Az UAV-pályatervezés kihívásai és lehetséges megoldásai
Copyright (c) 2024 Mihályi Géza
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Absztrakt
Kutatásom során az UAV-pályatervezés nehézségeit és kihívásait vizsgáltam. Bemutatom az esetlegesen felmerülő legismertebb problémákat. Ilyen lehet a „pontszerű test”-probléma (Point Vehicle) vagy a „kocogó”-probléma (Jogger’s Problem). Bemutatom a legismertebb és jelen tudásunk szerint leghatásosabb, State-of-Art1 megoldásokat is, mint a Visible Graph vagy az A* alapú algoritmusok.
Kulcsszavak:
Hogyan kell idézni
Hivatkozások
T. Amukele, „Using Drones to Deliver Blood Products in Rwanda,” The Lancet Global Health, pp. e463–e464, 2022. Online: https://doi.org/10.1016/S2214-109X(22)00095-X
A. N. Albert et al., „Intricacies of Medical Drones in Healthcare Delivery: Implications for Africa,” Technology in Society, 51. szám, 66, p. 101624, 2021. Online: https://doi.org/10.1016/j.techsoc.2021.101624
„The Verge,” [Online]. Elérhető: https://www.theverge.com/sponsored/goldman-sachs-drones.
„Statista,” [Online]. Elérhető: https://shorturl.at/f0TuN
H. Xiaojian et al., „A UAV Dynamic Path Planning Algorithm,” in 2020 35th Youth Academic Annual Conference of Chinese Association of Automation (YAC), 2020, pp. 127–131.
J. F. Shortle et al., „Simulating Collision Probabilities of Landing Airplanes at Nontowered Airports,” Simulation, pp. 21–31. 2004. Online: https://doi.org/10.1177/0037549704042028
B. M. Sathyaraj et al., „Multiple UAVs Path Planning Algorithms: A Comparative Study,” Fuzzy Optimization and Decision Making, pp. 257–267. 2008. Online: https://doi.org/10.1007/s10700-008-9035-0
S. Aggarwal, N. Kumar, „Path Planning Techniques for Unmanned Aerial Vehicles: A Review, Solutions, and Challenges,” Computer Communications, pp. 270–299. 2020. Online: https://doi.org/10.1016/j.comcom.2019.10.014
R. Szabolcsi, „3D Flight Path Planning For Multirotor UAV,” Review of the Air Force Academy, pp. 5–16, 2020. Online: https://doi.org/10.19062/1842-9238.2020.18.1.1
R. Szabolcsi, „Multirotoros pilóta nélküli légijárművek háromdimenziós repülési pályáinak számítógépes tervezése és szimulációja,” Hadtudomány, pp. 133–150. 2020. Online: https://doi.org/10.17047/HADTUD.2020.30.4.133
R. Szabolcsi, „Flight Path Planning for Small UAV Low Altitude Flights,” Repüléstudományi Közlemények, pp. 159–167. 2020. Online: https://doi.org/10.2478/raft-2020-0019
R. Szabolcsi, „Pilóta nélküli légi jármű kis magasságú repülési pályáinak tervezése,” Repüléstudományi Közlemények, 2020. Online: https://doi.org/10.32560/rk.2020.1.2
C. Goerzen, Z. Kong, B. Mettler, „A Survey of Motion Planning Algorithms from the Perspective of Autonomous UAV Guidance,” Journal of Intelligent and Robotic Systems, pp. 65–100. 2020. Online: https://doi.org/10.1007/s10846-009-9383-1
H. Liu et al., „An Autonomous Path Planning Method for Unmanned Aerial Vehicle Based on a Tangent Intersection and Target Guidance Strategy,” IEEE Transactions on Intelligent Transportation Systems, pp. 3061–3073. 2022. Online: https://doi.org/10.1109/TITS.2020.3030444
Bortoff, Scott, „Path Planning for UAVs,” American Control Conference, 2000. Proceedings of the 2000, pp. 364–368. 2000. Online: https://doi.org/10.1109/ACC.2000.878915
Balampanis et al., 2017 International Conference on Unmanned Aircraft Systems (ICUAS), 2017.
M. Rhinehart, Monte Carlo Testing of 2- and 3-dimensional Route Planners for Autonomous UAV Navigation in Urban Environments, Thesis (M.S.) University of Minnesota, 2008.
[Online]. Elérhető: https://cs.stanford.edu/people/eroberts/courses/soco/projects/1998-99/robotics/basicmotion.html.
Blasi et al., „Path Planning and Real-Time Collision Avoidance Based on the Essential Visibility Graph,” Applied Sciences, p. 5613. 2020. Online: https://doi.org/10.3390/app10165613
C. Xia, C. Xiangmin, „The UAV Dynamic Path Planning Algorithm Research Based on Voronoi Diagram,” The 26th Chinese Control and Decision Conference (2014 CCDC), pp. 1069–1071. 2014.
I. W. Geoffrey, C. Sammut, Encyclopedia of Machine Learning. Boston, MA: Springer US, 2010. Online: https://doi.org/10.1007/978-0-387-30164-8
V. Jeauneau, A. Kotenkoff, L. Jouanneau, „Path Planner Methods for UAVs in Real Environment,” FAC-PapersOnLine, pp. 292–297. 2018. Online: https://doi.org/10.1016/j.ifacol.2018.11.557
F. Daniel et al., „A Systematic Literature Review of A* Pathfinding,” Procedia Computer Science, pp. 507–514. 2021. Online: https://doi.org/10.1016/j.procs.2021.01.034
J. Borenstein, Y. Koren, „Potential Field Methods and Their Inherent Limitations for Mobile Robot Navigation,” Proceedings – IEEE International Conference on Robotics and Automation, pp. 1398–1404. 1991.
[Online]. Elérhető: http://www-personal.umich.edu/~johannb/vff&vfh.htm.
J. Borenstein, Y. Koren, „The Vector Field Histogram-Fast Obstacle Avoidance for Mobile Robots,” IEEE Transactions on Robotics and Automation, pp. 278–288. 1991. Online: https://doi.org/10.1109/70.88137
T. Ahmad et al., „Drone Deep Reinforcement Learning: A Review,” Electronics, 2021. Online: https://doi.org/10.3390/electronics10090999
H. Xiaojian et al., „A UAV Dynamic Path Planning Algorithm,” in 2020 35th Youth Academic Annual Conference of Chinese Association of Automation (YAC), 2020, pp. 127–131. Online: https://doi.org/10.1109/YAC51587.2020.9337581
Cetin et al., „Drone Navigation and Avoidance of Obstacles Through Deep Reinforcement Learning,” in 2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC), 2019, pp. 1–7. Online: https://doi.org/10.1109/DASC43569.2019.9081749