Open Access
Issue
RAIRO-Oper. Res.
Volume 58, Number 4, July-August 2024
Page(s) 2951 - 2989
DOI https://doi.org/10.1051/ro/2024073
Published online 31 July 2024
  • M.N. Ab Wahab, S. Nefti-Meziani and A. Atyabi, A comparative review on mobile robot path planning: classical or meta-heuristic methods? Annu. Rev. Control 50 (2020) 233–252. [CrossRef] [MathSciNet] [Google Scholar]
  • P. Abichandani, H.Y. Benson and M. Kam, Mathematical programming approaches for multi-vehicle motion planning: linear, nonlinear, and mixed integer programming. Found. Trends Rob. 2 (2013) 261–338. [Google Scholar]
  • F. Adolf, A. Langer, L.M.P. Silva and F. Thielecke, Probabilistic roadmaps and ant colony optimization for UAV mission planning. IFAC Proc. Vol. 40 (2007) 264–269. [CrossRef] [Google Scholar]
  • S. Aggarwal and N. Kumar, Path planning techniques for unmanned aerial vehicles: a review, solutions, and challenges. Comput. Commun. 149 (2020) 270–299. [CrossRef] [Google Scholar]
  • W.G. Aguilar, S. Morales, H. Ruiz and V. Abad, RRT* GL based optimal path planning for real-time navigation of UAVs, in International Work-Conference on Artificial Neural Networks. Springer (2017) 585–595. [Google Scholar]
  • Z. Ahmad, F. Ullah, C. Tran and S. Lee, Efficient energy flight path planning algorithm using 3-d visibility roadmap for small unmanned aerial vehicle. Int. J. Aerosp. Eng. 2017 (2017). DOI: 10.1155/2017/2849745. [CrossRef] [Google Scholar]
  • Airbus, Zephyr: the first stratospheric uas of its kind (2022). https://www.airbus.com/en/products-services/defence/uas/uas-solutions/zephyr. [Google Scholar]
  • A. Ait Saadi, A. Soukane, Y. Meraihi, A. Benmessaoud Gabis, S. Mirjalili and A. Ramdane-Cherif, UAV path planning using optimization approaches: a survey. Arch. Comput. Methods Eng. 29 (2022) 4233–4284. [CrossRef] [Google Scholar]
  • D. Alejo, J. Cobano, G. Heredia and A. Ollero, Particle swarm optimization for collision-free 4d trajectory planning in unmanned aerial vehicles, in 2013 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE (2013) 298–307. [Google Scholar]
  • K. Alexis, RotorS simulator. http://www.kostasalexis.com/rotors-simulator.html. [Google Scholar]
  • Z.A. Ali, H. Zhangang and W.B. Hang, Cooperative path planning of multiple UAVs by using max–min ant colony optimization along with cauchy mutant operator. Fluctuation Noise Lett. 20 (2021) 2150002. [CrossRef] [Google Scholar]
  • A. Alihodzic, Fireworks algorithm with new feasibility-rules in solving UAV path planning, in 2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI). IEEE (2016) 53–57. [Google Scholar]
  • A. Alihodzic, E. Tuba, R. Capor-Hrosik, E. Dolicanin and M. Tuba, Unmanned aerial vehicle path planning problem by adjusted elephant herding optimization, in 2017 25th Telecommunication Forum (TELFOR). IEEE (2017) 1–4. [Google Scholar]
  • R.E. Allen and M. Pavone, A real-time framework for kinodynamic planning in dynamic environments with application to quadrotor obstacle avoidance. Rob. Auton. Syst. 115 (2019) 174–193. [CrossRef] [Google Scholar]
  • I. Alzugaray, L. Teixeira and M. Chli, Short-term UAV path-planning with monocular-inertial slam in the loop, in 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE (2017) 2739–2746. [Google Scholar]
  • R. Amadeo, Loon’s bubble bursts – alphabet shuts down internet balloon company (2021). https://arstechnica.com/gadgets/2021/01/loons-bubble-bursts-alphabet-shuts-down-internet-balloon-company/. [Google Scholar]
  • Amazon, Amazon customers in Lockeford, California, will be among the first to receive Prime Air drone deliveries in the U.S. (2022). https://www.aboutamazon.com/news/transportation/amazon-prime-air-prepares-for-drone-deliveries. [Google Scholar]
  • M.D.S. Arantes, J.D.S. Arantes, C.F.M. Toledo and B.C. Williams, A hybrid multi-population genetic algorithm for UAV path planning, in Proceedings of the Genetic and Evolutionary Computation Conference 2016. ACM (2016) 853–860. [Google Scholar]
  • M. Arjomandi, S. Agostino, M. Mammone, M. Nelson and T. Zhou, Classification of unmanned aerial vehicles. Report for Mechanical Engineering class, University of Adelaide, Adelaide, Australia (2006). [Google Scholar]
  • A. Atyabi and D.M.W. Powers, Review of classical and heuristic-based navigation and path planning approaches. Int. J. Adv. Comput. Technol. (IJACT) 5 (2013). [Google Scholar]
  • Y. Bao, X. Fu and X. Gao, Path planning for reconnaissance UAV based on particle swarm optimization, in 2010 Second International Conference on Computational Intelligence and Natural Computing Proceedings (CINC). IEEE (2010) 28–32. [Google Scholar]
  • BBC, Facebook abandons its Project Aquila flying internet plan (2018). https://www.bbc.com/news/technology-44624702. [Google Scholar]
  • R.W. Beard, T.W. McLain, M.A. Goodrich and E.P. Anderson, Coordinated target assignment and intercept for unmanned air vehicles. IEEE Trans. Rob. Autom. 18 (2002) 911–922. [CrossRef] [Google Scholar]
  • A. Behjat, S. Paul and S. Chowdhury, Learning reciprocal actions for cooperative collision avoidance in quadrotor unmanned aerial vehicles. Rob. Auton. Syst. 121 (2019) 103270. [CrossRef] [Google Scholar]
  • L.P. Behnck, D. Doering, C.E. Pereira and A. Rettberg, A modified simulated annealing algorithm for SUAVs path planning. Ifac-Papersonline 48 (2015) 63–68. [CrossRef] [Google Scholar]
  • I. Bekmezci, O.K. Sahingoz and S¸. Temel, Flying ad-hoc networks (FANETs): a survey. Ad Hoc Netw. 11 (2013) 1254–1270. [CrossRef] [Google Scholar]
  • S. Benders and S. Schopferer, A line-graph path planner for performance constrained fixed-wing UAVs in wind fields, in 2017 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE (2017) 79–86. [Google Scholar]
  • J. Berndt, JSBSim: an open source flight dynamics model in C++, in AIAA Modeling and Simulation Technologies Conference and Exhibit. AIAA (2004) 4923. [Google Scholar]
  • E. Besada-Portas, L. de la Torre, M. Jesus and B. de Andrés-Toro, Evolutionary trajectory planner for multiple UAVs in realistic scenarios. IEEE Trans. Rob. 26 (2010) 619–634. [CrossRef] [Google Scholar]
  • E. Besada-Portas, L. De La Torre, A. Moreno and J.L. Risco-MartíN, On the performance comparison of multi-objective evolutionary UAV path planners. Inf. Sci. 238 (2013) 111–125. [CrossRef] [Google Scholar]
  • Best drone flight simulators(and drone games) of 2018. https://www.dronethusiast.com/drone-flight-simulator/. [Google Scholar]
  • J.T. Betts, Survey of numerical methods for trajectory optimization. J. Guidance Control Dyn. 21 (1998) 193–207. [CrossRef] [Google Scholar]
  • C. Blum, J. Puchinger, G.R. Raidl and A. Roli, Hybrid metaheuristics in combinatorial optimization: a survey. Appl. Soft Comput. 11 (2011) 4135–4151. [CrossRef] [Google Scholar]
  • S.A. Bortoff, Path planning for UAVs, in Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No. 00CH36334). IEEE (2000) 364–368. [Google Scholar]
  • I. Boussa¨ıd, J. Lepagnot and P. Siarry, A survey on optimization metaheuristics. Inf. Sci. 237 (2013) 82–117. [CrossRef] [Google Scholar]
  • R.A. Brooks and T. Lozano-Perez, A subdivision algorithm in configuration space for findpath with rotation. IEEE Trans. Syst. Man Cybern. 2 (1985) 224–233. [CrossRef] [Google Scholar]
  • I. Bulyko and M. Ostendorf, Joint prosody prediction and unit selection for concatenative speech synthesis, in 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No. 01CH37221). IEEE (2001) 781–784. [Google Scholar]
  • A. Bundy and L. Wallen, Breadth-first search, in Catalogue of Artificial Intelligence Tools. Springer (1984) 13. [Google Scholar]
  • M.N. Bygi and M. Ghodsi, 3D visibility graph. Computational Science and its Applications, Kuala Lampur (2007). [Google Scholar]
  • D. Cagigas, Hierarchical D* algorithm with materialization of costs for robot path planning. Rob. Auton. Syst. 52 (2005) 190–208. [CrossRef] [Google Scholar]
  • M. Cakir, 2D path planning of UAVs with genetic algorithm in a constrained environment, in 2015 6th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO). IEEE (2015) 1–5. [Google Scholar]
