Cloud DEVS-based computation of UAVs trajectories for search and rescue missions

References

• Al-Zoubi, K., & Wainer, G. (2009). Performing distributed simulation with RESTful webservices. In Proceedings of the 2009 winter simulation conference. (WSC), 1323–1334.
   Google Scholar
• Andres-Toro, B., Giron-Sierra, J. M., Fernandez-Blanco, P., Lopez-Orozco, J. A., & Besada- Portas, E. (2004). Multiobjective optimization and multivariable control of the beer fermentation process with the use of evolutionary algorithms. Journal of Zhejiang University- ScienceA, 5(4), 378–389. https://doi.org/10.1631/jzus.2004.0378
   Google Scholar
• Bordón-Ruiz, J. B., Besada-Portas, E., Risco-Martín, J. L., & López-Orozco, J. A. (2021). DEVS-based evaluation of UAVs-based target-search strategies in realistically-modeled missions. ACM SIGSIM Conference on Principles of Advanced. discrete simulation (PADS).
   Google Scholar
• Bourgault, F., Furukawa, T., & Durrant-Whyte, H. F. (2004). Decentralized Bayesian negotiation for cooperative search. In IEEE/RSJ international conference on intelligent robots and systems (IROS) (Vol. 3, pp. 2681–2686). IEE.
   Google Scholar
• Cárdenas, R., Arroba, P., Blanco, R., Malagón, P., Risco-Martín, J. L., & Moya, J. M. (2020). Mercury: A modeling, simulation, and optimization framework for data stream-oriented IoT applications. Simulation Modelling Practice and Theory, 101, 102037. https://doi.org/10.1016/j.simpat.2019.102037
   Web of Science ®Google Scholar
• Carpin, S., Burch, D., Basilico, N., Chung, T. H., & Kölsch, M. (2013). Variable resolution search with quadrotors: Theory and practice. Journal of Field Robotics, 30(5), 685–701. https://doi.org/10.1002/rob.21468
   Web of Science ®Google Scholar
• Deb, K., Pratap, A., Agrawal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary. Computing, 6, 182–197. doi:10.1109/4235.996017. https://ieeexplore.ieee.org/document/996017
   Google Scholar
• Delle Fave, F., Xu, Z., Rogers, A., & Jennings, N. R. (2010). Decentralised coordination of unmanned aerial vehicles for target search using the max-sum algorithm. In Proceedings of the workshop on agents in real time and. environment, 35–44.
   Google Scholar
• Fedorov, A. (2019). Path planning for uav search using growing area algorithm and clustering. In Fourth Conference on Software Engineering and Information Management.
   Google Scholar
• Grocholsky, B., Keller, J., Kumar, V., & Pappas, G. (2006). Cooperative air and ground surveillance. IEEE Robotics & Automation Magazine / IEEE Robotics & Automation Society, 13(3), 16–25. https://doi.org/10.1109/MRA.2006.1678135
   Web of Science ®Google Scholar
• Hall, S. (1997). A DEVS based simulation architecture for analysis of multi-vehicle interactions. In Proceedings of SPIE - the International Society for Optical Engineering, 287–294.
   Google Scholar
• Happe, J., & Berger, J. (2010). CoUAV: A multi-UAV cooperative search path planning simulation environment. In Summer computer simulation. conference, 86–93.
   Google Scholar
• Holman, K., Kuzub, J., & Wainer, G. (2010). UAV search strategies using Cell-DEVS. In Spring simulation multiconference.
   Google Scholar
• Hu, J., Xie, L., Xu, J., & Xu, Z. (2014). Multi-agent cooperative target search. Sensors, 14(6), 9408–9428. https://doi.org/10.3390/s140609408
   Web of Science ®Google Scholar
• Ivić, S., Crnković, B., Arbabi, H., Loire, S., Clary, P., & Mezić, I. (2020). Search strategy in a complex and dynamic environment: The MH370 case. SciRep, 10.
   Google Scholar
• Izzo, D., Rucinski, M., & Biscani, F. (2012). The generalized Island model.
   Google Scholar
• Juan, B., Bordón-Ruiz, José, A. López-Orozco & Eva Besada Portas. (2022). DEVS/SAR: DEVS Search And Rescue M\&S\&O framework. https://github.com/iscar-ucm/devs-sar/releases/tag/2022-jos
   Google Scholar
• Kiam, J. J., Besada-Portas, E., & Schulte, A. (2021). Hierarchical mission planning with a GA-optimizer for unmanned high altitude pseudo-satellites. Electronics, 21(5), 36.
   Google Scholar
• Kratzke, T., Stone, L., & Frost, J. R. (2010). Search and rescue optimal planning system. In 13th international conference on information fusion.
