64 research outputs found

    Smooth 3D Path Planning by Means of Multiobjective Optimization for Fixed-Wing UAVs

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    [EN] Demand for 3D planning and guidance algorithms is increasing due, in part, to the increase in unmanned vehicle-based applications. Traditionally, two-dimensional (2D) trajectory planning algorithms address the problem by using the approach of maintaining a constant altitude. Addressing the problem of path planning in a three-dimensional (3D) space implies more complex scenarios where maintaining altitude is not a valid approach. The work presented here implements an architecture for the generation of 3D flight paths for fixed-wing unmanned aerial vehicles (UAVs). The aim is to determine the feasible flight path by minimizing the turning effort, starting from a set of control points in 3D space, including the initial and final point. The trajectory generated takes into account the rotation and elevation constraints of the UAV. From the defined control points and the movement constraints of the UAV, a path is generated that combines the union of the control points by means of a set of rectilinear segments and spherical curves. However, this design methodology means that the problem does not have a single solution; in other words, there are infinite solutions for the generation of the final path. For this reason, a multiobjective optimization problem (MOP) is proposed with the aim of independently maximizing each of the turning radii of the path. Finally, to produce a complete results visualization of the MOP and the final 3D trajectory, the architecture was implemented in a simulation with Matlab/Simulink/flightGear.The authors would like to acknowledge the Spanish Ministerio de Ciencia, Innovacion y Universidades for providing funding through the project RTI2018-096904-B-I00 and the local administration Generalitat Valenciana through projects GV/2017/029 and AICO/2019/055. Franklin Samaniego thanks IFTH (Instituto de Fomento al Talento Humano) Ecuador (2015-AR2Q9209), for its sponsorship of this work.Samaniego, F.; Sanchís Saez, J.; Garcia-Nieto, S.; Simarro Fernández, R. (2020). Smooth 3D Path Planning by Means of Multiobjective Optimization for Fixed-Wing UAVs. Electronics. 9(1):1-23. https://doi.org/10.3390/electronics9010051S12391Kyriakidis, M., Happee, R., & de Winter, J. C. F. (2015). Public opinion on automated driving: Results of an international questionnaire among 5000 respondents. Transportation Research Part F: Traffic Psychology and Behaviour, 32, 127-140. doi:10.1016/j.trf.2015.04.014Münzer, S., Zimmer, H. D., Schwalm, M., Baus, J., & Aslan, I. (2006). Computer-assisted navigation and the acquisition of route and survey knowledge. Journal of Environmental Psychology, 26(4), 300-308. doi:10.1016/j.jenvp.2006.08.001Morales, Y., Kallakuri, N., Shinozawa, K., Miyashita, T., & Hagita, N. (2013). Human-comfortable navigation for an autonomous robotic wheelchair. 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. doi:10.1109/iros.2013.6696743Krotkov, E., & Hebert, M. (s. f.). Mapping and positioning for a prototype lunar rover. Proceedings of 1995 IEEE International Conference on Robotics and Automation. doi:10.1109/robot.1995.525697Rodriguez-Seda, E. J. (2014). Decentralized trajectory tracking with collision avoidance control for teams of unmanned vehicles with constant speed. 2014 American Control Conference. doi:10.1109/acc.2014.6859184Xiaoping Ren, & Zixing Cai. (2010). Kinematics model of unmanned driving vehicle. 2010 8th World Congress on Intelligent Control and Automation. doi:10.1109/wcica.2010.5554512Jun, J.-Y., Saut, J.