Abstract
In recent years drones have become more widely used in military and non-military applications. Automation of these drones will become more important as their use increases. Individual drones acting autonomously will be able to achieve some tasks, but swarms of autonomous drones working together will be able to achieve much more complex tasks and be able to better adapt to changing environments. In this paper we describe an example scenario involving a swarm of drones from a military coalition and civil/humanitarian organisations that are working collaboratively to monitor areas at risk of flooding. We provide a definition of a swarm and how they can operate by exchanging messages. We define a flexible set of policies that are applicable to our scenario that can be easily extended to other scenarios or policy paradigms. These policies ensure that the swarms of drones behave as expected (e.g., for safety and security). Finally we discuss the challenges and limitations around policies for autonomous swarms and how new research, such as generative policies, can aid in solving these limitations.
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- 1.
For the sake of simplicity, during the description of the scenario, we use a small number of drones.
- 2.
For the sake of readability, we decided to do not illustrate the surveillance reports in the diagram.
- 3.
We assume that we deal with one request for performing an action at a time, and in the case the leader requests an action to a drone while it is still performing a previous one, then it drops the previous action and starts the new one.
- 4.
References
Darpa: Service academies swarm challenge live-fly competition (2017). https://www.youtube.com/watch?v=RZ-CKA4fUhg
Amazon.com Inc.: Amazon Prime Air. https://www.amazon.com/b?node=8037720011
Amazon.com Inc.: Determining safe access with a best-equipped, best-served model for small unmanned aircraft systems (2015). https://images-na.ssl-images-amazon.com/images/G/01/112715/download/Amazon_Determining_Safe_Access_with_a_Best-Equipped_Best-Served_Model_for_sUAS.pdf
Amazon.com Inc.: Revising the airspace model for the safe integration of small unmanned aircraft systems’ (2015). https://images-na.ssl-images-amazon.com/images/G/01/112715/download/Amazon_Revising_the_Airspace_Model_for_the_Safe_Integration_of_sUAS.pdf
Amgoud, L., Dimopoulos, Y., Moraitis, P.: Making decisions through preference-based argumentation. In: Brewka, G., Lang, J. (eds.) KR, pp. 113–123. AAAI Press (2008)
Bondarenko, A., Dung, P.M., Kowalski, R.A., Toni, F.: An abstract, argumentation-theoretic approach to default reasoning. Artif. Intelli. 93(1–2), 63–101 (1997)
Bouvry, P., et al.: ASIMUT project: aid to SItuation management based on MUltimodal, MUltiUAVs, MUltilevel acquisition techniques. In: Proceedings of the 3rd Workshop on Micro Aerial Vehicle Networks, Systems, and Applications, pp. 17–20. ACM (2017)
Cortes, J., Martinez, S., Karatas, T., Bullo, F.: Coverage control for mobile sensing networks. In: IEEE International Conference on Robotics and Automation (ICRA 2002), vol. 2, pp. 1327–1332. IEEE (2002)
Craven, R., Lobo, J., Ma, J., Russo, A., Lupu, E.C., Bandara, A.K.: Expressive policy analysis with enhanced system dynamicity. In: Proceedings of the 2009 ACM Symposium on Information, Computer and Communications Security, ASIACCS, pp. 239–250 (2009)
Cullen, A., Williams, B., Bertino, E., Arunkumar, S., Karafili, E., Lupu, E.: Mission support for drones: a policy based approach. In: Proceedings of the 3rd Workshop on Micro Aerial Vehicle Networks, Systems, and Applications, DroNet@MobiSys 2017, pp. 7–12 (2017)
(EASA): EASA concept of operation for drones: a risk based approach to regulation of unmanned aircraft (2015). https://www.easa.europa.eu/sites/default/files/dfu/204696_EASA_concept_drone_brochure_web.pdf (2015)
Gharibi, M., Boutaba, R., Waslander, S.L.: Internet of drones. IEEE Access 4, 1148–1162 (2016)
Hunjet, R., Stevens, T., Elliot, M., Fraser, B., George, P.: Survivable communications and autonomous delivery service a generic swarming framework enabling communications in contested environments. In: IEEE Military Communications Conference (MILCOM), pp. 788–793 (2017)
Google Inc.: Google UAS Airspace System Overview (2015). https://utm.arc.nasa.gov/docs/GoogleUASAirspaceSystemOverview5pager[1].pdf
Joint doctrine note 2/11: the UK approach to unmanned aircraft systems, March 2011
Kakas, A.C., Kowalski, R.A., Toni, F.: Abductive logic programming. J. log. Comput. 2(6), 719–770 (1992)
Karafili, E., Kakas, A.C., Spanoudakis, N.I., Lupu, E.C.: Argumentation-based security for social good. In: AAAI Fall Symposium Series, pp. 164–170 (2017)
Karafili, E., Lupu, E.C.: Enabling data sharing in contextual environments: policy representation and analysis. In: Proceedings of the 22nd ACM on Symposium on Access Control Models and Technologies, SACMAT, pp. 231–238 (2017)
Karafili, E., Lupu, E.C., Arunkumar, S., Bertino, E.: Argumentation-based policy analysis for drone systems. In: 2017 IEEE SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI, pp. 1–6 (2017)
Karafili, E., Lupu, E.C., Cullen, A., Williams, B., Arunkumar, S., Calo, S.B.: Improving data sharing in data rich environments. In: IEEE International Conference on Big Data, BigData 2017, pp. 2998–3005 (2017)
Karafili, E., Spanaki, K., Lupu, E.C.: An argumentation reasoning approach for data processing. Comput. Ind. 94, 52–61 (2018)
Murukannaiah, P.K., Kalia, A.K., Telangy, P.R., Singh, M.P.: Resolving goal conflicts via argumentation-based analysis of competing hypotheses. In: IEEE 23rd International Requirements Engineering Conference (RE), pp. 156–165 (2015)
NASA: NASA UTM 2015: the next era of aviation (2015). https://utm.arc.nasa.gov/utm2015.shtml
Prakken, H., Sartor, G.: Argument-based extended logic programming with defeasible priorities. J. Appl. Non-Class. Log. 7(1), 25–75 (1997)
Reynolds, C.W.: Flocks, herds and schools: a distributed behavioral model. In: ACM SIGGRAPH Computer Graphics, vol. 21, pp. 25–34. ACM (1987)
Steve, O.E., Hanks, D.W., Draper, D.: An approach to planning with incomplete information. In: Third International Conference Principles of Knowledge Representation and Reasoning (KR 1992), p. 115 (1992)
Verma, D.C., et al.: Generative policy model for autonomic management. In: 2017 IEEE SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI, pp. 1–6 (2017)
Weld, D.S.: Recent advances in ai planning. AI Mag. 20(2), 93 (1999)
Google X: Project Wing. https://x.company/wing/
Acknowledgments
This research was sponsored by the U.S. Army Research Laboratory and the U.K. Ministry of Defence under Agreement Number W911NF-16-3-0001. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Army Research Laboratory, the U.S. Government, the U.K. Ministry of Defence or the U.K. Government. The U.S. and U.K. Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.
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Cullen, A., Karafili, E., Pilgrim, A., Williams, C., Lupu, E. (2018). Policy Support for Autonomous Swarms of Drones. In: Saracino, A., Mori, P. (eds) Emerging Technologies for Authorization and Authentication. ETAA 2018. Lecture Notes in Computer Science(), vol 11263. Springer, Cham. https://doi.org/10.1007/978-3-030-04372-8_6
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DOI: https://doi.org/10.1007/978-3-030-04372-8_6
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