Areas Determination for Autonomous- and Semi-autonomous Aerial Systems Considering Run-Time Technical Reliability Assessment. J Intell Robot Syst 97, 511–529 (2020). https://doi.org/10.1007/s10846-019-01056-4

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