-P., & Benamar, F. (2016). Pose estimation-based path planning for a tracked mobile robot traversing uneven terrains. Robotics and Autonomous Systems, 75, 325-339. doi:10.1016/j.robot.2015.09.014Li, Y., Ding, L., & Liu, G. (2016). Attitude-based dynamic and kinematic models for wheels of mobile robot on deformable slope. Robotics and Autonomous Systems, 75, 161-175. doi:10.1016/j.robot.2015.10.006Mekonnen, G., Kumar, S., & Pathak, P. M. (2016). Wireless hybrid visual servoing of omnidirectional wheeled mobile robots. Robotics and Autonomous Systems, 75, 450-462. doi:10.1016/j.robot.2015.08.008Xu, J., Wang, M., & Qiao, L. (2015). Dynamical sliding mode control for the trajectory tracking of underactuated unmanned underwater vehicles. Ocean Engineering, 105, 54-63. doi:10.1016/j.oceaneng.2015.06.022Gafurov, S. A., & Klochkov, E. V. (2015). Autonomous Unmanned Underwater Vehicles Development Tendencies. Procedia Engineering, 106, 141-148. doi:10.1016/j.proeng.2015.06.017Qi, X., & Cai, Z. (2018). Three-dimensional formation control based on nonlinear small gain method for multiple underactuated underwater vehicles. Ocean Engineering, 151, 105-114. doi:10.1016/j.oceaneng.2018.01.032Ramasamy, S., Sabatini, R., Gardi, A., & Liu, J. (2016). LIDAR obstacle warning and avoidance system for unmanned aerial vehicle sense-and-avoid. Aerospace Science and Technology, 55, 344-358. doi:10.1016/j.ast.2016.05.020Zhu, L., Cheng, X., & Yuan, F.-G. (2016). A 3D collision avoidance strategy for UAV with physical constraints. Measurement, 77, 40-49. doi:10.1016/j.measurement.2015.09.006Chee, K. Y., & Zhong, Z. W. (2013). Control, navigation and collision avoidance for an unmanned aerial vehicle. Sensors and Actuators A: Physical, 190, 66-76. doi:10.1016/j.sna.2012.11.017Courbon, J., Mezouar, Y., Guénard, N., & Martinet, P. (2010). Vision-based navigation of unmanned aerial vehicles. Control Engineering Practice, 18(7), 789-799. doi:10.1016/j.conengprac.2010.03.004Aguilar, 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/electronics5040070Yan, 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/s11633-013-0750-9Yeh, 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.2012.6385875Liang, Y., & Xu, L. (2009). Global path planning for mobile robot based genetic algorithm and modified simulated annealing algorithm. Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation - GEC ’09. doi:10.1145/1543834.1543875Liu, J., Yang, J., Liu, H., Tian, X., & Gao, M. (2016). An improved ant colony algorithm for robot path planning. Soft Computing, 21(19), 5829-5839. doi:10.1007/s00500-016-2161-7Cao, H., Sun, S., Zhang, K., & Tang, Z. (2016). Visualized trajectory planning of flexible redundant robotic arm using a novel hybrid algorithm. Optik, 127(20), 9974-9983. doi:10.1016/j.ijleo.2016.07.078Duan, H., & Qiao, P. (2014). Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning. International Journal of Intelligent Computing and Cybernetics, 7(1), 24-37. doi:10.1108/ijicc-02-2014-0005Pandey, A., & Parhi, D. R. (2017). Optimum path planning of mobile robot in unknown static and dynamic environments using Fuzzy-Wind Driven Optimization algorithm. Defence Technology, 13(1), 47-58. doi:10.1016/j.dt.2017.01.001Samaniego, F., Sanchis, J., García-Nieto, S., & Simarro, R. (2019). Recursive Rewarding Modified Adaptive Cell Decomposition (RR-MACD): A Dynamic Path Planning Algorithm for UAVs. Electronics, 8(3), 306. doi:10.3390/electronics8030306Dubins, L. E. (1957). On Curves of Minimal Length with a Constraint on Average Curvature, and with Prescribed Initial and Terminal Positions and Tangents. American Journal of Mathematics, 79(3), 497. doi:10.2307/2372560Fleury, S., Soueres, P., Laumond, J.