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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References
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)
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
Arcaini, P., Riccobene, E., Scandurra, P.: Modeling and analyzing MAPE-K feedback loops for self-adaptation. Proc. SEAMS 2015, 13–23 (2015)
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)
Brs̆c̆ić, D., Kidokoro, H., Suehiro, Y., Kanda, T.: Escaping from children’s abuse of social robots. In: Proceedings HRI, pp. 59–66 (2015)
Caro, T.: Antipredator Defenses in Birds and Mammals. University of Chicago Press, Chicago (2005)
Caro, T.: Antipredator deception in terrestrial vertebrates. Curr. Zool. 60(1), 16–25 (2014)
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)
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)
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)
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)
Domenici, P., Blagburn, J.M., Bacon, J.P.: Animal escapology II: escape trajectory case studies. J. Exp. Biol. 214(15), 2474–2494 (2011)
Helfman, G.S.: Threat-sensitive predator avoidance in damselfish-trumpetfish interactions. Behav. Ecol. Sociobiol. 24(1), 47–58 (1989)
Humphries, D.A., Driver, P.M.: Protean defence by prey animals. Oecologia 5(4), 285–302 (1970)
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)
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)
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)
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)
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)
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)
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)
Tews, A., Mataric, M., Sukhatme, G.: Avoiding detection in a dynamic environment. In: Proceedings of IROS, pp. 3773–3778 (2004)
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)
Woodman, R., Winfield, A.F., Harper, C., Fraser, M.: Building safer robots: safety driven control. Int. J. Robot. Res. 31(13), 1603–1626 (2012)
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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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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