   Google Scholar
• Lanillos, P., Gan, S., Besada-Portas, E., Pajares, G., & Sukkarieh, S. (2014). Multi-UAV target search using decentralized gradient-based negotiation with expected observation. Information Science, 282, 92–110. https://doi.org/10.1016/j.ins.2014.05.054
   Web of Science ®Google Scholar
• Lanillos, P., Yañez-Zuluaga, J., Ruz, J., & Besada-Portas, E. (2013). A Bayesian approach for constrained multi-agent minimum time search in uncertain dynamic domains. In The genetic and evolutionary computation. conference, 391–398.
   Google Scholar
• Li, L., Zhang, X., Yue, W., & Liu, Z. (2021). Cooperative search for dynamic targets by multiple UAVs with communication data losses. ISA. Transactions
   Web of Science ®Google Scholar
• Linchant, J., Lisein, J., Semki, J., Lejeune, P., & Vermeulen, C. (2015). Are unmanned aircraft systems (UAS) the future of wildlife monitoring? a review of accomplishments and challenges. Mammal Review, 45(4), 4. https://doi.org/10.1111/mam.12046
   Web of Science ®Google Scholar
• Mittal, S., Risco-Martín, J. L., Masuda, T., & Mittal, S. K. (2017). DEVSML 3.0 stack: Rapid deployment of DEVS farm in distributed cloud environment using microservices and containers. In Spring simulation multiconference (SpringSim 2017). Diseases of the Esophagus: Official Journal of the International Society for Diseases of the Esophagus, 30(6), 1–19. https://doi.org/10.1093/dote/dox048
   Google Scholar
• Mittal, S., Risco-Martín, J. L., & Zeigler, B. P. (2009). DEVS/SOA: A cross-platform framework for net-centric modeling and simulation in devs unified process. SIMULATION, 85(7), 419–450. https://doi.org/10.1177/0037549709340968
   Google Scholar
• Moreno, A., de la Torre, L., Risco-Martin, J. L., Besada-Portas, E., & Aranda, J. (2011). DEVS-based validation of UAV path planning in hostile environments. In The International Defense and Homeland Security Simulation Workshop (p. 135–140).
   Google Scholar
• Moreno, A., de la Torre, L., Risco-Martin, J. L., Besada-Portas, E., Aranda, J., & Ayala, J. L. (2011). DEVS-based parallel framework for multi-objective evolutionary algortithms. In The Fourth International Workshop on Parallel Architectures and Bioinspired Algorithms.
   Google Scholar
• Nigam, N. (2014). The multiple unmanned air vehicle persistent surveillance problem: A review. Machines, 2(1), 13–72. https://doi.org/10.3390/machines2010013
   Google Scholar
• Ntaimo, L., Hu, X., & Sun, Y. (2008). DEVS-FIRE: Towards an integrated simulation environment for surface wildfire spread and containment. Simulation, 84(4), 137–155. https://doi.org/10.1177/0037549708094047
   Google Scholar
• opensource.com. (2021). What is docker? https://opensource.com/resources/what-docker. (Accessed April. 30, 2021)
   Google Scholar
• Pecker-Marcosig, E., Zudaire, S., Garrett, M., Uchitel, S., & Castro, R. (2020). Unified DEVS- based platform for modelling and simulation of hybrid control systems. Winter Simulation Conference, 1051–1162.
   Google Scholar
• Pérez, E. (2017). Integrating mathematical optimization in DEVS for nuclear medicine patient and resource scheduling. Winter Simulation Conference, 398–407.
   Google Scholar
• Perez-Carabaza, S., Bermudez-Ortega, J., Besada-Portas, E., Lopez-Orozco, J. A., & de la Cruz, J. M. (2017). A multi-UAV minimum time search planner based on ACOR. In Proceedings of the genetic and evolutionary computation. conference, 35–42.
   Google Scholar
• Perez-Carabaza, S., Besada-Portas, E., Lopez-Orozco, J. A., & de la Cruz, J. M. (2016). A real world multi-UAV evolutionary planner for minimum time target detection. In Proceedings of the genetic and evolutionary computation conference 2016. 981–988.
   Google Scholar
• Pérez-Carabaza, S., Besada-Portas, E., López-Orozco, J., & Pajares, G. (2019). Minimum time search in real-world scenarios using multiple UAVs with onboard orientable cameras. Journal of Sensors, 2019, 22. https://doi.org/10.1155/2019/7673859
   Web of Science ®Google Scholar
• Pérez-Carabaza, S., Scherer, J., Rinner, B., López-Orozco, J., & Besada-Portas, E. (2019). UAV trajectory optimization for minimum time search with communication constraints and collision avoidance. Engineering Applications of Artificial Intelligence, 85, 357–371. https://doi.org/10.1016/j.engappai.2019.06.002
   Web of Science ®Google Scholar
• Riehl, J. R., Collins, G. E., Hespanha, J. P., Xie, Q., Li, P., Chen, J., & Yao, S. (2011). Cooperative search by UAV teams: A model predictive approach using dynamic graphs. IEEE Transactions on Aerospace and Electronic Systems. Chemical Communications (Cambridge, England), 47(4), 2637–2656. https://doi.org/10.1039/c0cc05188h
   Google Scholar
• Risco-Martín, J. L., Mittal, S., Fabero, J. C., Zapater, M., & Hermida, R. (2017). Reconsidering the performance of DEVS modeling and simulation environments using the DEVStone benchmark. Simulation, 93(6), 459–476. https://doi.org/10.1177/0037549717690447
   Google Scholar
• Risco, J. L., Mittal, S., Atienza, D., Hidalgo, J. I., & Lanchares, J. (2008). Optimization of dynamic data types in embedded systems using DEVS/SOA-based modeling and simulation. In Proceedings of the 3rd international icst conference on scalable information. Systems 2009, 1–11.