-P., & Chatila, R. (1995). Primitives for smoothing mobile robot trajectories. IEEE Transactions on Robotics and Automation, 11(3), 441-448. doi:10.1109/70.388788Vanegas, 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.2018.8587835Brezak, M., & Petrovic, I. (2014). Real-time Approximation of Clothoids With Bounded Error for Path Planning Applications. IEEE Transactions on Robotics, 30(2), 507-515. doi:10.1109/tro.2013.2283928Barsky, B. A., & DeRose, T. D. (1989). Geometric continuity of parametric curves: three equivalent characterizations. IEEE Computer Graphics and Applications, 9(6), 60-69. doi:10.1109/38.41470Kim, H., Kim, D., Shin, J.-U., Kim, H., & Myung, H. (2014). Angular rate-constrained path planning algorithm for unmanned surface vehicles. Ocean Engineering, 84, 37-44. doi:10.1016/j.oceaneng.2014.03.034Isaacs, J., & Hespanha, J. (2013). Dubins Traveling Salesman Problem with Neighborhoods: A Graph-Based Approach. Algorithms, 6(1), 84-99. doi:10.3390/a6010084Masehian, E., & Kakahaji, H. (2014). NRR: a nonholonomic random replanner for navigation of car-like robots in unknown environments. Robotica, 32(7), 1101-1123. doi:10.1017/s0263574713001276Fraichard, T., & Scheuer, A. (2004). From Reeds and Shepp’s to Continuous-Curvature Paths. IEEE Transactions on Robotics, 20(6), 1025-1035. doi:10.1109/tro.2004.833789Pepy, R., Lambert, A., & Mounier, H. (s. f.). Path Planning using a Dynamic Vehicle Model. 2006 2nd International Conference on Information & Communication Technologies. doi:10.1109/ictta.2006.1684472Girbés, V., Vanegas, G., & Armesto, L. (2019). Clothoid-Based Three-Dimensional Curve for Attitude Planning. Journal of Guidance, Control, and Dynamics, 42(8), 1886-1898. doi:10.2514/1.g003551De Lorenzis, L., Wriggers, P., & Hughes, T. J. R. (2014). Isogeometric contact: a review. GAMM-Mitteilungen, 37(1), 85-123. doi:10.1002/gamm.201410005Pigounakis, K. G., Sapidis, N. S., & Kaklis, P. D. (1996). Fairing Spatial B-Spline Curves. Journal of Ship Research, 40(04), 351-367. doi:10.5957/jsr.1996.40.4.351Pérez, L. H., Aguilar, M. C. M., Sánchez, N. M., & Montesinos, A. F. (2018). Path Planning Based on Parametric Curves. Advanced Path Planning for Mobile Entities. doi:10.5772/intechopen.72574Huh, U.-Y., & Chang, S.-R. (2014). A G2 Continuous Path-smoothing Algorithm Using Modified Quadratic Polynomial Interpolation. International Journal of Advanced Robotic Systems, 11(2), 25. doi:10.5772/57340Chang, S.-R., & Huh, U.-Y. (2014). A Collision-Free G2 Continuous Path-Smoothing Algorithm Using Quadratic Polynomial Interpolation. International Journal of Advanced Robotic Systems, 11(12), 194. doi:10.5772/59463Yaochu Jin, & Sendhoff, B. (2008). Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 38(3), 397-415. doi:10.1109/tsmcc.2008.919172Velasco-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.g001294Honig, E., Schucking, E. L., & Vishveshwara, C. V. (1974). Motion of charged particles in homogeneous electromagnetic fields. Journal of Mathematical Physics, 15(6), 774-781. doi:10.1063/1.1666728Iyer, B. R., & Vishveshwara, C. V. (1988). The Frenet-Serret formalism and black holes in higher dimensions. Classical and Quantum Gravity, 5(7), 961-970. doi:10.1088/0264-9381/5/7/005Laumanns, M., Thiele, L., Deb, K., & Zitzler, E. (2002). Combining Convergence and Diversity in Evolutionary Multiobjective Optimization. Evolutionary Computation, 10(3), 263-282. doi:10.1162/106365602760234108Blasco, X., Herrero, J. M., Sanchis, J., & Martínez, M. (2008). A new graphical visualization of n-dimensional Pareto front for decision-making in multiobjective optimization. Information Sciences, 178(20), 3908-3924. doi:10.1016/j.ins.2008.06.01