  • A. Calcara, A. Gilli, M. Gilli, R. Marchetti and I. Zaccagnini, Why drones have not revolutionized war: the enduring hider-finder competition in air warfare. Int. Secur. 46 (2022) 130–171. [CrossRef] [Google Scholar]
  • S.A. Cambone, K.J. Krieg, P. Pace and W. Linton, Unmanned aircraft systems roadmap 2005–2030. Office Secretary Defense 8 (2005) 4–15. [Google Scholar]
  • E. Capello, G. Guglieri and F.B. Quagliotti, UAVs and simulation: an experience on MAVs. Aircraft Eng. Aerospace Technol. 81 (2009) 38–50. [CrossRef] [Google Scholar]
  • A. Carrio, C. Sampedro, A. Rodriguez-Ramos and P. Campoy, A review of deep learning methods and applications for unmanned aerial vehicles. J. Sensors 2017 (2017). DOI: 10.1155/2017/3296874. [CrossRef] [Google Scholar]
  • F. Causa and G. Fasano, Navigation-aware path planning for multiple UAVs in urban environment, in 2020 AIAA/IEEE 39th Digital Avionics Systems Conference (DASC). IEEE (2020) 1–10. [Google Scholar]
  • U. Cekmez, M. Ozsiginan and O.K. Sahingoz, A UAV path planning with parallel ACO algorithm on CUDA platform, in 2014 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE (2014) 347–354. [Google Scholar]
  • U. Cekmez, M. Ozsiginan and O.K. Sahingoz, Multi-UAV path planning with parallel genetic algorithms on CUDA architecture, in Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion. ACM (2016) 1079–1086. [Google Scholar]
  • I. Chaari, A. Koubaa, H. Bennaceur, A. Ammar, M. Alajlan and H. Youssef, Design and performance analysis of global path planning techniques for autonomous mobile robots in grid environments. Int. J. Adv. Robotic Syst. 14 (2017) 1729881416663663. [Google Scholar]
  • H. Chang and T. Jin, Command fusion based fuzzy controller design for moving obstacle avoidance of mobile robot, in Future Information Communication Technology and Applications. Springer, Dordrecht (2013) 905–913. [Google Scholar]
  • I.M. Chao, B.L. Golden and E.A. Wasil, A fast and effective heuristic for the orienteering problem. Eur. J. Oper. Res. 88 (1996) 475–489. [CrossRef] [Google Scholar]
  • S.R. Chapala, G.S. Pirati and U.R. Nelakuditi, Determination of coordinate transformations in UAVs, in 2016 Second International Conference on Cognitive Computing and Information Processing (CCIP). IEEE (2016) 1–5. [Google Scholar]
  • X. Chen and J. Zhang, The three-dimension path planning of UAV based on improved artificial potential field in dynamic environment, in 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics. IEEE (2013) 144–147. [Google Scholar]
  • D.Z. Chen, R.J. Szczerba and J. Uhran, Planning conditional shortest paths through an unknown environment: a framed-quadtree approach, in Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots. IEEE (1995) 33–38. [Google Scholar]
  • Y. Chen, J. Han and X. Zhao, Three-dimensional path planning for unmanned aerial vehicle based on linear programming. Robotica 30 (2012) 773–781. [CrossRef] [Google Scholar]
  • Y. Chen, J. Yu, Y. Mei, Y. Wang and X. Su, Modified central force optimization (MCFO) algorithm for 3D UAV path planning. Neurocomputing 171 (2016) 878–888. [CrossRef] [Google Scholar]
  • T. Chen, G. Zhang, X. Hu and J. Xiao, Unmanned aerial vehicle route planning method based on a star algorithm, in 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE (2018) 1510–1514. [Google Scholar]
  • A.J. Chen, M.J. Sun, Z.H. Wang, N.Z. Feng and Y. Shen, Attitude trajectory tracking of quadrotor UAV using super-twisting observer-based adaptive controller. Proc. Inst. Mech. Eng. Part G: J. Aerosp. Eng. 235 (2021) 1146–1157. [CrossRef] [Google Scholar]
  • P. Cheng and S.M. LaValle, Resolution complete rapidly-exploring random trees, in Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No. 02CH37292). IEEE (2002) 267–272. [Google Scholar]
  • C.T. Cheng, K. Fallahi, H. Leung and K.T. Chi, Cooperative path planner for UAVs using ACO algorithm with gaussian distribution functions, in 2009 IEEE International Symposium on Circuits and Systems. IEEE (2009) 173–176. [Google Scholar]
  • Y. Cheng, D. Li, W.E. Wong, M. Zhao and D. Mo, Multi-UAV collaborative path planning using hierarchical reinforcement learning and simulated annealing. Int. J. Perform. Eng. 18 (2022) 463. [CrossRef] [Google Scholar]
  • Z. Chengjun and M. Xiuyun, Spare A* search approach for UAV route planning, in 2017 IEEE International Conference on Unmanned Systems (ICUS). IEEE (2017) 413–417. [Google Scholar]
  • S.Y. Choi and D. Cha, Unmanned aerial vehicles using machine learning for autonomous flight; state-of-the-art. Adv. Rob. 33 (2019) 265–277. [CrossRef] [Google Scholar]
  • D. Choi, K. Lee and D. Kim, Enhanced potential field-based collision avoidance for unmanned aerial vehicles in a dynamic environment, in AIAA Scitech 2020 Forum. American Institute of Aeronautics and Astronautics, Inc. (2020) 0487. [Google Scholar]
  • H. Cicibas, K.A. Demir and N. Arica, Comparison of 3D versus 4D path planning for unmanned aerial vehicles. Defence Sci. J. 66 (2016) 651–664. [CrossRef] [Google Scholar]
  • J. Cosgrove, Drones, manned aircraft now a regular staple in california firefighting (2018). https://www.govtech.com/dc/drones-manned-aircraft-now-a-regular-staple-in-california-firefighting.html. [Google Scholar]
  • Credits for supporting HELI-X. http://www.heli-x.info/cms/credits/. [Google Scholar]
  • C. Cui, N. Wang and J. Chen, Improved ant colony optimization algorithm for UAV path planning, in 2014 5th IEEE International Conference on Software Engineering and Service Science (ICSESS). IEEE (2014) 291–295. [Google Scholar]
  • N. Dadkhah and B. Mettler, Survey of motion planning literature in the presence of uncertainty: considerations for UAV guidance. J. Intell. Rob. Syst. 65 (2012) 233–246. [CrossRef] [Google Scholar]
  • J. Dai, Y. Wang, C. Wang, J. Ying and J. Zhai, Research on hierarchical potential field method of path planning for UAVs, in 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). IEEE (2018) 529–535. [Google Scholar]
  • H. Daryanavard and A. Harifi, UAV path planning for data gathering of IoT nodes: ant colony or simulated annealing optimization, in 2019 3rd International Conference on Internet of Things and Applications (IoT). IEEE (2019) 1–4. [Google Scholar]
  • E. Dasdemir, M. Köksalan and D.T. Öztürk, A flexible reference point-based multi-objective evolutionary algorithm: an application to the UAV route planning problem. Comput. Oper. Res. 114 (2020) 104811. [CrossRef] [MathSciNet] [Google Scholar]
  • A. Dashkevich, S. Rosokha and D. Vorontsova, Simulation tool for the drone trajectory planning based on genetic algorithm approach, in 2020 IEEE KhPI Week on Advanced Technology (KhPIWeek). IEEE (2020) 387–390. [Google Scholar]
  • L. De Filippis, G. Guglieri and F. Quagliotti, Path planning strategies for UAVs in 3D environments. J. Intell. Rob. Syst. 65 (2012) 247–264. [CrossRef] [Google Scholar]
  • S.K. Debnath, R. Omar and N.A. Latip, Comparison of different configuration space representations for path planning under combinatorial method. Indonesian J. Electr. Eng. Comput. Sci. 1 (2019) 401–408. [Google Scholar]
  • S.K. Debnath, R. Omar and N.B.A. Latip, A review on energy efficient path planning algorithms for unmanned air vehicles, in Computational Science and Technology. Springer, Singapore (2019) 523–532. [CrossRef] [Google Scholar]
  • S.K. Debnath, R. Omar, S. Bagchi, E.N. Sabudin, M.H.A.S. Kandar, K. Foysol and T.K. Chakraborty, Different cell decomposition path planning methods for unmanned air vehicles-a review, in Proceedings of the 11th National Technical Seminar on Unmanned System Technology 2019. Springer (2020) 99–111. [Google Scholar]
  • Defence Advanced Research Agency, DARPA’s 60th anniversary. https://www.darpa.mil/attachments/DARAPA60_publication-no-ads.pdf. [Google Scholar]
  • R. DeFrangesco and S. DeFrangesco, The Big Book of Drones. CRC Press (2022). [CrossRef] [Google Scholar]
  • R. Deits and R. Tedrake, Efficient mixed-integer planning for UAVs in cluttered environments, in 2015 IEEE International Conference on Robotics and Automation (ICRA). IEEE (2015) 42–49. [Google Scholar]
