Unmanned Aerial Vehicles (UAVs) in environmental biology: A Review

Acquiring information about the environment is a key step during each study in the field of environmental biology at different levels, from an individual species to community and biome. However, obtaining information about the environment is frequently difficult because of, for example, the phenological timing, spatial distribution of a species or limited accessibility of a particular area for the field survey. Moreover, remote sensing technology, which enables the observation of the Earth’s surface and is currently very common in environmental research, has many limitations such as insufficient spatial, spectral and temporal resolution and a high cost of data acquisition. Since the 1990s, researchers have been exploring the potential of different types of unmanned aerial vehicles (UAVs) for monitoring Earth’s surface. The present study reviews recent scientific literature dealing with the use of UAV in environmental biology. Amongst numerous papers, short communications and conference abstracts, we selected 110 original studies of how UAVs can be used in environmental biology and which organisms can be studied in this manner. Most of these studies concerned the use of UAV to measure the vegetation parameters such as crown height, volume, number of individuals (14 studies) and quantification of the spatio-temporal dynamics of vegetation changes (12 studies). UAVs were also frequently applied to count birds and mammals, especially those living in the water. Generally, the analytical part of the present study was divided into following sections: (1) detecting, assessing and predicting threats on vegetation, (2) measuring the biophysical parameters of vegetation, (3) quantifying the dynamics of changes in plants and habitats and (4) population and behaviour studies of animals. At the end, we also synthesised all the information showing, amongst others, the advances in environmental biology because of UAV application. Considering that 33% of studies found and included in this review were published in 2017 and 2018, it is expected that the number and variety of applications of UAVs in environmental biology will increase in the future

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

[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