Abstract
Disaster management has always been a struggle due to unpredictable changing conditions and chaotic occurrences that require real-time adaption. Highly optimized missions and robust systems mitigate uncertainty effects and improve notoriously success rates. This paper brings a niching hybrid human–machine system that combines UAVs fast responsiveness with two robust, decentralized, and scalable bio-inspired techniques. Cloud-Sharing Network (CSN) and Pseudo-Central Network (PCN), based on Bacterial and Honeybee behaviors, are presented, and applied to Safe and Rescue (SAR) operations. A post-earthquake scenario is proposed, where a heterogeneous fleet of UAVs cooperates with human rescue teams to detect and locate victims distributed along the map. Monte Carlo simulations are carried out to test both approaches through state-of-the-art metrics. This paper introduces two hybrid and bio-inspired schemes to deal with critical scouting stages, poor communications environments and high uncertainly levels in disaster release operations. Role heterogeneity, path optimization and hive data-sharing structure give PCN an efficient performance as far as task allocation and communications are concerned. Cloud-sharing network gains strength when the allocated agents per victim and square meter is high, allowing fast data transmission. Potential applications of these algorithms are not only comprehended in SAR field, but also in surveillance, geophysical mapping, security and planetary exploration.
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References
Chen, D., Liu, Z., Wang, L., Dou, M.: Natural disaster monitoring with wireless sensor networks: a case study of data-intensive applications upon low-cost scalable systems. Mobile Networks and Applications 18(5), 651–663 (2013). https://doi.org/10.1007/s11036-013-0456-9
Office, U. N.: INSARAG Guidelines and Methodology, p. 150 (2007).
Ochoa, S. F. and Santos, R. (2015) “Human-centric wireless sensor networks to improve information availability during urban search and rescue activities,” Information Fusion. Elsevier B.V., 22, pp. 71–84. https://doi.org/10.1016/j.inffus.2013.05.009.
American Red Cross (2015) “Drones for disaster response and relief operations,” (April), p. 51.
Tkach, I. and Edan, Y. (2019) Distributed heterogeneous multi sensor task allocation systems. Springer International Publishing (Automation, Collaboration, & E-Services).
Chapman, A., Micillo, R., Kota, R. and Jennings, N. (2009) “Decentralised dynamic task allocation: a practical game–theoretic approach,” in, pp. 915–922. https://doi.org/10.1145/1558109.1558139.
Al-Buraiki, O. and Payeur, P. (2019) “Probabilistic Task Assignment for Specialized Multi-Agent Robotic Systems,” in, pp. 1–7. https://doi.org/10.1109/ROSE.2019.8790420.
Robin, C. and Lacroix, S. (2015) A taxonomy of multi-robot target detection and tracking problems.
Jang, I. (2018) Effective task allocation frameworks for large-scale multiple agent systems.
Bhattacharya, S., Ghrist, R., Kumar, V.: Multi-robot coverage and exploration on Riemannian manifolds with boundaries. Int. J. Robot. Res. 33, 113–137 (2014). https://doi.org/10.1177/0278364913507324
Kolling, A., Carpin, S.: Pursuit-evasion on trees by robot teams. IEEE Trans. Rob. 26(1), 32–47 (2010). https://doi.org/10.1109/TRO.2009.2035737
Moors, M., Rohling, T. and Schulz, D. (2005) A probabilistic approach to coordinated multi-robot indoor surveillance,” in 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3447–3452
Yu, H., Beard, R. W., Argyle, M. and Chamberlain, C. (2011) Probabilistic path planning for cooperative target tracking using aerial and ground vehicles,” in Proceedings of the 2011 American Control Conference, pp. 4673–4678
Espinós Longa, M., Inalhan, G. and Tsourdos, A. (2022) Swarm Intelligence in Cooperative Environments: Introducing the N-Step Dynamic Tree Search Algorithm,” in AIAA SCITECH 2022 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, pp. 1–13. https://doi.org/10.2514/6.2022-1839.
Espinós Longa, M., Tsourdos, A. and Inalhan, G. (2022) Swarm intelligence in cooperative environments: N-Step dynamic tree search algorithm extended analysis. American Control Conference (accepted), pp. 1–13.