  • C. Di Franco and G. Buttazzo, Energy-aware coverage path planning of UAVs, in 2015 IEEE International Conference on Autonomous Robot Systems and Competitions. IEEE (2015) 111–117. [Google Scholar]
  • E.W. Dijkstra, A note on two problems in connexion with graphs. Numer. Math. 1 (1959) 269–271. [Google Scholar]
  • Z. Dong, Z. Chen, R. Zhou and R. Zhang, A hybrid approach of virtual force and A* search algorithm for UAV path re-planning, in 2011 6th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE (2011) 1140–1145. [Google Scholar]
  • M. Dorigo, M. Birattari and T. Stutzle, Ant colony optimization. IEEE Comput. Intell. Mag. 1 (2006) 28–39. [CrossRef] [Google Scholar]
  • H.B. Duan, X.Y. Zhang, J. Wu and G.J. Ma, Max–min adaptive ant colony optimization approach to multi-UAVs coordinated trajectory replanning in dynamic and uncertain environments. J. Bionic Eng. 6 (2009) 161–173. [CrossRef] [Google Scholar]
  • H. Duan, Y. Yu, X. Zhang and S. Shao, Three-dimension path planning for UCAV using hybrid meta-heuristic ACO-DE algorithm. Simul. Model. Pract. Theory 18 (2010) 1104–1115. [CrossRef] [Google Scholar]
  • M. Elbanhawi and M. Simic, Sampling-based robot motion planning: a review. IEEE Access 2 (2014) 56–77. [CrossRef] [Google Scholar]
  • A. Elfes, Using occupancy grids for mobile robot perception and navigation. Computer 22 (1989) 46–57. [CrossRef] [Google Scholar]
  • C. Ellis, The best free drone simulator 2018 – TechRadar. https://www.techradar.com/news/the-best-free-drone-simulator. [Google Scholar]
  • X. Fan, Y. Guo, H. Liu, B. Wei and W. Lyu, Improved artificial potential field method applied for AUV path planning. Math. Prob. Eng. 2020 (2020) 1–21. [Google Scholar]
  • D. Ferguson and A. Stentz, Using interpolation to improve path planning: the field D* algorithm. J. Field Rob. 23 (2006) 79–101. [CrossRef] [Google Scholar]
  • M. Flint, M. Polycarpou and E. Fernandez-Gaucherand, Cooperative path-planning for autonomous vehicles using dynamic programming. IFAC Proc. Vol. 35 (2002) 481–486. [CrossRef] [Google Scholar]
  • J.L. Foo, J. Knutzon, J. Oliver and E. Winer, Three-dimensional multi-objective path planner for unmanned aerial vehicles using particle swarm optimization, in 48th AIAA ASME ASCE AHS ASC Structures, Structural Dynamics, and Materials Conference. AIAA (2007) 1881. [Google Scholar]
  • P.L. Frana and T.J. Misa, An interview with Edsger W. Dijkstra. Commun. ACM 53 (2010) 41–47. [CrossRef] [Google Scholar]
  • M. Freese, S. Singh, F. Ozaki and N. Matsuhira, Virtual robot experimentation platform V-REP: a versatile 3D robot simulator, in International Conference on Simulation, Modeling, and Programming for Autonomous Robots. Springer (2010) 51–62. [Google Scholar]
  • FSD, Drones in humanitarian action. Tech. report. Fondation Suisse de Déminage (FSD) (2016). [Google Scholar]
  • Y. Fu, M. Ding and C. Zhou, Phase angle-encoded and quantum-behaved particle swarm optimization applied to three-dimensional route planning for UAV. IEEE Trans. Syst. Man Cybern. Part A: Syst. Humans 42 (2012) 511–526. [CrossRef] [Google Scholar]
  • A. Fügenschuh, Aspects of time in mixed-integer (non-)linear optimization. Professur für Angewandte Mathematik, Helmut-Schmidt-Universit¨at Hamburg (2015). [Google Scholar]
  • F. Furrer, M. Burri, M. Achtelik and R. Siegwart, Rotors – a modular Gazebo MAV simulator framework, in Robot Operating System (ROS). Springer, Cham (2016) 595–625. [CrossRef] [Google Scholar]
  • E. Galceran and M. Carreras, A survey on coverage path planning for robotics. Rob. Auton. Syst. 61 (2013) 1258–1276. [CrossRef] [Google Scholar]
  • F. Gao and S. Shen, Online quadrotor trajectory generation and autonomous navigation on point clouds, in 2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR). IEEE (2016) 139–146. [Google Scholar]
  • J. Garcia-Bernardo, P.S. Dodds and N.F. Johnson, Quantitative patterns in drone wars. Phys. A: Stat. Mech. App. 443 (2016) 380–384. [CrossRef] [Google Scholar]
  • A. Gasparetto, P. Boscariol, A. Lanzutti and R. Vidoni, Path planning and trajectory planning algorithms: a general overview, in Motion and Operation Planning of Robotic Systems. Springer, Cham (2015) 3–27. [CrossRef] [Google Scholar]
  • S.A. Gautam and N. Verma, Path planning for unmanned aerial vehicle based on genetic algorithm & artificial neural network in 3D, in 2014 International Conference on Data Mining and Intelligent Computing (ICDMIC). IEEE (2014) 1–5. [Google Scholar]
  • S. Ghambari, J. Lepagnot, L. Jourdan and L. Idoumghar, A comparative study of meta-heuristic algorithms for solving UAV path planning, in 2018 IEEE Symposium Series on Computational Intelligence (SSCI). (2018) 174–181. [Google Scholar]
  • S. Ghambari, L. Idoumghar, L. Jourdan and J. Lepagnot, An improved TLBO algorithm for solving UAV path planning problem, in 2019 IEEE Symposium Series on Computational Intelligence (SSCI) (2019) 2261–2268. [Google Scholar]
  • S. Ghambari, L. Idoumghar, L. Jourdan and J. Lepagnot, A hybrid evolutionary algorithm for offline UAV path planning, in International Conference on Artificial Evolution (Evolution Artificielle). Springer (2019) 205–218. [Google Scholar]
  • S. Ghoshroy, The X-37B: Backdoor weaponization of space? Bull. At. Sci. 71 (2015) 19–29. [CrossRef] [Google Scholar]
  • A.R. Giard, S. Dharba, M. Pachter and P.R. Chandler, Stochastic dynamic rogramming for uncertainty handling in UAV operations, in 2007 American Control Conference ACC’07. IEEE (2007) 1079–1084. [CrossRef] [Google Scholar]
  • J. Giesbrecht, Global path planning for unmanned ground vehicles. Tech. report. Defence Research and Development Suffield (ALBERTA) (2004). [Google Scholar]
  • D. Glavaški, M. Volf and M. Bonkovic, Robot motion planning using exact cell decomposition and potential field methods, in Proceedings of the 9th WSEAS international conference on Simulation, modelling and optimization. World Scientific and Engineering Academy and Society (WSEAS) (2009) 126–131. [Google Scholar]
  • U. Goel, S. Varshney, A. Jain, S. Maheshwari and A. Shukla, Three dimensional path planning for UAVs in dynamic environment using glow-worm swarm optimization. Proc. Comput. Sci. 133 (2018) 230–239. [CrossRef] [Google Scholar]
  • C. Goerzen, Z. Kong and B. Mettler, A survey of motion planning algorithms from the perspective of autonomous UAV guidance. J. Intell. Rob. Syst. 57 (2010) 65. [CrossRef] [Google Scholar]
  • M. Golabi, S. Ghambari, J. Lepagnot, L. Jourdan, M. Brévilliers and L. Idoumghar, Bypassing or flying above the obstacles? A novel multi-objective UAV path planning problem, in 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE (2020) 1–8. [Google Scholar]
  • D.E. Goldberg and J.H. Holland, Genetic algorithms and machine learning. Mach. Learn. 3 (1988) 95–99. [CrossRef] [Google Scholar]
  • D. González, J. Pérez, V. Milanés and F. Nashashibi, A review of motion planning techniques for automated vehicles. IEEE Trans. Intell. Transp. Syst. 17 (2015) 1135–1145. [Google Scholar]
  • Y. Guan, M. Gao and Y. Bai, Double-ant colony based UAV path planning algorithm, in Proceedings of the 2019 11th International Conference on Machine Learning and Computing. ACM (2019) 258–262. [Google Scholar]
  • J.A. Guerrero, J.A. Escareño and Y. Bestaoui, Quad-rotor MAV trajectory planning in wind fields, in 2013 IEEE International Conference on Robotics and Automation. IEEE (2013) 778–783. [Google Scholar]
  • J.S. Gutmann, M. Fukuchi and M. Fujita, A floor and obstacle height map for 3D navigation of a humanoid robot, in Proceedings of the 2005 IEEE International Conference on Robotics and Automation. IEEE (2005) 1066–1071. [Google Scholar]
  • P.E. Hart, N.J. Nilsson and B. Raphael, A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. 4 (1968) 100–107. [CrossRef] [Google Scholar]
  • M. Hassanalian and A. Abdelkefi, Classifications, applications, and design challenges of drones: a review. Prog. Aerosp. Sci. 91 (2017) 99–131. [CrossRef] [Google Scholar]
