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Recursive Rewarding Modified Adaptive Cell Decomposition (RR-MACD): A Dynamic Path Planning Algorithm for UAVs

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Recursive Rewarding Modified Adaptive Cell Decomposition (RR-MACD): A Dynamic Path Planning Algorithm for UAVs

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dc.contributor.author Samaniego-Riera, Franklin Eduardo es_ES
dc.contributor.author Sanchís Saez, Javier es_ES
dc.contributor.author Garcia-Nieto, Sergio es_ES
dc.contributor.author Simarro Fernández, Raúl es_ES
dc.date.accessioned 2020-05-29T03:33:04Z
dc.date.available 2020-05-29T03:33:04Z
dc.date.issued 2019-03-08 es_ES
dc.identifier.uri http://hdl.handle.net/10251/144574
dc.description.abstract [EN] A relevant task in unmanned aerial vehicles (UAV) flight is path planning in 3D environments. This task must be completed using the least possible computing time. The aim of this article is to combine methodologies to optimise the task in time and offer a complete 3D trajectory. The flight environment will be considered as a 3D adaptive discrete mesh, where grids are created with minimal refinement in the search for collision-free spaces. The proposed path planning algorithm for UAV saves computational time and memory resources compared with classical techniques. With the construction of the discrete meshing, a cost response methodology is applied as a discrete deterministic finite automaton (DDFA). A set of optimal partial responses, calculated recursively, indicates the collision-free spaces in the final path for the UAV flight. es_ES
dc.description.sponsorship The authors would like to acknowledge the Spanish Ministry of Economy and Competitiveness for providing funding through the project DPI2015-71443-R and the local administration Generalitat Valenciana through the project GV/2017/029. Franklin Samaniego thanks IFTH (Instituto de Fomento al Talento Humano) Ecuador (2015-AR2Q9209), for its sponsorship of this work. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Electronics es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject UAV es_ES
dc.subject Path planning es_ES
dc.subject Adaptive discrete mesh es_ES
dc.subject Octree es_ES
dc.subject.classification INGENIERIA DE SISTEMAS Y AUTOMATICA es_ES
dc.title Recursive Rewarding Modified Adaptive Cell Decomposition (RR-MACD): A Dynamic Path Planning Algorithm for UAVs es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/electronics8030306 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/IFTH//AR2Q9209/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//DPI2015-71443-R/ES/DESARROLLO DE HERRAMIENTAS AVANZADAS PARA METODOLOGIAS DE DISEÑO Y OPTIMIZACION MULTIOBJETIVO EN INGENIERIA DE CONTROL. APLICACION A SISTEMAS MULTIVARIABLES./ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GVA//GV%2F2017%2F029/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería de Sistemas y Automática - Departament d'Enginyeria de Sistemes i Automàtica es_ES
dc.description.bibliographicCitation Samaniego-Riera, FE.; Sanchís Saez, J.; Garcia-Nieto, S.; Simarro Fernández, R. (2019). Recursive Rewarding Modified Adaptive Cell Decomposition (RR-MACD): A Dynamic Path Planning Algorithm for UAVs. Electronics. 8(3):1-21. https://doi.org/10.3390/electronics8030306 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/electronics8030306 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 21 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 8 es_ES
dc.description.issue 3 es_ES
dc.identifier.eissn 2079-9292 es_ES
dc.relation.pasarela S\380325 es_ES
dc.contributor.funder Generalitat Valenciana es_ES
dc.contributor.funder Instituto de Fomento al Talento Humano, Ecuador es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
dc.description.references Valavanis, K. P., & Vachtsevanos, G. J. (Eds.). (2015). Handbook of Unmanned Aerial Vehicles. doi:10.1007/978-90-481-9707-1 es_ES
dc.description.references 20 Great UAV Applications Areas for Droneshttp://air-vid.com/wp/20-great-uav-applications-areas-drones/ es_ES
dc.description.references Industry Experts—Microdroneshttps://www.microdrones.com/en/industry-experts/ es_ES
dc.description.references Li, J., & Han, Y. (2017). Optimal Resource Allocation for Packet Delay Minimization in Multi-Layer UAV Networks. IEEE Communications Letters, 21(3), 580-583. doi:10.1109/lcomm.2016.2626293 es_ES