   Google Scholar
• Saadaoui, H., El Bouanani, F., Dobson, F. S., Saadaoui, H., Viblanc, V. A., & Bize, P. (2018). Information sharing based on local PSO for UAVs cooperative search of unmoved targets. In International conference on advanced communication technologies and networking. Ecology and Evolution, 8(2), 1084–1095. https://doi.org/10.1002/ece3.3677
   Google Scholar
• Shakhatreh, H., SawalMeh, A. H., Al-Fuqaha, A., Dou, Z., Almaita, E., Khalil, I., … Guizan, M. (2019). Unmanned aerial vehicles (UAVs): A survey on civil applications and key research challenges. IEEE Access.
   Google Scholar
• Skorobogatov, G., Barrado, C., & Salami, E. (2020). Multiple UAV systems: A survey. Unmanned Systems, 8(2), 149–169. https://doi.org/10.1142/S2301385020500090
   Web of Science ®Google Scholar
• Tisdale, J., Kim, Z., & Hedrick, J. K. (2009). Autonomous UAV path planning and estimation. IEEE Robotics Automation Magazine, 16(2), 35–42. https://doi.org/10.1109/MRA.2009.932529
   Web of Science ®Google Scholar
• Trummel, K. E., & Weisinger, J. R. (1986). The complexity of the optimal searcher path problem. Operational Research, 34(2), 324–327. https://doi.org/10.1287/opre.34.2.324
   Google Scholar
• Van Tendeloo, Y., & Vangheluwe, H. (2015). PythonPDEVS: A distributed parallel DEVS simulator. Springsim (TMS-DEVS), 844–851.
   Google Scholar
• Wang, Y., Zhang, M. X., & Zheng, Y. J. (2017). A hyper-heuristic method for UAV search planning. In International conference on swarm intelligence.
   Google Scholar
• Wong, E., Bourgault, F., & Furukawa, T. (2005). Multi-vehicle Bayesian search for multiple lost targets. In IEEE international conference on robotics and automation (pp. 3169–3174).
   Google Scholar
• Wymore, A. W. (2018). Model-based systems engineering (Vol. 3). CRC press.
   Google Scholar
• Yang, Y., Minai, A., & Polycarpou, M. (2002). Decentralized cooperative search in UAV’s using opportunistic learning. In AIAA guidance, navigation, and control conference and exhibit.
   Google Scholar
• Yao, P., Want, H., & Ji, H. (2017). Gaussian mixture model and receding horizon control for multiple UAV search in complex environment. Nonlinear Dynamics, 88(2), 903–919. https://doi.org/10.1007/s11071-016-3284-1
   Web of Science ®Google Scholar
• Yao, P., Xie, Z., & Ren, P. (2019). Optimal UAV route planning for coverage search of stationary target in river. IEEE Transactions on Control Systems Technology, 27(2), 822–829. https://doi.org/10.1109/TCST.2017.2781655
   Web of Science ®Google Scholar
• Yeong, S. P., King, L. M., & Dol, S. S. (2015). A review on marine search and rescue operations using unmanned aerial vehicles. International Journal of Marine and Environmental Sciences, 9(2).
   Google Scholar
• Yuan, C., Zhang, Y., Liu, Z., Zhang, Y., & Liu, Z. (2015). A survey on technologies for automatic forest fire monitoring, detection and fighting using UAVs and remote sensing techniques. Canadian Journal of Forest Research, 45(7), 783–792. https://doi.org/10.1139/cjfr-2014-0347
   Google Scholar
• Zeigler, B. P., & Kim, D. (2019). Multi-resolution modeling for adaptive UAV service systems. In Proceedings of spring simulation. conference, 1–12.
   Google Scholar
• Zeigler, B. P., Moon, Y., Lopes, V. L., & Kim, J. (1996). DEVS approximation of infiltration using genetic algorithm optimization of a fuzzy system. Mathematical and Computer Modelling, 23(11–12), 215–228. https://doi.org/10.1016/0895-7177(96)00074-X
   Google Scholar
• Zeigler, B. P., Muzy, A., & Kofman, E. (2018). Theory of modeling and simulation: Discrete event & iterative system computational foundations. Academic press.
   Google Scholar