  • S. Hayat, E. Yanmaz, T.X. Brown and C. Bettstetter, Multi-objective UAV path planning for search and rescue, in 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE (2017) 5569–5574. [Google Scholar]
  • Y. He, Q. Zeng, J. Liu, G. Xu and X. Deng, Path planning for indoor UAV based on ant colony optimization, in 2013 25th Chinese Control and Decision Conference (CCDC). IEEE (2013) 2919–2923. [Google Scholar]
  • T. Hebecker, R. Buchholz and F. Ortmeier, Model-based local path planning for UAVs. J. Intell. Rob. Syst. 78 (2015) 127–142. [CrossRef] [Google Scholar]
  • H. Heidari and M. Saska, Collision-free trajectory planning of multi-rotor UAVs in a wind condition based on modified potential field. Mech. Mach. Theory 156 (2021) 104140. [CrossRef] [Google Scholar]
  • S. Hrabar, 3D path planning and stereo-based obstacle avoidance for rotorcraft UAVs, in 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE (2008) 807–814. [Google Scholar]
  • T.W. Hsu and J.S. Liu, Design of smooth path based on the conversion between η 3 spline and bezier curve, in 2020 American Control Conference (ACC). IEEE (2020) 3230–3235. [Google Scholar]
  • C. Huang and J. Fei, UAV path planning based on particle swarm optimization with global best path competition. Int. J. Pattern Recognit. Artif. Intell. 32 (2018) 1859008. [CrossRef] [MathSciNet] [Google Scholar]
  • H. Huang, J. Deng, Y. Lan, A. Yang, X. Deng and L. Zhang, A fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery. PloS One 13 (2018) e0196302. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  • L. Huo, J. Zhu, G. Wu and Z. Li, A novel simulated annealing based strategy for balanced UAV task assignment and path planning. Sensors 20 (2020) 4769. [CrossRef] [PubMed] [Google Scholar]
  • Y.K. Hwang and N. Ahuja, Gross motion planning – a survey. ACM Comput. Surv. (CSUR) 24 (1992) 219–291. [CrossRef] [Google Scholar]
  • D. Ibrahim, An overview of soft computing. Proc. Comput. Sci. 102 (2016) 34–38. [CrossRef] [Google Scholar]
  • B.T. Ingersoll, J.K. Ingersoll, P. DeFranco and A. Ning, UAV path-planning using Bezier curves and a receding horizon approach, in AIAA Modeling and Simulation Technologies Conference. American Institute of Aeronautics and Astronautics, Inc. (2016) 3675. [Google Scholar]
  • Introduction FlightGear flight simulator. http://home.flightgear.org/about/. [Google Scholar]
  • I. Iswanto, O. Wahyunggoro and A.I. Cahyadi, Quadrotor path planning based on modified fuzzy cell decomposition algorithm. TELKOMNIKA (Telecommun. Comput. Electron. Control) 14 (2016) 655–664. [CrossRef] [Google Scholar]
  • V. Jamshidi, V. Nekoukar and M.H. Refan, Analysis of parallel genetic algorithm and parallel particle swarm optimization algorithm UAV path planning on controller area network. J. Control Autom. Electr. Syst. 31 (2020) 129–140. [CrossRef] [Google Scholar]
  • Y. Jang, Y. Lee and H.J. Kim, Navigation-assistant path planning within a MAV team, in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE (2020) 1436–1443. [Google Scholar]
  • P.T. Jardine, S. Givigi and S. Yousefi, Parameter tuning for prediction-based quadcopter trajectory planning using learning automata. IFAC-PapersOnLine 50 (2017) 2341–2346. [CrossRef] [Google Scholar]
  • A.L. Jennings, R. Ordonez and N. Ceccarelli, Dynamic programming applied to UAV way point path planning in wind, in 2008 IEEE International Conference on Computer-Aided Control Systems. IEEE (2008) 215–220. [Google Scholar]
  • W. Jing, D. Deng, Y. Wu and K. Shimada, Multi-UAV coverage path planning for the inspection of large and complex structures, in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE (2020) 1480–1486. [Google Scholar]
  • M. Jones, S. Djahel and K. Welsh, Path-planning for unmanned aerial vehicles with environment complexity considerations: a survey. ACM Comput. Surv. 55 (2023) 1–39. [CrossRef] [Google Scholar]
  • A. Jorge, L. Torgo, P. Brazdil, R. Camacho and J. Gama, Knowledge Discovery in Databases: PKDD 2005: 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, October 3–7, 2005, Proceedings. Vol. 3721. Springer Porto, Portugal (2005). [Google Scholar]
  • J.L. Junell, E.J. Van Kampen, C.C. de Visser and Q.P. Chu, Reinforcement learning applied to a quadrotor guidance law in autonomous flight, in AIAA Guidance, Navigation, and Control Conference. American Institute of Aeronautics and Astronautics, Inc. (2015) 1990. [Google Scholar]
  • G. Kahn, A. Villaflor, V. Pong, P. Abbeel and S. Levine, Uncertainty-aware reinforcement learning for collision avoidance. Preprint arXiv:1702.01182 (2017). [Google Scholar]
  • F. Kamrani and R. Ayani, Using on-line simulation for adaptive path planning of UAVs, in 11th IEEE International Symposium on Distributed Simulation and Real-Time Applications (DS-RT’07). IEEE (2007) 167–174. [Google Scholar]
  • E.M. Kan, M.H. Lim, Y.S. Ong, A.H. Tan and S.P. Yeo, Extreme learning machine terrain-based navigation for unmanned aerial vehicles. Neural Comput. App. 22 (2013) 469–477. [CrossRef] [Google Scholar]
  • S. Karaman and E. Frazzoli, Sampling-based algorithms for optimal motion planning. Int. J. Rob. Res. 30 (2011) 846–894. [CrossRef] [Google Scholar]
  • L.E. Kavraki, P. Svestka, J.C. Latombe and M.H. Overmars, Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Trans. Rob. Autom. 12 (1996) 566–580. [CrossRef] [Google Scholar]
  • L.E. Kavraki, J.C. Latombe, R. Motwani and P. Raghavan, Randomized query processing in robot path planning. J. Comput. Syst. Sci. 57 (1998) 50–60. [CrossRef] [Google Scholar]
  • E. Kayacan and R. Maslim, Type-2 fuzzy logic trajectory tracking control of quadrotor VTOL aircraft with elliptic membership functions. IEEE/ASME Trans. Mech. 22 (2017) 339–348. [CrossRef] [Google Scholar]
  • J. Kennedy and R. Eberhart, Particle swarm optimization, in Proceedings of ICNN’95-International Conference on Neural Networks. IEEE (1995) 1942–1948. [Google Scholar]
  • P. Kermani and A.A. Afzalian, Flight path planning using ga and fuzzy logic considering communication constraints, in 2014 7th International Symposium on Telecommunications (IST). IEEE (2014) 6–11. [Google Scholar]
  • H. Kesteloo, Thermal drone finds missing woman in seconds (2021). https://dronexl.co/2021/11/14/thermal-drone-finds-missing-woman/. [Google Scholar]
  • O. Khatib, Real-time obstacle avoidance for manipulators and mobile robots, in Autonomous Robot Vehicles. Springer, Stanford, CA (1986) 396–404. [Google Scholar]
  • F. Kiani, A. Seyyedabbasi, R. Aliyev, M.U. Gulle, H. Basyildiz and M.A. Shah, Adapted-RRT: novel hybrid method to solve three-dimensional path planning problem using sampling and metaheuristic-based algorithms. Neural Comput. App. 33 (2021) 15569–15599. [CrossRef] [Google Scholar]
  • I. Kim, S. Shin, J. Wu, S.D. Kim and C.G. Kim, Obstacle avoidance path planning for UAV using reinforcement learning under simulated environment, in IASER 3rd International Conference on Electronics, Electrical Engineering, Computer Science, Okinawa (2017) 34–36. [Google Scholar]
  • E. King, Y. Kuwata and J.P. How, Experimental demonstration of coordinated control for multi-vehicle teams. Int. J. Syst. Sci. 37 (2006) 385–398. [CrossRef] [Google Scholar]
  • M. Kloetzer, C. Mahulea and R. Gonzalez, Optimizing cell decomposition path planning for mobile robots using different metrics, in 2015 19th International Conference on System Theory, Control and Computing (ICSTCC). IEEE (2015) 565–570. [Google Scholar]
  • S. Koenig, C. Tovey and Y. Smirnov, Performance bounds for planning in unknown terrain. Artif. Intell. 147 (2003) 253–279. [CrossRef] [Google Scholar]
  • J. Kok and J. Chahl, A low-cost simulation platform for flapping wing MAVs, in Bioinspiration, Biomimetics, and Bioreplication 2015. International Society for Optics and Photonics (2015) 94290L. [Google Scholar]
  • S. Konatowski and P. Paw lowski, Application of the ACO algorithm for UAV path planning. Przeglad Elektrotechniczny 95 (2019) 115–118. [Google Scholar]
