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All issues Volume 40 / No 6 (December 2022) JNWPU, 40 6 (2022) 1288-1296 References
Open Access
Issue
JNWPU
Volume 40, Number 6, December 2022
Page(s) 1288 - 1296
DOI https://doi.org/10.1051/jnwpu/20224061288
Published online 10 February 2023
  1. GUPTE S L, MOHANDAS P I T, CONRAD J M, et al. A survey of quadrotor unmanned aerial vehicles[C]//Proceedings of IEEE Southeastcon, 2012 [Google Scholar]
  2. ANGELOV P. Sense and avoid in UAS: research and applications[M]. Hoboken: John Wiley & Sons, Ltd., 2012: 99–103 [Google Scholar]
  3. KORF R E. Depth-first iterative-deepening: an optimal admissible tree search[J]. Artificial Intelligence, 2012, 27(1): 97–109 [Google Scholar]
  4. CHABINI I, LAN S. Adaptations of the A* algorithm for the computation of fastest paths in deterministic discrete-time dynamic networks[J]. IEEE Trans on Intelligent Transportation Systems, 2002, 3(1): 60–74 [Article] [CrossRef] [Google Scholar]
  5. NANNICINI G, SCHULTE D, LIBERTI L, et al. Bidirectional A* search on time-dependent road networks[J]. Networks, 2011, 59(2): 240–251 [Google Scholar]
  6. STENTX A. Optimal and efficient path planning for partially known environments//Intelligent unmanned ground vehicles[M] Boston: Springer, 1997: 203–220 [Google Scholar]
  7. WONG W E, DEBROY V, GAO R, et al. The DStar method for effective software fault localization[J]. IEEE Trans on Reliability, 2014, 63(1): 290–238 [Article] [CrossRef] [Google Scholar]
  8. KOEING S, LIKHACHEV M. Fast replanning for navigation in unknown terrain[J]. IEEE Trans on Robotics, 2005, 21(3): 254–363 [Google Scholar]
  9. KARAMAN S, WALTER M R, PEREZ A, et al. Anytime Motion Planning using the RRT*[C]//IEEE International Conference on Robotics and Automation, 2011 [Google Scholar]
  10. RODRIGUEZ S, TANG X Y, LIEN J M, et al. An obstacle-based rapidly-exploring random tree[C]//IEEE International Conference on Robotics and Automation, 2006 [Google Scholar]
  11. WU X, XU L, ZHEN R, et al. Biased sampling potentially guided intelligent bidirectional RRT* algorithm for UAV path planning in 3D environment[J]. Mathematical Problems in Engineering, 2019, 2019: 5157403 [Google Scholar]
  12. SONG J, ZHAO M, LIU Y, et al. Multi-rotor UAVs path planning method based on improved artificial potential field method[C]//2019 Chinese Control Conference, 2019 [Google Scholar]
  13. SNAPE J, BERG J V D, GUY S J, et al. The hybrid reciprocal velocity obstacle[J]. IEEE Trans on Robotics, 2011, 27(4): 696–709 [Article] [CrossRef] [Google Scholar]
  14. ULRICH I, BORENSTEIN J. VFH+: reliable obstacle avoidance for fast mobile robots[C]//IEEE International Conference on Robotics and Automation, 1998 [Google Scholar]
  15. RASHEDI E, HOSSEIN N-P, SAEID S, et al. GSA: a gravitational search algorithm[J]. Information Sciences, 2009, 179(13): 2232–2248 [Article] [CrossRef] [Google Scholar]
  16. PETER J. M, LAARHOVEN V, EMILE H, et al. Simulated annealing//Simulated annealing: theory and application[M]. Dordrecht: Springer, 1987: 7-15 [Google Scholar]
  17. HU Y R, YANG S X. A knowledge based genetic algorithm for path planning of a mobile robot[C]//IEEE International Conference on Robotics and Automation, 2004 [Google Scholar]
  18. LYU Y, CHEN Y, TIAN J. Particle swarm optimization for fixed-wing UAV path planning[C]//International Conference on Autonomous Unmanned Systems, Singapore, 2021 [Google Scholar]
  19. CHHOTRAY A, DAYAL R P. Navigational control analysis of two-wheeled self-balancing robot in an unknown terrain using back-propagation neural network integrated modified DAYANI approach[J]. Scholarly Journal, 2019, 37(8): 1346–1362 [Google Scholar]
  20. SARADINDU G, PARTAP K P, PARHI D R. Performance comparison of novel WNN approach with RBFNN in navigation of autonomous mobile robotic agent[J]. Serbian Journal of Electrical Engineering, 2016, 13(2): 239–262 [Article] [CrossRef] [Google Scholar]
  21. KAHN G, VILLAFLOR A, DING B, et al. Self-supervised deep reinforcement learning with generalized computation graphs for robot navigation[C]//IEEE International Conference on Robotics and Automation, 2018 [Google Scholar]
  22. VANEGAS G. SAMANIEGO F, GIRBES V, et al. Smooth 3D path planning for non-holonomic UAVs[C]//International Conference on Systems and Control. 2018 [Google Scholar]
  23. LEE D, SONG H J, SHIM D H. Optimal path planning based on spline-RRT* for fixed-wing UAVs operating in three-dimensional environments[C]//International Conference on Control, Automation and Systems, 2014 [Google Scholar]
  24. KELLER J, THAKUR D, LIKHACHEV M, et al. Coordinated path planning for fixed-wing UAS conducting persistent surveillance missions[J]. IEEE Trans on Automation Science and Engineering, 2017, 14(1): 17–24 [Article] [CrossRef] [Google Scholar]
  25. BENDERS S, SCHOPFERER S. A line-graph path planner for performance constrained fixed-wing UAVs in wind fields[C]//International Conference on Unmanned Aircraft Systems, 2017 [Google Scholar]
  26. SONG X Q, HU S Q. 2D path planning with dubins-path-based A*algorithm for a fixed-wing UAV[C]//IEEE International Conference on Control Science and Systems Engineering, 2017 [Google Scholar]
  27. SANDERS G, RAY T. Optimal offline path planning of a fixed wing unmanned aerial vehicle (UAV) using an evolutionary algorithm[C]//IEEE Congress on Evolutionary Computation, 2007 [Google Scholar]
  28. ROBERGE V, TARBOUCHI M, LABONTE G. Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning[J]. IEEE Trans on Industrial Informatics, 2013, 9(1): 132–141 [Article] [CrossRef] [Google Scholar]
  29. ROBERGE V, TARBOUCHI M, LABONTE G. Fast genetic algorithm path planner for fixed-wing military UAV using GPU[J]. IEEE Trans on Aerospace and Electronic Systems, 2018, 54(5): 2015–2117 [Google Scholar]
  30. DUBINS L E. On curves of minimal length with a constraint on average curvature, and with prescribed initial and terminal positions and tangents[J]. American Journal of Mathematics, 1957, 79(3): 497–516 [Article] [CrossRef] [Google Scholar]
  31. SHKEL A M, LUMELSKY V. Classification of the dubins set[J]. Robotics and Autonomous Systems, 2001, 34(4): 179–202 [Article] [CrossRef] [Google Scholar]

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