Espinós Longa, M., Inalhan, G. and Tsourdos, A. (2022) “Swarm intelligence in cooperative environments: N-Step dynamic tree search algorithm overview. J. Aerospace Inform. Systems (submitted).
Singh, P., Dhiman, G.: A hybrid fuzzy time series forecasting model based on granular computing and bio-inspired optimization approaches. Journal of Computational Science 27, 370–385 (2018). https://doi.org/10.1016/j.jocs.2018.05.008
Tripathy, M., Mishra, S.: Bacteria foraging-based solution to optimize both real power loss and voltage stability limit. IEEE Trans. Power Syst. 22(1), 240–248 (2007)
Bayindir, L. (2016) A review of swarm robotics tasks. Neurocomputing, 172(August 2015), pp. 292–321. https://doi.org/10.1016/j.neucom.2015.05.116.
Ducatelle, F., Förster, A., Di Caro, G. and Gambardella, L. M. (2009) New task allocation methods for robotic swarms, in.
Pini, G., Brutschy, A., Birattari, M. and Dorigo, M. (2009) “Interference reduction through task partitioning in a robotic swarm,” ICINCO 2009 - 6th International Conference on Informatics in Control, Automation and Robotics, Proceedings, 2 RA, pp. 52–59. https://doi.org/10.5220/0002195200520059.
Huang, L., Ding, Y., Zhou, M., Jin, Y., Hao, K.: Multiple-Solution Optimization Strategy for Multirobot Task Allocation. IEEE Transactions on Systems, Man, and Cybernetics: Systems 50(11), 4283–4294 (2020). https://doi.org/10.1109/TSMC.2018.2847608
Abielmona, R., Falcon, R., Zincir-Heywood, N. and Abbass, H. (2016) Recent advances in computational intelligence in defense and security. https://doi.org/10.1007/978-3-319-26450-9_1.
Pandian, A. (2013) Training neural networks with ant colony optimization.
Mavrovouniotis, M. and Yang, S. (2013) Evolving neural networks using ant colony optimization with pheromone trail limits, in 2013 13th UK Workshop on Computational Intelligence (UKCI), pp. 16–23.
Blum, C. and Socha, K. (2005) “Training feed-forward neural networks with ant colony optimization: An application to pattern classification, in Fifth International Conference on Hybrid Intelligent Systems, HIS 2005, pp. 6 pp.-. https://doi.org/10.1109/ICHIS.2005.104.
Li, Q., Jiang, Z.-P.: Two decentralized heading consensus algorithms for nonlinear multi-agent systems. Asian Journal of Control 10(2), 187–200 (2008). https://doi.org/10.1002/asjc.018
Wang, J., Sun, Y., Zhang, Z., Gao, S.: Solving multitrip pickup and delivery problem with time windows and manpower planning using multiobjective algorithms. IEEE/CAA Journal of Automatica Sinica 7(4), 1134–1153 (2020). https://doi.org/10.1109/JAS.2020.1003204
Hu, B., Cao, Z., Zhou, M.: An efficient RRT-based framework for planning short and smooth wheeled robot motion under kinodynamic constraints. IEEE Trans. Industr. Electron. 68(4), 3292–3302 (2021). https://doi.org/10.1109/TIE.2020.2978701
Jian, Y.-L., Lian, F.-L. and Lee, H.-T. (2008) Deployment of a team of biomimetic searching agents based on limited communication quantity. Asian J. Control, 10(4). https://doi.org/10.1002/asjc.043.
Li, P. and Duan, H. (2014) Bio-inspired computation in unmanned aerial vehicles, in Bio-Inspired Computation in Unmanned Aerial Vehicles, pp. 35–69. https://doi.org/10.1007/978-3-642-41196-0_2.
Girma, A., Bahadori, N., Sarkar, M., Tadewos, T.G., Behnia, M.R., Mahmoud, M.N., Karimoddini, A., Homaifar, A.: IoT-enabled autonomous system collaboration for disaster-area management. IEEE/CAA Journal of Automatica Sinica 7(5), 1249–1262 (2020). https://doi.org/10.1109/JAS.2020.1003291
Corne, D., Reynolds, A. and Bonabeau, E. (2012) “Swarm Intelligence,” in Handbook of Natural Computing, pp. 1599–1623.