  • M.H. Korayem, M. Nazemizadeh and H.R. Nohooji, Optimal point-to-point motion planning of non-holonomic mobile robots in the presence of multiple obstacles. J. Brazilian Soc. Mech. Sci. Eng. 36 (2014) 221–232. [CrossRef] [Google Scholar]
  • S. Kurnaz, O. Cetin and O. Kaynak, Adaptive neuro-fuzzy inference system based autonomous flight control of unmanned air vehicles. Expert Syst. App. 37 (2010) 1229–1234. [CrossRef] [Google Scholar]
  • Y. Kuwata and J. How, Three dimensional receding horizon control for UAVs, in AIAA Guidance, Navigation, and Control Conference and Exhibit. American Institute of Aeronautics and Astronautics, Inc. (2004) 5144. [Google Scholar]
  • J. Kwak and Y. Sung, Autonomous UAV flight control for GPS-based navigation. IEEE Access 6 (2018) 37947–37955. [CrossRef] [Google Scholar]
  • W.G. La, S. Park and H. Kim, D-MUNS: Distributed multiple UAVs’ network simulator, in 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN). IEEE (2017) 15–17. [Google Scholar]
  • N.B.A. Latip, R. Omar and S.K. Debnath, Optimal path planning using equilateral spaces oriented visibility graph method. Int. J. Electr. Comput. Eng. (IJECE) 7 (2017) 3046–3051. [CrossRef] [Google Scholar]
  • J.C. Latombe, Exact cell decomposition, in Robot Motion Planning. Springer, Boston, MA (1991) 200–247. [CrossRef] [Google Scholar]
  • J.C. Latombe, Robot Motion Planning. Vol. 124. Springer Science & Business Media Stanford (2012). [Google Scholar]
  • S.M. LaValle, Planning Algorithms. Cambridge University Press (2006). [CrossRef] [Google Scholar]
  • S.M. LaValle, Rapidly-Exploring Random Trees: A New Tool for Path Planning. Citeseer (1998). [Google Scholar]
  • D. Lee and D.H. Shim, RRT-based path planning for fixed-wing UAVs with arrival time and approach direction constraints, in 2014 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE (2014) 317–328. [Google Scholar]
  • G. Lei, M.Z. Dong, T. Xu and L. Wang, Multi-agent path planning for unmanned aerial vehicle based on threats analysis, in 2011 3rd International Workshop on Intelligent Systems and Applications. IEEE (2011) 1–4. [Google Scholar]
  • G. Li, A. Yamashita, H. Asama and Y. Tamura, An efficient improved artificial potential field based regression search method for robot path planning, in 2012 IEEE International Conference on Mechatronics and Automation. IEEE (2012) 1227–1232. [Google Scholar]
  • P. Li and H. Duan, Path planning of unmanned aerial vehicle based on improved gravitational search algorithm. Sci. Chin. Technol. Sci. 55 (2012) 2712–2719. [CrossRef] [Google Scholar]
  • Y. Li, H. Chen, M.J. Er and X. Wang, Coverage path planning for UAVs based on enhanced exact cellular decomposition method. Mechatronics 21 (2011) 876–885. [CrossRef] [Google Scholar]
  • Y. Li, W. Wei, Y. Gao, D. Wang and Z. Fan, PQ-RRT*: An improved path planning algorithm for mobile robots. Expert Syst. App. 152 (2020) 113425. [CrossRef] [Google Scholar]
  • S.R. Lindemann and S.M. LaValle, Current issues in sampling-based motion planning, in Robotics Research. The Eleventh International Symposium. Springer (2005) 36–54. [Google Scholar]
  • Y. Liu and Y. Zhao, A virtual-waypoint based artificial potential field method for UAV path planning, in 2016 IEEE Chinese Guidance, Navigation and Control Conference (CGNCC). IEEE (2016) 949–953. [Google Scholar]
  • A. Loquercio, A.I. Maqueda, C.R. Del-Blanco and D. Scaramuzza, Dronet: learning to fly by driving. IEEE Rob. Autom. Lett. 3 (2018) 1088–1095. [CrossRef] [Google Scholar]
  • Z. Ma, J. Hu, Y. Niu and H. Yu, Reactive obstacle avoidance method for a UAV, in Deep Learning for Unmanned Systems. Springer, (2021) 83–108. [CrossRef] [Google Scholar]
  • T.T. Mac, C. Copot, A. Hernandez and R. De Keyser, Improved potential field method for unknown obstacle avoidance using UAV in indoor environment, in 2016 IEEE 14th International Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE (2016) 345–350. [Google Scholar]
  • D.G. Macharet, A.A. Neto and M.F.M. Campos, Feasible UAV path planning using genetic algorithms and bézier curves, in Brazilian Symposium on Artificial Intelligence. Springer (2010) 223–232. [Google Scholar]
  • E. Magid, R. Lavrenov and I. Afanasyev, Voronoi-based trajectory optimization for UGV path planning, in 2017 International Conference on Mechanical, System and Control Engineering (ICMSC). IEEE (2017) 383–387. [Google Scholar]
  • A. Mairaj, A.I. Baba and A.Y. Javaid, Application specific drone simulators: recent advances and challenges. Simul. Modell. Pract. Theory 94 (2019) 100–117. [CrossRef] [Google Scholar]
  • A.L. Majdik, Y. Albers-Schoenberg and D. Scaramuzza, MAV urban localization from google street view data, in 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE (2013) 3979–3986. [Google Scholar]
  • E. Masehian and M. Amin-Naseri, A voronoi diagram-visibility graph-potential field compound algorithm for robot path planning. J. Rob. Syst. 21 (2004) 275–300. [CrossRef] [Google Scholar]
  • F. Matoui, B. Boussaid and M.N. Abdelkrim, Distributed path planning of a multi-robot system based on the neighborhood artificial potential field approach. Simulation 95 (2019) 637–657. [CrossRef] [Google Scholar]
  • P. Mátyás and N. Máté, Brief history of UAV development. Repüléstudományi Közlemények 31 (2019) 155–166. [Google Scholar]
  • T. McLain and R. Beard, Trajectory planning for coordinated rendezvous of unmanned air vehicles, in AIAA Guidance, navigation, and control conference and exhibit. American Institute of Aeronautics and Astronautics, Inc. (2000) 4369. [Google Scholar]
  • M. McNabb, Amazon drone delivery: a brief history of the patents, problems and progress from the dronelife archives (2022). https://dronelife.com/2022/06/20/amazon-drone-delivery-a-brief-history-of-the-patents-problems-and-progress-from-the-dronelife-archives/. [Google Scholar]
  • F.L.L. Medeiros and J.D.S. Da Silva, A Dijkstra algorithm for fixed-wing UAV motion planning based on terrain elevation, in Brazilian Symposium on Artificial Intelligence. Springer (2010) 213–222. [Google Scholar]
  • A.A. Meera, M. Popović, A. Millane and R. Siegwart, Obstacle-aware adaptive informative path planning for UAV-based target search, in 2019 International Conference on Robotics and Automation (ICRA). IEEE (2019) 718–724. [Google Scholar]
  • R.A. Meyers, Encyclopedia of Complexity and Systems Science. Springer New York (2009). [Google Scholar]
  • Microdrones, Drones in construction: Watch this 3 minute case study (2022). https://www.microdrones.com/en/content/drones-in-construction-watch-this-3-minute-case-study/. [Google Scholar]
  • S. Mittal and K. Deb, Three-dimensional offline path planning for UAVs using multiobjective evolutionary algorithms, in 2007 IEEE Congress on Evolutionary Computation. IEEE (2007) 3195–3202. [Google Scholar]
  • J. Modares, F. Ghanei, N. Mastronarde and K. Dantu, UB-ANC planner: energy efficient coverage path planning with multiple drones, in 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE (2017) 6182–6189. [Google Scholar]
  • J. Mou, T. Hu, P. Chen and L. Chen, Cooperative mass path planning for marine man overboard search. Ocean Eng. 235 (2021) 109376. [CrossRef] [Google Scholar]
  • V. Narayanan, M. Phillips and M. Likhachev, Anytime safe interval path planning for dynamic environments, in 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE (2012) 4708–4715. [Google Scholar]
  • M. Needham and A.E. Hodler, Graph Algorithms: Practical Examples in Apache Spark and NEO4J. O’Reilly Media Sebastopol, CA, USA (2019). [Google Scholar]
  • A.A.R. Newaz, F.A. Pratama and N.Y. Chong, Exploration priority based heuristic approach to UAV path planning, in 2013 IEEE RO-MAN. IEEE (2013) 521–526. [Google Scholar]
  • L.R. Newcome, Unmanned Aviation: A Brief History of Unmanned Aerial Vehicles. American Institute of Aeronautics and Astronautics Reston, VA, USA (2004). [CrossRef] [Google Scholar]
  • I.K. Nikolos and A.N. Brintaki, Coordinated UAV path planning using differential evolution, in Proceedings of the 2005 IEEE International Symposium on, Mediterrean Conference on Control and Automation Intelligent Control. IEEE (2005) 549–556. [Google Scholar]