dc.description.references Stuchlík, R., Stachoň, Z., Láska, K., & Kubíček, P. (2015). Unmanned Aerial Vehicle – Efficient mapping tool available for recent research in polar regions. Czech Polar Reports, 5(2), 210-221. doi:10.5817/cpr2015-2-18 es_ES
dc.description.references Pulver, A., & Wei, R. (2018). Optimizing the spatial location of medical drones. Applied Geography, 90, 9-16. doi:10.1016/j.apgeog.2017.11.009 es_ES
dc.description.references Claesson, A., Svensson, L., Nordberg, P., Ringh, M., Rosenqvist, M., Djarv, T., … Hollenberg, J. (2017). Drones may be used to save lives in out of hospital cardiac arrest due to drowning. Resuscitation, 114, 152-156. doi:10.1016/j.resuscitation.2017.01.003 es_ES
dc.description.references Reineman, B. D., Lenain, L., Statom, N. M., & Melville, W. K. (2013). Development and Testing of Instrumentation for UAV-Based Flux Measurements within Terrestrial and Marine Atmospheric Boundary Layers. Journal of Atmospheric and Oceanic Technology, 30(7), 1295-1319. doi:10.1175/jtech-d-12-00176.1 es_ES
dc.description.references LaValle, S. M. (2006). Planning Algorithms. doi:10.1017/cbo9780511546877 es_ES
dc.description.references Elbanhawi, M., & Simic, M. (2014). Sampling-Based Robot Motion Planning: A Review. IEEE Access, 2, 56-77. doi:10.1109/access.2014.2302442 es_ES
dc.description.references Hernandez, K., Bacca, B., & Posso, B. (2017). Multi-goal Path Planning Autonomous System for Picking up and Delivery Tasks in Mobile Robotics. IEEE Latin America Transactions, 15(2), 232-238. doi:10.1109/tla.2017.7854617 es_ES
dc.description.references Kohlbrecher, S., von Stryk, O., Meyer, J., & Klingauf, U. (2011). A flexible and scalable SLAM system with full 3D motion estimation. 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics. doi:10.1109/ssrr.2011.6106777 es_ES
dc.description.references Aguilar, W., & Morales, S. (2016). 3D Environment Mapping Using the Kinect V2 and Path Planning Based on RRT Algorithms. Electronics, 5(4), 70. doi:10.3390/electronics5040070 es_ES
dc.description.references Aguilar, W. G., Morales, S., Ruiz, H., & Abad, V. (2017). RRT* GL Based Optimal Path Planning for Real-Time Navigation of UAVs. Lecture Notes in Computer Science, 585-595. doi:10.1007/978-3-319-59147-6_50 es_ES
dc.description.references Yao, P., Wang, H., & Su, Z. (2015). Hybrid UAV path planning based on interfered fluid dynamical system and improved RRT. IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society. doi:10.1109/iecon.2015.7392202 es_ES
dc.description.references Yan, F., Liu, Y.-S., & Xiao, J.-Z. (2013). Path Planning in Complex 3D Environments Using a Probabilistic Roadmap Method. International Journal of Automation and Computing, 10(6), 525-533. doi:10.1007/s11633-013-0750-9 es_ES
dc.description.references Yeh, H.-Y., Thomas, S., Eppstein, D., & Amato, N. M. (2012). UOBPRM: A uniformly distributed obstacle-based PRM. 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. doi:10.1109/iros.2012.6385875 es_ES
dc.description.references Denny, J., & Amatoo, N. M. (2013). Toggle PRM: A Coordinated Mapping of C-Free and C-Obstacle in Arbitrary Dimension. Algorithmic Foundations of Robotics X, 297-312. doi:10.1007/978-3-642-36279-8_18 es_ES
dc.description.references Li, Q., Wei, C., Wu, J., & Zhu, X. (2014). Improved PRM method of low altitude penetration trajectory planning for UAVs. Proceedings of 2014 IEEE Chinese Guidance, Navigation and Control Conference. doi:10.1109/cgncc.2014.7007587 es_ES
dc.description.references Ortiz-Arroyo, D. (2015). A hybrid 3D path planning method for UAVs. 2015 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS). doi:10.1109/red-uas.2015.7440999 es_ES
dc.description.references Thanou, M., & Tzes, A. (2014). Distributed visibility-based coverage using a swarm of UAVs in known 3D-terrains. 2014 6th International Symposium on Communications, Control and Signal Processing (ISCCSP). doi:10.1109/isccsp.2014.6877904 es_ES
dc.description.references Qu, Y., Zhang, Y., & Zhang, Y. (2014). Optimal flight path planning for UAVs in 3-D threat environment. 2014 International Conference on Unmanned Aircraft Systems (ICUAS). doi:10.1109/icuas.2014.6842250 es_ES
dc.description.references Fang, Z., Luan, C., & Sun, Z. (2017). A 2D Voronoi-Based Random Tree for Path Planning in Complicated 3D Environments. Advances in Intelligent Systems and Computing, 433-445. doi:10.1007/978-3-319-48036-7_31 es_ES