Nedic, N., Prsic, D., Dubonjic, L., Stojanovic, V., Djordjevic, V.: “Optimal cascade hydraulic control for a parallel robot platform by PSO”, The International Journal of Advanced Manufacturing Technology 2014 72:5. Springer 72(5), 1085–1098 (2014). https://doi.org/10.1007/S00170-014-5735-5
Potter, M. A. and de Jong, K. A.: A cooperative coevolutionary approach to function optimization, in Davidor, Y., Schwefel, H.-P., and Männer, R. (eds) Parallel Problem Solving from Nature. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 249–257. (1994)https://doi.org/10.1007/3-540-58484-6_269.
Panait, L., Wiegand, R. P. and Luke, S. (no date aj) Improving Coevolutionary Search for Optimal Multiagent Behaviors.
Ficici, S. G. and Pollack, J. B. (2000) “A game-theoretic approach to the simple coevolutionary algorithm,” in Schoenauer, M., Deb, K., Rudolph, G., Yao, X., Lutton, E., Merelo, J. J., and Schwefel, H.-P. (eds) Parallel Problem Solving from Nature. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 467–476. https://doi.org/10.1007/3-540-45356-3_46.
Haynes, T., Wainwright, R., Sen, S. and Schoenefeld, D. (1995) Strongly typed genetic programming in evolving cooperation strategies.,” in Proceedings of the 6th International Conference on Genetic Algorithm, pp. 271–278.
Passino, K. M. (2002) Biomimicry of bacterial foraging, IEEE Control Systems Magazine, (June), pp. 52–67.
Quijano, N. and Passino, K. M. (2007) Honey bee social foraging algorithms for resource allocation: theory and application, pp. 1–39.
Cabreira, T., Brisolara, L., Ferreira, P.R., Jr.: Survey on coverage path planning with unmanned aerial vehicles. Drones (2019). https://doi.org/10.3390/drones3010004
Liu, Z.-N., Liu, X.-Q., Yang, L.-J., Leo, D. and Zhao, H.-W. (2018) An autonomous dock and battery swapping system for multirotor UAV, (May). https://doi.org/10.13140/RG.2.2.19437.90085.
Rohan, A., Rabah, M., Asghar, F., Talha, M., Kim, S.-H.: Advanced drone battery charging system. Journal of Electrical Engineering & Technology 14(3), 1395–1405 (2019). https://doi.org/10.1007/s42835-019-00119-8
Valente, J., Sanz, D., del Cerro, J., Barrientos, A., de Frutos, M.Á.: Near-optimal coverage trajectories for image mosaicing using a mini quad-rotor over irregular-shaped fields. Precision Agric. 14(1), 115–132 (2013)
Artemenko, O., Dominic, O. J., Andryeyev, O. and Mitschele-Thiel, A. (2016) Energy-aware trajectory planning for the localization of mobile devices using an unmanned aerial vehicle, 2016 25th International Conference on Computer Communication and Networks (ICCCN), pp. 1–9.
Xu, A., Viriyasuthee, C. and Rekleitis, I. (2011) Optimal complete terrain coverage using an unmanned aerial vehicle. Proceedings - IEEE International Conference on Robotics and Automation, pp. 2513–2519. https://doi.org/10.1109/ICRA.2011.5979707.
Öst, G. (2012) Search path generation with UAV applications using approximate convex decomposition. Linköping University, The Institute of Technology.
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This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) and BAE Systems under the project reference number 2454254.
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All authors contributed to the study conception and design. Literature review, mathematical development and analysis were performed by Marc Espinós Longa. The first draft of the manuscript was written by Marc Espinós Longa and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Longa, M.E., Tsourdos, A. & Inalhan, G. Human–Machine Network Through Bio-Inspired Decentralized Swarm Intelligence and Heterogeneous Teaming in SAR Operations. J Intell Robot Syst 105, 88 (2022). https://doi.org/10.1007/s10846-022-01690-5
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DOI: https://doi.org/10.1007/s10846-022-01690-5