  • I.K. Nikolos, K.P. Valavanis, N.C. Tsourveloudis and A.N. Kostaras, Evolutionary algorithm based offline/online path planner for UAV navigation. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 33 (2003) 898–912. [CrossRef] [PubMed] [Google Scholar]
  • H. Niu, A. Savvaris, A. Tsourdos and Z. Ji, Voronoi-visibility roadmap-based path planning algorithm for unmanned surface vehicles. J. Navig. 72 (2019) 850–874. [CrossRef] [Google Scholar]
  • K. Nonami, F. Kendoul, S. Suzuki, W. Wang and D. Nakazawa, Autonomous Flying Robots: Unmanned Aerial Vehicles and Micro Aerial Vehicles. Springer Science & Business Media Japan (2010). [Google Scholar]
  • O. Nowers, D.J. Duxbury, J. Zhang and B.W. Drinkwater, Novel ray-tracing algorithms in NDE: application of Dijkstra and A* algorithms to the inspection of an anisotropic weld. NDT & E Int. 61 (2014) 58–66. [CrossRef] [Google Scholar]
  • U. Orozco-Rosas, O. Montiel and R. Sepúlveda, Mobile robot path planning using membrane evolutionary artificial potential field. Appl. Soft Comput. 77 (2019) 236–251. [CrossRef] [Google Scholar]
  • A. Otto, N. Agatz, J. Campbell, B. Golden and E. Pesch, Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: a survey. Networks 72 (2018) 411–458. [CrossRef] [MathSciNet] [Google Scholar]
  • R.P. Padhy, S. Verma, S. Ahmad, S.K. Choudhury and P.K. Sa, Deep neural network for autonomous UAV navigation in indoor corridor environments. Proc. Comput. Sci. 133 (2018) 643–650. [CrossRef] [Google Scholar]
  • J. Pan and D. Manocha, Efficient configuration space construction and optimization for motion planning. Engineering 1 (2015) 046–057. [CrossRef] [Google Scholar]
  • L. Paulino, C. Hannum, A.S. Varde and C.J. Conti, Search methods in motion planning for mobile robots, in Intelligent Systems and Applications, edited by K. Arai. Springer International Publishing (2022) 802–822. [CrossRef] [Google Scholar]
  • Y.V. Pehlivanoğlu, A new particle swarm optimization method for the path planning of UAV in 3D environment. J. Aeron. Space Technol. 5 (2012) 1–14. [Google Scholar]
  • M. Pérez-Ortiz, J.M. Peña, P.A. Gutiérrez, J. Torres-Sánchez, C. Hervás-Martínez and F. López-Granados, Selecting patterns and features for between-and within-crop-row weed mapping using UAV-imagery. Expert Syst. App. 47 (2016) 85–94. [CrossRef] [Google Scholar]
  • A.R. Perry, The flightgear flight simulator, in Proceedings of the USENIX Annual Technical Conference. Vol. 686. USENIX (2004) 1–12. [Google Scholar]
  • P.O. Pettersson and P. Doherty, Probabilistic roadmap based path planning for an autonomous unmanned aerial vehicle, in Proc. of the ICAPS-04 Workshop on Connecting Planning Theory with Practice. American Association for Artificial Intelligence (2004). [Google Scholar]
  • P.O. Pettersson and P. Doherty, Probabilistic roadmap based path planning for an autonomous unmanned helicopter. J. Intell. Fuzzy Syst. 17 (2006) 395–405. [Google Scholar]
  • A. Plioutsias, N. Karanikas and M.M. Chatzimihailidou, Hazard analysis and safety requirements for small drone operations: to what extent do popular drones embed safety? Risk Anal. 38 (2018) 562–584. [CrossRef] [PubMed] [Google Scholar]
  • M. Popović, G. Hitz, J. Nieto, I. Sa, R. Siegwart and E. Galceran, Online informative path planning for active classification using UAVs, in 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE (2017) 5753–5758. [Google Scholar]
  • S. Poudel and S. Moh, Hybrid path planning for efficient data collection in UAV-aided WSNS for emergency applications. Sensors 21 (2021) 2839. [CrossRef] [PubMed] [Google Scholar]
  • S. Primatesta, M. Scanavino, G. Guglieri and A. Rizzo, A risk-based path planning strategy to compute optimum risk path for unmanned aircraft systems over populated areas, in 2020 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE (2020) 641–650. [Google Scholar]
  • A. Pruitt, Hone your drone piloting skills with the Zephyr simulator – TechRepublic. https://tinyurl.com/y8g4byaf. [Google Scholar]
  • A. Puente-Castro, D. Rivero, A. Pazos and E. Fernandez-Blanco, A review of artificial intelligence applied to path planning in UAV swarms. Neural Comput. App. 34 (2022) 153–170. [CrossRef] [Google Scholar]
  • Z. Qadir, F. Ullah, H.S. Munawar and F. Al-Turjman, Addressing disasters in smart cities through UAVs path planning and 5G communications: a systematic review. Comput. Commun. 168 (2021) 114–135. [CrossRef] [Google Scholar]
  • Z. Qi, Z. Shao, Y.S. Ping, L.M. Hiot and Y.K. Leong, An improved heuristic algorithm for UAV path planning in 3D environment, in 2010 Second International Conference on Intelligent Human-Machine Systems and Cybernetics. IEEE (2010) 258–261. [Google Scholar]
  • H. Qiu and H. Duan, A multi-objective pigeon-inspired optimization approach to UAV distributed flocking among obstacles. Inf. Sci. 509 (2020) 515–529. [CrossRef] [Google Scholar]
  • C. Qu, W. Gai, J. Zhang and M. Zhong, A novel hybrid grey wolf optimizer algorithm for unmanned aerial vehicle (UAV) path planning. Knowl.-Based Syst. 194 (2020) 105530. [CrossRef] [Google Scholar]
  • L. Quan, L. Han, B. Zhou, S. Shen and F. Gao, Survey of UAV motion planning. IET Cyber-Syst. Rob. 2 (2020) 14–21. [CrossRef] [Google Scholar]
  • M. Quigley, K. Conley, B. Gerkey, J. Faust, T. Foote, J. Leibs, R. Wheeler and A.Y. Ng, ROS: an open-source robot operating system, in ICRA Workshop on Open Source Software. Kobe, Japan (2009) 5. [Google Scholar]
  • Y. Quiñonez, F. Barrera, I. Bugueño and J. Bekios-Calfa, Simulation and path planning for quadcopter obstacle avoidance in indoor environments using the ROS framework, in International Conference on Software Process Improvement. Springer (2017) 295–304. [Google Scholar]
  • A.H. Qureshi and Y. Ayaz, Intelligent bidirectional rapidly-exploring random trees for optimal motion planning in complex cluttered environments. Rob. Auton. Syst. 68 (2015) 1–11. [CrossRef] [Google Scholar]
  • M. Radmanesh and M. Kumar, Grey wolf optimization based sense and avoid algorithm for UAV path planning in uncertain environment using a Bayesian framework, in 2016 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE (2016) 68–76. [Google Scholar]
  • M. Radmanesh, M. Kumar, P.H. Guentert and M. Sarim, Overview of path-planning and obstacle avoidance algorithms for UAVs: a comparative study. Unmanned Syst. 6 (2018) 95–118. [CrossRef] [Google Scholar]
  • P.L. Raeva, J. Šedina and A. Dlesk, Monitoring of crop fields using multispectral and thermal imagery from UAV. Eur. J. Remote Sens. 52 (2019) 192–201. [CrossRef] [Google Scholar]
  • C. Ramirez-Atencia, G. Bello-Orgaz, M.D. R-Moreno and D. Camacho, Solving complex multi-UAV mission planning problems using multi-objective genetic algorithms. Soft Comput. 21 (2017) 4883–4900. [CrossRef] [Google Scholar]
  • Real drone simulator. http://www.realdronesimulator.com/. [Google Scholar]
  • A. Rejeb, K. Rejeb, S. Simske and H. Treiblmaier, Humanitarian drones: a review and research agenda. Internet Things 16 (2021) 100434. [CrossRef] [Google Scholar]
  • Q. Ren, Y. Yao, G. Yang and X. Zhou, Multi-objective path planning for UAV in the urban environment based on CDNSGA-II, in 2019 IEEE International Conference on Service-Oriented System Engineering (SOSE). IEEE (2019) 350–3505. [Google Scholar]
  • V. Roberge, M. Tarbouchi and G. Labonté, Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning. IEEE Trans. Ind. Inf. 9 (2012) 132–141. [Google Scholar]
  • V. Roberge, M. Tarbouchi and G. Labonté, Fast genetic algorithm path planner for fixed-wing military UAV using GPU. IEEE Trans. Aerosp. Electron. Syst. 54 (2018) 2105–2117. [CrossRef] [Google Scholar]
  • S.J. Russell, Artificial Intelligence a Modern Approach. Pearson Education, Inc. (2010). [Google Scholar]
  • R.A. Saeed, D.R. Recupero and P. Remagnino, A boundary node method for path planning of mobile robots. Rob. Auton. Syst. 123 (2020) 103320. [CrossRef] [Google Scholar]
