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Risk-Based Triggering of Bio-inspired Self-preservation to Protect Robots from Threats

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Towards Autonomous Robotic Systems (TAROS 2017)

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Abstract

Safety in autonomous systems has been mostly studied from a human-centered perspective. Besides the loads they may carry, autonomous systems are also valuable property, and self-preservation mechanisms are needed to protect them in the presence of external threats, including malicious robots and antagonistic humans. We present a biologically inspired risk-based triggering mechanism to initiate self-preservation strategies. This mechanism considers environmental and internal system factors to measure the overall risk at any moment in time, to decide whether behaviours such as fleeing or hiding are necessary, or whether the system should continue on its task. We integrated our risk-based triggering mechanism into a delivery rover that is being attacked by a drone and evaluated its effectiveness through systematic testing in a simulated environment in Robot Operating System (ROS) and Gazebo, with a variety of different randomly generated conditions. We compared the use of the triggering mechanism and different configurations of self-preservation behaviours to not having any of these. Our results show that triggering self-preservation increases the distance between the drone and the rover for many of these configurations, and, in some instances, the drone does not catch up with the rover. Our study demonstrates the benefits of embedding risk awareness and self-preservation into autonomous systems to increase their robustness, and the value of using bio-inspired engineering to find solutions in this area.

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Notes

  1. 1.

    http://www.ros.org/.

  2. 2.

    http://gazebosim.org/.

  3. 3.

    http://wiki.ros.org/Robots/Jackal.

  4. 4.

    http://wiki.ros.org/hector_quadrotor.

  5. 5.

    https://github.com/riveras/self-preservation.

References

  1. Amo, L., López, P., Martín, J.: Wall lizards combine chemical and visual cues of ambush snake predators to avoid overestimating risk inside refuges. Anim. Behav. 67(4), 647–653 (2004)

    Article  Google Scholar 

  2. Araiza-Illan, D., Dodd, T.J.: Bio-inspired autonomous navigation and escape from pursuers with potential functions. In: Herrmann, G., Studley, M., Pearson, M., Conn, A., Melhuish, C., Witkowski, M., Kim, J.-H., Vadakkepat, P. (eds.) TAROS 2012. LNCS, vol. 7429, pp. 84–95. Springer, Heidelberg (2012). doi:10.1007/978-3-642-32527-4_8

    Chapter  Google Scholar 

  3. Arcaini, P., Riccobene, E., Scandurra, P.: Modeling and analyzing MAPE-K feedback loops for self-adaptation. Proc. SEAMS 2015, 13–23 (2015)

    Google Scholar 

  4. Barnett, C., Bateson, M., Rowe, C.: State-dependent decision making: educated predators strategically trade off the costs and benefits of consuming aposematic prey. Behav. Ecol. 18(4), 645–651 (2007)

    Article  Google Scholar 

  5. Brs̆c̆ić, D., Kidokoro, H., Suehiro, Y., Kanda, T.: Escaping from children’s abuse of social robots. In: Proceedings HRI, pp. 59–66 (2015)

    Google Scholar 

  6. Caro, T.: Antipredator Defenses in Birds and Mammals. University of Chicago Press, Chicago (2005)

    Google Scholar 

  7. Caro, T.: Antipredator deception in terrestrial vertebrates. Curr. Zool. 60(1), 16–25 (2014)

    Article  Google Scholar 

  8. Chivers, D.P., McCormick, M.I., Mitchell, M.D., Ramasamy, R.A., Ferrari, M.C.O.: Background level of risk determines how prey categorize predators and non-predators. Proc. Roy. Soc. Lond. B Biol. Sci. 281(1787), 1–6 (2014)

    Article  Google Scholar 

  9. Cooper, W.E., Stankowich, T.: Prey or predator? Body size of an approaching animal affects decisions to attack or escape. Behav. Ecol. 21(6), 1278–1284 (2010)

    Article  Google Scholar 

  10. Curiac, D.I., Volosencu, C.: Imparting protean behavior to mobile robots accomplishing patrolling tasks in the presence of adversaries. Bioinspiration Biomimetics 10, 1–10 (2015)