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

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    [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.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.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/electronics8030306S12183Valavanis, K. P., & Vachtsevanos, G. J. (Eds.). (2015). Handbook of Unmanned Aerial Vehicles. doi:10.1007/978-90-481-9707-120 Great UAV Applications Areas for Droneshttp://air-vid.com/wp/20-great-uav-applications-areas-drones/Industry Experts—Microdroneshttps://www.microdrones.com/en/industry-experts/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.2016.2626293Stuchlík, 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/cpr2015-2-18Pulver, A., & Wei, R. (2018). Optimizing the spatial location of medical drones. Applied Geography, 90, 9-16. doi:10.1016/j.apgeog.2017.11.009Claesson, 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.2017.01.003Reineman, 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-12-00176.1LaValle, S. M. (2006). Planning Algorithms. doi:10.1017/cbo9780511546877Elbanhawi, M., & Simic, M. (2014). Sampling-Based Robot Motion Planning: A Review. IEEE Access, 2, 56-77. doi:10.1109/access.2014.2302442Hernandez, 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.2017.7854617Kohlbrecher, 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.2011.6106777Aguilar, 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/electronics5040070Aguilar, 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_50Yao, 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.2015.7392202Yan, 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/s11633-013-0750-9Yeh, 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.2012.6385875Denny, 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_18Li, 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.2014.7007587Ortiz-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.2015.7440999Thanou, 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.2014.6877904Qu, 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.2014.6842250Fang, 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_31Khuswendi, 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.2011.6021579Chen, 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.2013.181Rivera, 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.2012.26Liu 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.2016.7829099Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1(1), 269-271. doi:10.1007/bf01386390Verscheure, 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.2010.5626112Hart, 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.1968.300136Ferguson, 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_22De 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/s10846-011-9568-2Gautam, 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.2014.6954257Maturana, 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.2015.7139679Iswanto, 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.v14i2.2989Duan, 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.2009.10.006He, 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.2013.6561444Zhang, 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:10.1155/2013/705238Goel, 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.2018.07.028YongBo, 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.2017.05.059Wang, 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.2015.11.040Aghababa, 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.2012.06.002Mac, 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.2016.08.001Szirmay-Kalos, L., & Márton, G. (1998). Worst-case versus average case complexity of ray-shooting. Computing, 61(2), 103-131. doi:10.1007/bf02684409Berger, 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)90073-1Min, 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.2006.07.019Hasbestan, 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.2018.04.039Pantano, 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.2006.06.011Ryde, J., & Hu, H. (2009). 3D mapping with multi-resolution occupied voxel lists. Autonomous Robots, 28(2), 169-185. doi:10.1007/s10514-009-9158-3Samet, 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.2002.1024101Samaniego, 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.2017.8247533Skoldstam, 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.2007.4434894Yang, 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.2011.5934986Normativa Sobre Drones en España [2019]—Aerial Insightshttp://www.aerial-insights.co/blog/normativa-drones-espana/Disposición 15721 del BOE núm. 316 de 2017 - BOE.eshttps://www.boe.es/boe/dias/2017/12/29/pdfs/BOE-A-2017-15721.pdfVelasco-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.g001294Vanegas, 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.2018.8587835Samaniego, 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.2018.858781

    HCVerso3: An Open-Label, Phase IIb Study of Faldaprevir and Deleobuvir with Ribavirin in Hepatitis C Virus Genotype-1b-Infected Patients with Cirrhosis and Moderate Hepatic Impairment

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    This study evaluated the interferon-free, oral combination of deleobuvir (non-nucleoside HCV NS5-RNA-polymerase inhibitor) and faldaprevir (HCV NS3/4A-protease inhibitor) with ribavirin in patients with HCV genotype-1b and moderate (Child-Pugh B [CPB], n = 17) or mild hepatic impairment (Child-Pugh A [CPA], n = 18). Patients received faldaprevir 120 mg and deleobuvir (600 mg [CPA], 400 mg [CPB]) twice-daily with weight-based ribavirin for 24 weeks. Baseline characteristics were similar between groups. Among CPA patients, 13/18 completed treatment; discontinuations were for adverse events (AEs, n = 1), lack of efficacy (n = 3) and withdrawal (n = 1). Among CPB patients, 8/17 completed treatment; discontinuations were for AEs (n = 6), withdrawal (n = 1) and 'other' (n = 2). Sustained virologic response at post-treatment Week 12 (SVR12) was achieved by 11 (61%) CPA patients (95% confidence interval: 38.6%-83.6%) and 9 (53%) CPB patients (95% confidence interval: 29.2%-76.7%), including most CPA (11/16) patients with Week 4 HCV RNA <25 IU.mL-1 (target detected or not detected) and most CPB (8/9) patients with Week 4 HCV RNA <25 IU.mL-1 (target not detected); 0/4 CPB patients with Week 4 HCV RNA <25 IU.mL-1 (target detected) achieved SVR12. The most common AEs in both groups were nausea, diarrhoea and vomiting. Serious AEs were observed in 9 (53%) CPB patients and 1 (6%) CPA patient. Plasma trough concentrations of deleobuvir and faldaprevir were not substantially different between the CPA and CPB groups. In conclusion, in this small study the safety and efficacy profiles for 24 weeks of treatment with faldaprevir+deleobuvir+ribavirin in patients with mild or moderate hepatic impairment were consistent with the safety and efficacy profile of this regimen in non-cirrhotic patients. Faldaprevir+deleobuvir+ribavirin resulted in SVR12 in 53-61% of patients: proportions achieving SVR4 but not SVR12 were higher than in non-cirrhotic patients and overall response rates were lower than rates reported with other all-oral regimens in patients with cirrhosis