dc.description.references Khuswendi, T., Hindersah, H., & Adiprawita, W. (2011). UAV path planning using potential field and modified receding horizon A* 3D algorithm. Proceedings of the 2011 International Conference on Electrical Engineering and Informatics. doi:10.1109/iceei.2011.6021579 es_ES
dc.description.references Chen, X., & Zhang, J. (2013). The Three-Dimension Path Planning of UAV Based on Improved Artificial Potential Field in Dynamic Environment. 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics. doi:10.1109/ihmsc.2013.181 es_ES
dc.description.references Rivera, D. M., Prieto, F. A., & Ramirez, R. (2012). Trajectory Planning for UAVs in 3D Environments Using a Moving Band in Potential Sigmoid Fields. 2012 Brazilian Robotics Symposium and Latin American Robotics Symposium. doi:10.1109/sbr-lars.2012.26 es_ES
dc.description.references Liu Lifen, Shi Ruoxin, Li Shuandao, & Wu Jiang. (2016). Path planning for UAVS based on improved artificial potential field method through changing the repulsive potential function. 2016 IEEE Chinese Guidance, Navigation and Control Conference (CGNCC). doi:10.1109/cgncc.2016.7829099 es_ES
dc.description.references Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1(1), 269-271. doi:10.1007/bf01386390 es_ES
dc.description.references Verscheure, L., Peyrodie, L., Makni, N., Betrouni, N., Maouche, S., & Vermandel, M. (2010). Dijkstra’s algorithm applied to 3D skeletonization of the brain vascular tree: Evaluation and application to symbolic. 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology. doi:10.1109/iembs.2010.5626112 es_ES
dc.description.references Hart, P., Nilsson, N., & Raphael, B. (1968). A Formal Basis for the Heuristic Determination of Minimum Cost Paths. IEEE Transactions on Systems Science and Cybernetics, 4(2), 100-107. doi:10.1109/tssc.1968.300136 es_ES
dc.description.references Ferguson, D., & Stentz, A. (s. f.). Field D*: An Interpolation-Based Path Planner and Replanner. Robotics Research, 239-253. doi:10.1007/978-3-540-48113-3_22 es_ES
dc.description.references De Filippis, L., Guglieri, G., & Quagliotti, F. (2011). Path Planning Strategies for UAVS in 3D Environments. Journal of Intelligent & Robotic Systems, 65(1-4), 247-264. doi:10.1007/s10846-011-9568-2 es_ES
dc.description.references Gautam, S. A., & Verma, N. (2014). Path planning for unmanned aerial vehicle based on genetic algorithm & artificial neural network in 3D. 2014 International Conference on Data Mining and Intelligent Computing (ICDMIC). doi:10.1109/icdmic.2014.6954257 es_ES
dc.description.references Maturana, D., & Scherer, S. (2015). 3D Convolutional Neural Networks for landing zone detection from LiDAR. 2015 IEEE International Conference on Robotics and Automation (ICRA). doi:10.1109/icra.2015.7139679 es_ES
dc.description.references Iswanto, I., Wahyunggoro, O., & Imam Cahyadi, A. (2016). Quadrotor Path Planning Based on Modified Fuzzy Cell Decomposition Algorithm. TELKOMNIKA (Telecommunication Computing Electronics and Control), 14(2), 655. doi:10.12928/telkomnika.v14i2.2989 es_ES
dc.description.references Duan, H., Yu, Y., Zhang, X., & Shao, S. (2010). Three-dimension path planning for UCAV using hybrid meta-heuristic ACO-DE algorithm. Simulation Modelling Practice and Theory, 18(8), 1104-1115. doi:10.1016/j.simpat.2009.10.006 es_ES
dc.description.references He, Y., Zeng, Q., Liu, J., Xu, G., & Deng, X. (2013). Path planning for indoor UAV based on Ant Colony Optimization. 2013 25th Chinese Control and Decision Conference (CCDC). doi:10.1109/ccdc.2013.6561444 es_ES
dc.description.references Zhang, Y., Wu, L., & Wang, S. (2013). UCAV Path Planning by Fitness-Scaling Adaptive Chaotic Particle Swarm Optimization. Mathematical Problems in Engineering, 2013, 1-9. doi:10.1155/2013/705238 es_ES
dc.description.references Goel, U., Varshney, S., Jain, A., Maheshwari, S., & Shukla, A. (2018). Three Dimensional Path Planning for UAVs in Dynamic Environment using Glow-worm Swarm Optimization. Procedia Computer Science, 133, 230-239. doi:10.1016/j.procs.2018.07.028 es_ES
dc.description.references YongBo, C., YueSong, M., JianQiao, Y., XiaoLong, S., & Nuo, X. (2017). Three-dimensional unmanned aerial vehicle path planning using modified wolf pack search algorithm. Neurocomputing, 266, 445-457. doi:10.1016/j.neucom.2017.05.059 es_ES