    Article  Google Scholar 

  11. Dogramadzi, S., Giannaccini, M.E., Harper, C., Sobhani, M., Woodman, R., Choung, J.: Environmental hazard analysis - a variant of preliminary hazard analysis for autonomous mobile robots. J. Intell. Robot. Syst. 76(1), 73–117 (2014)

    Article  Google Scholar 

  12. Domenici, P., Blagburn, J.M., Bacon, J.P.: Animal escapology II: escape trajectory case studies. J. Exp. Biol. 214(15), 2474–2494 (2011)

    Article  Google Scholar 

  13. Helfman, G.S.: Threat-sensitive predator avoidance in damselfish-trumpetfish interactions. Behav. Ecol. Sociobiol. 24(1), 47–58 (1989)

    Article  Google Scholar 

  14. Humphries, D.A., Driver, P.M.: Protean defence by prey animals. Oecologia 5(4), 285–302 (1970)

    Article  Google Scholar 

  15. Martín, J., López, P.: When to come out from a refuge: risk-sensitive and state-dependent decisions in an alpine lizard. Behav. Ecol. 10(5), 487–492 (1999)

    Article  Google Scholar 

  16. Martin-Guillerez, D., Guiochet, J., Powell, D., Zanon, C.: A UML-based method for risk analysis of human-robot interactions. In: Proceedings SERENE, pp. 32–41 (2010)

    Google Scholar 

  17. Rezazadegan, F., Geng, J., Ghirardi, M., Menga, G., Murè, S., Camuncoli, G., Demichela, M.: Risk-based design for the physical human-robot interaction (pHRI): an overview. Chem. Eng. Trans. 43, 1249–1254 (2015)

    Google Scholar 

  18. Salvini, P., Ciaravella, G., Yu, W., Ferri, G., Manzi, A., Mazzolai, B., Laschi, C., Oh, S., Dario, P.: How safe are service robots in urban environments? Bullying a robot. In: Proceedings of RO-MAN, pp. 1–7 (2010)

    Google Scholar 

  19. Smith, M.E., Belk, M.C.: Risk assessment in western mosquitofish (Gambusia affinis): do multiple cues have additive effects? Behav. Ecol. Sociobiol. 51(1), 101–107 (2001)

    Article  Google Scholar 

  20. Stankowich, T., Blumstein, D.T.: Fear in animals: a meta-analysis and review of risk assessment. Proc. Roy. Soc. Lond. B Biol. Sci. 272(1581), 2627–2634 (2005)

    Article  Google Scholar 

  21. Stankowich, T., Coss, R.G.: Effects of predator behavior and proximity on risk assessment by columbian black-tailed deer. Behav. Ecol. 17(2), 246–254 (2006)

    Article  Google Scholar 

  22. Tews, A., Mataric, M., Sukhatme, G.: Avoiding detection in a dynamic environment. In: Proceedings of IROS, pp. 3773–3778 (2004)

    Google Scholar 

  23. Wang, G., Chen, X., Liu, S., Wong, C., Chu, S.: Mechanical chameleon through dynamic real-time plasmonic tuning. ACS Nano 10(2), 1788–1794 (2016)

    Article  Google Scholar 

  24. Woodman, R., Winfield, A.F., Harper, C., Fraser, M.: Building safer robots: safety driven control. Int. J. Robot. Res. 31(13), 1603–1626 (2012)

    Article  Google Scholar 

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Acknowledgement

The work by D. Araiza-Illan and K. Eder was funded by the EPSRC project “Robust Integrated Verification of Autonomous Systems” (ref. EP/J01205X/1).

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Correspondence to Dejanira Araiza-Illan .

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Chiu, SK., Araiza-Illan, D., Eder, K. (2017). Risk-Based Triggering of Bio-inspired Self-preservation to Protect Robots from Threats. In: Gao, Y., Fallah, S., Jin, Y., Lekakou, C. (eds) Towards Autonomous Robotic Systems. TAROS 2017. Lecture Notes in Computer Science(), vol 10454. Springer, Cham. https://doi.org/10.1007/978-3-319-64107-2_14

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  • DOI: https://doi.org/10.1007/978-3-319-64107-2_14

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