    High-resolution hepatitis C virus subtyping using NS5B deep sequencing and phylogeny, an alternative to current methods

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    HepatitisCvirus(HCV)is classified into seven major genotypesand67 subtypes. Recent studies haveshownthat inHCVgenotype 1-infected patients, response rates to regimens containingdirect-acting antivirals(DAAs)are subtype dependent. Currently available genotypingmethods have limited subtyping accuracy.Wehave evaluated theperformanceof adeep-sequencing-basedHCVsubtyping assay, developed for the 454/GS-Junior platform, in comparisonwith thoseof two commercial assays (VersantHCVgenotype 2.0andAbbott Real-timeHCVGenotype II)andusingdirectNS5Bsequencing as a gold standard (direct sequencing), in 114 clinical specimenspreviously tested by first-generation hybridization assay (82 genotype 1and32 with uninterpretable results). Phylogenetic analysis of deep-sequencing reads matched subtype 1 callingbypopulation Sanger sequencing(69%1b,31%1a) in 81 specimensandidentified amixed-subtype infection (1b/3a/1a) in one sample. Similarly,amongthe 32previously indeterminate specimens, identical genotypeandsubtype results were obtained by directanddeep sequencing in all but four samples with dual infection. In contrast, both VersantHCVGenotype 2.0andAbbott Real-timeHCVGenotype II failed subtype 1 calling in 13 (16%) samples eachandwere unable to identify theHCVgenotype and/or subtype inmore than half of the nongenotype 1 samples.Weconcluded that deep sequencing ismore efficient forHCVsubtyping than currently available methodsandallows qualitative identificationofmixed infectionsandmay bemorehelpfulwith respect to informing treatment strategies withnewDAA-containing regimens across allHCVsubtypesThis study has been supported by CDTI (Centro para el Desarrollo Tecnológico Industrial), Spanish Ministry of Economics and Competitiveness (MINECO), IDI-20110115; MINECO projects SAF 2009-10403; and also by the Spanish Ministry of Health, Instituto de Salud Carlos III (FIS) projects PI10/01505, PI12/01893, and PI13/00456. CIBERehd is funded by the Instituto de Salud Carlos III, Madrid, Spain. Work at CBMSO was supported by grant MINECO-BFU2011-23604, FIPSE, and Fundación Ramón Areces. X. Forns received unrestricted grant support from Roche and has acted as advisor for MSD, Gilead, and Abbvie. M. Alvarez-Tejado, J. Gregori, and J. M. Muñoz work in Roche Diagnostic

    Novel potential predictive markers of sunitinib outcomes in long-term responders versus primary refractory patients with metastatic clear-cell renal cell carcinoma