  • B. Sah, R. Gupta and D. Bani-Hani, Analysis of barriers to implement drone logistics. Int. J. Logistics Res. App. 24 (2021) 531–550. [CrossRef] [Google Scholar]
  • A.K. Saha, M.K. Arora, R.P. Gupta, M. Virdi and E. Csaplovics, GIS-based route planning in landslide-prone areas. Int. J. Geog. Inf. Sci. 19 (2005) 1149–1175. [CrossRef] [Google Scholar]
  • J.L. Sanchez-Lopez, M. Wang, M.A. Olivares-Mendez, M. Molina and H. Voos, A real-time 3D path planning solution for collision-free navigation of multirotor aerial robots in dynamic environments. J. Intell. Rob. Syst. 93 (2019) 33–53. [CrossRef] [Google Scholar]
  • T. Sangyam, P. Laohapiengsak, W. Chongcharoen and I. Nilkhamhang, Path tracking of UAV using self-tuning PID controller based on fuzzy logic, in Proceedings of SICE Annual Conference 2010. IEEE (2010) 1265–1269. [Google Scholar]
  • N. Sariff and N. Buniyamin, An overview of autonomous mobile robot path planning algorithms, in 2006 4th Student Conference On Research and Development. IEEE (2006) 183–188. [Google Scholar]
  • J. Saunders, B. Call, A. Curtis, R. Beard and T. McLain, Static and dynamic obstacle avoidance in miniature air vehicles, in Infotech@ Aerospace. American Institute of Aeronautics and Astronautics, Inc. (2005) 6950. [Google Scholar]
  • K. Sayler, A World of Proliferated Drones: A Technology Primer. A World of Proliferated Drones Series. Center for a New American Security (2015). [Google Scholar]
  • F. Schøler, A. la Cour-Harbo and M. Bisgaard, Generating approximative minimum length paths in 3B for UAVs, in 2012 IEEE Intelligent Vehicles Symposium (IV). IEEE (2012) 229–233. [Google Scholar]
  • T. Schouwenaars, B. De Moor, E. Feron and J. How, Mixed integer programming for multi-vehicle path planning, in 2001 European Control Conference (ECC). IEEE (2001) 2603–2608. [Google Scholar]
  • R. Schroer, UAVs: The future. [A century of powered flight: 1903–2003]. IEEE Aerosp. Electron. Syst. Mag. 18 (2003) 61–63. [CrossRef] [Google Scholar]
  • J. Sfeir, M. Saad and H. Saliah-Hassane, An improved artificial potential field approach to real-time mobile robot path planning in an unknown environment, in 2011 IEEE International Symposium on Robotic and Sensors Environments (ROSE). IEEE (2011) 208–213. [Google Scholar]
  • S. Shah, D. Dey, C. Lovett and A. Kapoor, Airsim: high-fidelity visual and physical simulation for autonomous vehicles, in Field and Service Robotics. Springer (2018) 621–635. [CrossRef] [Google Scholar]
  • K. Shang, S. Karungaru, Z. Feng, L. Ke and K. Terada, A GA-ACO hybrid algorithm for the multi-UAV mission planning problem, in 2014 14th International Symposium on Communications and Information Technologies (ISCIT). IEEE (2014) 243–248. [Google Scholar]
  • Z. Shang, J. Bradley and Z. Shen, A co-optimal coverage path planning method for aerial scanning of complex structures. Expert Syst. App. 158 (2020) 113535. [CrossRef] [Google Scholar]
  • S. Shao, Y. Peng, C. He and Y. Du, Efficient path planning for UAV formation via comprehensively improved particle swarm optimization. ISA Trans. 97 (2020) 415–430. [CrossRef] [Google Scholar]
  • D. Sharma and N. Kumar, A review on machine learning algorithms, tasks and applications. Int. J. Adv. Res. Comput. Eng. Technol. (IJARCET) 6 (2017) 2278–1323. [Google Scholar]
  • H. Shen and P. Li, Unmanned aerial vehicle (UAV) path planning based on improved pre-planning artificial potential field method, in 2020 Chinese Control And Decision Conference (CCDC). IEEE (2020) 2727–2732. [Google Scholar]
  • Z. Shi and W.K. Ng, A collision-free path planning algorithm for unmanned aerial vehicle delivery, in 2018 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE (2018) 358–362. [Google Scholar]
  • K. Shi, Z. Wu, B. Jiang and H.R. Karimi, Dynamic path planning of mobile robot based on improved simulated annealing algorithm. J. Franklin Inst. 360 (2023) 4378–4398. [CrossRef] [Google Scholar]
  • Z. Shiller, Off-line and on-line trajectory planning, in Motion and Operation Planning of Robotic Systems. Springer, Cham (2015) 29–62. [CrossRef] [Google Scholar]
  • R. Shivgan and Z. Dong, Energy-efficient drone coverage path planning using genetic algorithm, in 2020 IEEE 21st International Conference on High Performance Switching and Routing (HPSR). IEEE (2020) 1–6. [Google Scholar]
  • H. Shorakaei, M. Vahdani, B. Imani and A. Gholami, Optimal cooperative path planning of unmanned aerial vehicles by a parallel genetic algorithm. Robotica 34 (2016) 823–836. [CrossRef] [Google Scholar]
  • J.D. Silva Arantes, M.D. Silva Arantes, C.F. Motta Toledo, O.T. Júnior and B.C. Williams, Heuristic and genetic algorithm approaches for UAV path planning under critical situation. Int. J. Artif. Intell. Tools 26 (2017) 1760008. [CrossRef] [Google Scholar]
  • S. Skiena, Implementing Discrete Mathematics: Combinatorics and Graph Theory with Mathematica. Vol. 1. Addison-Wesley Reading, MA (1990). [Google Scholar]
  • C. Snow, Why drones are the future of the internet of things-sUAS news-the business of drones (2019). [Google Scholar]
  • B. Song, G. Qi and L. Xu, A survey of three-dimensional flight path planning for unmanned aerial vehicle, in 2019 Chinese Control and Decision Conference (CCDC). IEEE (2019) 5010–5015. [Google Scholar]
  • A. Sonmez, E. Kocyigit and E. Kugu, Optimal path planning for UAVs using genetic algorithm, in 2015 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE (2015) 50–55. [Google Scholar]
  • C. Stachniss, Robotic Mapping and Exploration. Vol. 55. Springer Freiburg, Germany (2009). [CrossRef] [Google Scholar]
  • A. Stentz, Optimal and efficient path planning for partially-known environments, in Proceedings of the 1994 IEEE International Conference on Robotics and Automation. IEEE (1994) 3310–3317. [Google Scholar]
  • R. Storn and K. Price, Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11 (1997) 341–359. [CrossRef] [Google Scholar]
  • Suaave.org. http://web4.cs.ucl.ac.uk/research/suaave/. [Google Scholar]
  • I.A. Sucan, M. Moll and L.E. Kavraki, The open motion planning library. IEEE Rob. Auto. Mag. 19 (2012) 72–82. [CrossRef] [Google Scholar]
  • P. Sujit and R. Beard, Multiple UAV path planning using anytime algorithms, in 2009 American Control Conference. IEEE (2009) 2978–2983. [Google Scholar]
  • X. Sun and S. Koenig, The fringe-saving A* search algorithm – a feasibility study, in IJCAI. Vol. 7. (2007) 2391–2397. [Google Scholar]
  • H.C. Sun and H.Y. Zhu, Study on path planning for UAV based on probabilistic roadmap method. J. Syst. Simul. 11 (2006) 3050–3053. [Google Scholar]
  • X. Sun, S. Koenig and W. Yeoh, Generalized adaptive A*, in Proceedings of the 7th International Joint Conference on Autonomous Agents And Multiagent Systems. Vol. 1. International Foundation for Autonomous Agents and Multiagent Systems (2008) 469–476. [Google Scholar]
  • Y. Sun, H. Meng, W.W. Li, D. Zhu and Z.L. Li, Study on UAV path planning simulation. Adv. Mater. Res. 765 (2013) 452–455. [CrossRef] [Google Scholar]
  • Z. Sun, J. Wu, J. Yang, Y. Huang, C. Li and D. Li, Path planning for GEO-UAV bistatic sar using constrained adaptive multiobjective differential evolution. IEEE Trans. Geosci. Remote Sens. 54 (2016) 6444–6457. [CrossRef] [Google Scholar]
  • H. Sun, J. Qi, C. Wu and M. Wang, Path planning for dense drone formation based on modified artificial potential fields, in 2020 39th Chinese Control Conference (CCC). IEEE (2020) 4658–4664. [Google Scholar]
  • R.S. Sutton and A.G. Barto, Reinforcement Learning: An Introduction. MIT Press (2018). [Google Scholar]
  • R.J. Szczerba, P. Galkowski, I.S. Glicktein and N. Ternullo, Robust algorithm for real-time route planning. IEEE Trans. Aerosp. Electron. Syst. 36 (2000) 869–878. [CrossRef] [Google Scholar]
  • L. Tai and M. Liu, Towards cognitive exploration through deep reinforcement learning for mobile robots. Preprint arXiv:1610.01733 (2016). [Google Scholar]
  • R. Tarjan, Depth-first search and linear graph algorithms. SIAM J. Comput. 1 (1972) 146–160. [CrossRef] [MathSciNet] [Google Scholar]