dc.description.references Wang, G.-G., Chu, H. E., & Mirjalili, S. (2016). Three-dimensional path planning for UCAV using an improved bat algorithm. Aerospace Science and Technology, 49, 231-238. doi:10.1016/j.ast.2015.11.040 es_ES
dc.description.references Aghababa, M. P. (2012). 3D path planning for underwater vehicles using five evolutionary optimization algorithms avoiding static and energetic obstacles. Applied Ocean Research, 38, 48-62. doi:10.1016/j.apor.2012.06.002 es_ES
dc.description.references Mac, T. T., Copot, C., Tran, D. T., & De Keyser, R. (2016). Heuristic approaches in robot path planning: A survey. Robotics and Autonomous Systems, 86, 13-28. doi:10.1016/j.robot.2016.08.001 es_ES
dc.description.references Szirmay-Kalos, L., & Márton, G. (1998). Worst-case versus average case complexity of ray-shooting. Computing, 61(2), 103-131. doi:10.1007/bf02684409 es_ES
dc.description.references Berger, M. J., & Oliger, J. (1984). Adaptive mesh refinement for hyperbolic partial differential equations. Journal of Computational Physics, 53(3), 484-512. doi:10.1016/0021-9991(84)90073-1 es_ES
dc.description.references Min, C., & Gibou, F. (2006). A second order accurate projection method for the incompressible Navier–Stokes equations on non-graded adaptive grids. Journal of Computational Physics, 219(2), 912-929. doi:10.1016/j.jcp.2006.07.019 es_ES
dc.description.references Hasbestan, J. J., & Senocak, I. (2018). Binarized-octree generation for Cartesian adaptive mesh refinement around immersed geometries. Journal of Computational Physics, 368, 179-195. doi:10.1016/j.jcp.2018.04.039 es_ES
dc.description.references Pantano, C., Deiterding, R., Hill, D. J., & Pullin, D. I. (2007). A low numerical dissipation patch-based adaptive mesh refinement method for large-eddy simulation of compressible flows. Journal of Computational Physics, 221(1), 63-87. doi:10.1016/j.jcp.2006.06.011 es_ES
dc.description.references Ryde, J., & Hu, H. (2009). 3D mapping with multi-resolution occupied voxel lists. Autonomous Robots, 28(2), 169-185. doi:10.1007/s10514-009-9158-3 es_ES
dc.description.references Samet, H., & Kochut, A. (s. f.). Octree approximation an compression methods. Proceedings. First International Symposium on 3D Data Processing Visualization and Transmission. doi:10.1109/tdpvt.2002.1024101 es_ES
dc.description.references Samaniego, F., Sanchis, J., Garcia-Nieto, S., & Simarro, R. (2017). UAV motion planning and obstacle avoidance based on adaptive 3D cell decomposition: Continuous space vs discrete space. 2017 IEEE Second Ecuador Technical Chapters Meeting (ETCM). doi:10.1109/etcm.2017.8247533 es_ES
dc.description.references Skoldstam, M., Akesson, K., & Fabian, M. (2007). Modeling of discrete event systems using finite automata with variables. 2007 46th IEEE Conference on Decision and Control. doi:10.1109/cdc.2007.4434894 es_ES
dc.description.references Yang, Y.-H. E., & Prasanna, V. K. (2011). Space-time tradeoff in regular expression matching with semi-deterministic finite automata. 2011 Proceedings IEEE INFOCOM. doi:10.1109/infcom.2011.5934986 es_ES
dc.description.references Normativa Sobre Drones en España [2019]—Aerial Insightshttp://www.aerial-insights.co/blog/normativa-drones-espana/ es_ES
dc.description.references Disposición 15721 del BOE núm. 316 de 2017 - BOE.eshttps://www.boe.es/boe/dias/2017/12/29/pdfs/BOE-A-2017-15721.pdf es_ES
dc.description.references Velasco-Carrau, J., García-Nieto, S., Salcedo, J. V., & Bishop, R. H. (2016). Multi-Objective Optimization for Wind Estimation and Aircraft Model Identification. Journal of Guidance, Control, and Dynamics, 39(2), 372-389. doi:10.2514/1.g001294 es_ES
dc.description.references Vanegas, G., Samaniego, F., Girbes, V., Armesto, L., & Garcia-Nieto, S. (2018). Smooth 3D path planning for non-holonomic UAVs. 2018 7th International Conference on Systems and Control (ICSC). doi:10.1109/icosc.2018.8587835 es_ES
dc.description.references Samaniego, F., Sanchis, J., Garcia-Nieto, S., & Simarro, R. (2018). Comparative Study of 3-Dimensional Path Planning Methods Constrained by the Maneuverability of Unmanned Aerial Vehicles. 2018 7th International Conference on Systems and Control (ICSC). doi:10.1109/icosc.2018.8587810 es_ES


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