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    Background: Several potential predictive markers of efficacy of targeted agents in patients with metastatic renal cell carcinoma (mRCC) have been identified. Interindividual heterogeneity warrants further investigation. Patients and methods: Multicenter, observational, retrospective study in patients with clear-cell mRCC treated with sunitinib. Patients were classified in two groups: long-term responders (LR) (progression-free survival (PFS)=22 months and at least stable disease), and primary refractory (PR) (progressive disease within 3-months of sunitinib onset). Objectives were to compare baseline clinical factors in both populations and to correlate tumor expression of selected signaling pathways components with sunitinib PFS. Results: 123 patients were analyzed (97 LR, 26 PR). In the LR cohort, overall response rate was 79% and median duration of best response was 30 months. Median PFS and overall survival were 43.2 (95% confidence intervals[CI]:37.2-49.3) and 63.5 months (95%CI:55.1-71.9), respectively. At baseline PR patients had a significantly lower proportion of nephrectomies, higher lactate dehydrogenase and platelets levels, lower hemoglobin, shorter time to and higher presence of metastases, and increased Fuhrman grade. Higher levels of HEYL, HEY and HES1 were observed in LR, although only HEYL discriminated populations significantly (AUC[ROC]=0.704; cut-off=34.85). Increased levels of hsa-miR-27b, hsa-miR-23b and hsa-miR-628-5p were also associated with prolonged survival. No statistical significant associations between hsa-miR-23b or hsa-miR-27b and the expression of c-Met were found. Conclusions: Certain mRCC patients treated with sunitinib achieve extremely long-term responses. Favorable baseline hematology values and longer time to metastasis may predict longer PFS. HEYL, hsa-miR-27b, hsa-miR-23b and hsa-miR- 628-5p could be potentially used as biomarkers of sunitinib response

    Comparative study of paediatric prescription drug utilization between the spanish and immigrant population

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    <p>Abstract</p> <p>Background</p> <p>The immigrant population has increased greatly in Spain in recent years to the point where immigrants made up 12% of the infant population in 2008. There is little information available on the profile of this group with regard to prescription drug utilization in universal public health care systems such as that operating in Spain. This work studies the overall and specific differences in prescription drug utilization between the immigrant and Spanish population.</p> <p>Methods</p> <p>Use was made of the Aragonese Health Service databases for 2006. The studied population comprises 159,908 children aged 0-14 years, 13.6% of whom are foreign nationals. Different utilization variables were calculated for each group. Prescription-drug consumption is measured in Defined Daily Doses (DDD) and DDD/1000 persons/day/(DID).</p> <p>Results</p> <p>A total of 833,223 prescriptions were studied. Utilization is lower for immigrant children than in Spanish children for both DID (66.27 v. 113.67) and average annual expense (€21.55 v. €41.14). Immigrant children consume fewer prescription drugs than Spanish children in all of the therapy groups, with the most prescribed (in DID) being: respiratory system, anti-infectives for systemic use, nervous system, sensory organs. Significant differences were observed in relation to the type of drugs and the geographical background of immigrants.</p> <p>Conclusion</p> <p>Prescription drug utilization is much greater in Spanish children than in immigrant children, particularly with reference to bronchodilators (montelukast and terbutaline) and attention-disorder hyperactivity drugs such as methylphenidate. There are important differences regarding drug type and depending on immigrants' geographical backgrounds that suggest there are social, cultural and access factors underlying these disparities.</p

    Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

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    BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

    Preemptive-TIPS improves outcome in high-risk variceal bleeding : An observational study

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    Objective Patients admitted with acute variceal bleeding (AVB) and Child Pugh C score (CP\u2010C) or Child Pugh B plus active bleeding at endoscopy (CP\u2010B+AB) are at high risk for treatment failure, rebleeding and mortality. Preemptive TIPS (p\u2010TIPS) has been shown to improve survival in these patients but its use in clinical practice has been challenged and not routinely incorporated. The present study aimed to further validate the role of preemptive TIPS in a large number of high\u2010risk patients. Design Multicenter, international, observational study including 671 patients from 34 centers admitted for AVB and high\u2010risk of treatment failure. Patients were managed according to current guidelines and use of drugs and endoscopic therapy (D+E) or preemptive TIPS (p\u2010TIPS) was based on individual center policy. Results p\u2010TIPS in the setting of AVB is associated with a lower mortality in Child C patients compared to D+E (1 year mortality 22% vs 47% in D+E group; P=0.002). Mortality rate in CP\u2010B+AB patients was low and p\u2010TIPS did not improve it. In CP\u2010C and CP\u2010B +AB patients, p\u2010TIPS reduces treatment failure and rebleeding (1 year CIF\u2010probability of remaining free of the composite endpoint: 92% vs 74% in the D+E group; P=0.017), development of \u201cde novo\u201d or worsening of previous ascites without increasing rates of hepatic encephalopathy. Conclusion p\u2010TIPS must be the treatment of choice in CP\u2010C patients with AVB. Due to the strong benefit in preventing further bleeding and ascites, p\u2010TIPS could be a good treatment strategy for CP\u2010B+AB patients

    Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

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    Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication

    Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

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    Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research
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