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

3D Dynamic Motion Planning for Robot-Assisted Cannula Flexible Needle Insertion into Soft Tissue

In robot-assisted needle-based medical procedures, insertion motion planning is a crucial aspect. 3D dynamic motion planning for a cannula flexible needle is challenging with regard to the nonholonomic motion of the needle tip, the presence of anatomic obstacles or sensitive organs in the needle path, as well as uncertainties due to the dynamic environment caused by the movements and deformations of the organs. The kinematics of the cannula flexible needle is calculated in this paper. Based on a rapid and robust static motion planning algorithm, referred to as greedy heuristic and reachability-guided rapidly-exploring random trees, a 3D dynamic motion planner is developed by using replanning. Aiming at the large detour problem, the convergence problem and the accuracy problem that replanning encounters, three novel strategies are proposed and integrated into the conventional replanning algorithm. Comparisons are made between algorithms with and without the strategies to verify their validity. Simulations showed that the proposed algorithm can overcome the above-noted problems to realize real-time replanning in a 3D dynamic environment, which is appropriate for intraoperative planning. © 2016 Author

Hierarchical path-finding for Navigation Meshes (HNA*)

Path-finding can become an important bottleneck as both the size of the virtual environments and the number of agents navigating them increase. It is important to develop techniques that can be efficiently applied to any environment independently of its abstract representation. In this paper we present a hierarchical NavMesh representation to speed up path-finding. Hierarchical path-finding (HPA*) has been successfully applied to regular grids, but there is a need to extend the benefits of this method to polygonal navigation meshes. As opposed to regular grids, navigation meshes offer representations with higher accuracy regarding the underlying geometry, while containing a smaller number of cells. Therefore, we present a bottom-up method to create a hierarchical representation based on a multilevel k-way partitioning algorithm (MLkP), annotated with sub-paths that can be accessed online by our Hierarchical NavMesh Path-finding algorithm (HNA*). The algorithm benefits from searching in graphs with a much smaller number of cells, thus performing up to 7.7 times faster than traditional A¿ over the initial NavMesh. We present results of HNA* over a variety of scenarios and discuss the benefits of the algorithm together with areas for improvement.Peer ReviewedPostprint (author's final draft

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In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future