  • G. Tartaglione and M. Ariola, Obstacle avoidance via landmark clustering in a path-planning algorithm, in 2018 Annual American Control Conference (ACC). IEEE (2018) 2776–2781. [Google Scholar]
  • L. Techy and C.A. Woolsey, Minimum-time path planning for unmanned aerial vehicles in steady uniform winds. J. Guidance Control Dyn. 32 (2009) 1736–1746. [CrossRef] [Google Scholar]
  • H. Tong, Path planning of UAV based on voronoi diagram and DPSO. Proc. Eng. 29 (2012) 4198–4203. [CrossRef] [Google Scholar]
  • H.H. Triharminto, A.S. Prabuwono, T.B. Adji, N.A. Setiawan and N.Y. Chong, UAV dynamic path planning for intercepting of a moving target: a review, in FIRA RoboWorld Congress. Springer (2013) 206–219. [Google Scholar]
  • E. Tsardoulias, A. Iliakopoulou, A. Kargakos and L. Petrou, A review of global path planning methods for occupancy grid maps regardless of obstacle density. J. Intell. Rob. Syst. 84 (2016) 829–858. [CrossRef] [Google Scholar]
  • T. Turker, O.K. Sahingoz and G. Yilmaz, 2D path planning for UAVs in radar threatening environment using simulated annealing algorithm, in 2015 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE (2015) 56–61. [Google Scholar]
  • V. Usenko, L. von Stumberg, A. Pangercic and D. Cremers, Real-time trajectory replanning for MAVs using uniform B-splines and a 3D circular buffer, in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE (2017) 215–222. [Google Scholar]
  • K.P. Valavanis and G.J. Vachtsevanos, Future of unmanned aviation, in Handbook of Unmanned Aerial Vehicles. Springer, Dordrecht (2015) 2993–3009. [CrossRef] [Google Scholar]
  • K.P. Valavanis and G.J. Vachtsevanos, Handbook of Unmanned Aerial Vehicles. Vol. 1. Springer Dordrecht (2015). [CrossRef] [Google Scholar]
  • S. Vemprala and S. Saripalli, Vision based collaborative path planning for micro aerial vehicles, in 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE (2018) 3889–3895. [Google Scholar]
  • B. Vizvári, M. Golabi, A. Nedjati, F. Gümü¸sbuğa and G. Izbirak, Top-down approach to design the relief system in a metropolitan city using UAV technology, part I: the first 48 h. Nat. Hazards 99 (2019) 571–597. [CrossRef] [Google Scholar]
  • Y. Volkan Pehlivanoglu, O. Baysal and A. Hacioglu, Path planning for autonomous UAV via vibrational genetic algorithm. Aircraft Eng. Aerosp. Technol. 79 (2007) 352–359. [CrossRef] [Google Scholar]
  • X. Wang, V. Yadav and S. Balakrishnan, Cooperative UAV formation flying with obstacle/collision avoidance. IEEE Trans. Control Syst. Technol. 15 (2007) 672–679. [CrossRef] [Google Scholar]
  • Y. Wang, D. Mulvaney, I. Sillitoe and E. Swere, Robot navigation by waypoints. J. Intell. Rob. Syst. 52 (2008) 175–207. [CrossRef] [Google Scholar]
  • Y. Wang, P. Bai, X. Liang, W. Wang, J. Zhang and Q. Fu, Reconnaissance mission conducted by UAV swarms based on distributed pso path planning algorithms. IEEE Access 7 (2019) 105086–105099. [CrossRef] [Google Scholar]
  • Y. Wang, F. He and X. Deng, Multi-aircraft cooperative path planning for maneuvering target detection. J. Ind. Manage. Optim. 18 (2022) 1935. [CrossRef] [Google Scholar]
  • L. Wei, H. Peng, Z. Zheng and C. Kaiyuan, Fbcri based real-time path planning for unmanned aerial vehicles in unknown environments with uncertainty. Robot 35 (2013) 641–650. [CrossRef] [Google Scholar]
  • N. Wen, X. Su, P. Ma, L. Zhao and Y. Zhang, Online UAV path planning in uncertain and hostile environments. Int. J. Mach. Learn. Cybern. 8 (2017) 469–487. [CrossRef] [Google Scholar]
  • J. Wilburn, M. Perhinschi and B. Wilburn, Enhanced modified voronoi algorithm for UAV path planning and obstacle avoidance. Int. Rev. Aerosp. Eng. 6 (2013) 54–63. [Google Scholar]
  • X. Wu, W. Bai, Y. Xie, X. Sun, C. Deng and H. Cui, A hybrid algorithm of particle swarm optimization, metropolis criterion and rts smoother for path planning of UAVs. Appl. Soft Comput. 73 (2018) 735–747. [CrossRef] [Google Scholar]
  • X. Wu, L. Xu, R. Zhen and X. Wu, Biased sampling potentially guided intelligent bidirectional RRT* algorithm for UAV path planning in 3D environment. Math. Prob. Eng. 2019 (2019) 1–12. [Google Scholar]
  • M. Wu, W. Chen and X. Tian, Optimal energy consumption path planning for quadrotor UAV transmission tower inspection based on simulated annealing algorithm. Energies 15 (2022) 8036. [CrossRef] [Google Scholar]
  • Y. Xu and T. Kanade, Space Robotics: Dynamics and Control. Vol. 188. Springer Science & Business Media Boston, MA (1992). [Google Scholar]
  • Z. Xu, D. Deng and K. Shimada, Autonomous UAV exploration of dynamic environments via incremental sampling and probabilistic roadmap. IEEE Rob. Autom. Lett. 6 (2021) 2729–2736. [CrossRef] [Google Scholar]
  • H.S. Yahia and A.S. Mohammed, Path planning optimization in unmanned aerial vehicles using meta-heuristic algorithms: a systematic review. Environ. Monit. Assess. 195 (2023) 30. [CrossRef] [Google Scholar]
  • F. Yan, Y.S. Liu and J.Z. Xiao, Path planning in complex 3D environments using a probabilistic roadmap method. Int. J. Autom. Comput. 10 (2013) 525–533. [CrossRef] [MathSciNet] [Google Scholar]
  • K. Yang and S. Sukkarieh, 3D smooth path planning for a UAV in cluttered natural environments, in 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE (2008) 794–800. [Google Scholar]
  • Q. Yang and S.J. Yoo, Optimal UAV path planning: sensing data acquisition over IoT sensor networks using multi-objective bio-inspired algorithms. IEEE Access 6 (2018) 13671–13684. [CrossRef] [Google Scholar]
  • L. Yang, J. Qi, J. Xiao and X. Yong, A literature review of UAV 3D path planning, in 2014 11th World Congress on Intelligent Control and Automation (WCICA). IEEE (2014) 2376–2381. [Google Scholar]
  • P. Yang, K. Tang, J.A. Lozano and X. Cao, Path planning for single unmanned aerial vehicle by separately evolving waypoints. IEEE Trans. Rob. 31 (2015) 1130–1146. [CrossRef] [Google Scholar]
  • S. Yang, S. Yang and X. Yi, An efficient spatial representation for path planning of ground robots in 3D environments. IEEE Access 6 (2018) 41539–41550. [CrossRef] [Google Scholar]
  • P. Yao and H. Wang, Dynamic adaptive ant lion optimizer applied to route planning for unmanned aerial vehicle. Soft Comput. 21 (2017) 5475–5488. [CrossRef] [Google Scholar]
  • P. Yao, H. Wang and H. Ji, Multi-UAVs tracking target in urban environment by model predictive control and improved grey wolf optimizer. Aerosp. Sci. Technol. 55 (2016) 131–143. [CrossRef] [Google Scholar]
  • C. Yin, Z. Xiao, X. Cao, X. Xi, P. Yang and D. Wu, Offline and online search: UAV multiobjective path planning under dynamic urban environment. IEEE Int. Things J. 5 (2017) 546–558. [Google Scholar]
  • C. YongBo, M. YueSong, Y. JianQiao, S. XiaoLong and X. Nuo, Three-dimensional unmanned aerial vehicle path planning using modified wolf pack search algorithm. Neurocomputing 266 (2017) 445–457. [CrossRef] [Google Scholar]
  • S. You, C. Wan and R. Dai, Iterative learning optimization for UAV path planning with avoidance zones, in 2019 American Control Conference (ACC). IEEE (2019) 2759–2764. [Google Scholar]
  • C. Yu and Z. Wang, UAV path planning using GSO-DE algorithm, in 2013 IEEE International Conference of IEEE Region 10 (TENCON 2013). IEEE (2013) 1–4. [Google Scholar]
  • G. Yu, H. Song and J. Gao, Unmanned aerial vehicle path planning based on TLBO algorithm. Int. J. Smart Sensing Intell. Syst. 7 (2014) 1310–1325. [Google Scholar]
  • X. Yu, C. Li and J. Zhou, A constrained differential evolution algorithm to solve UAV path planning in disaster scenarios. Knowl.-Based Syst. 204 (2020) 106209. [CrossRef] [Google Scholar]
  • X. Yu, C. Li and G.G. Yen, A knee-guided differential evolution algorithm for unmanned aerial vehicle path planning in disaster management. Appl. Soft Comput. 98 (2021) 106857. [CrossRef] [Google Scholar]
  • Y. Yuan, Z. Xingshe, Z. Kailong and D. Dong, Dynamic trajectory planning for unmanned aerial vehicle based on sparse A* search and improved artificial potential field. Control Theory Appl. 27 (2010) 953–959. [Google Scholar]
  • X. Yue and W. Zhang, UAV path planning based on k-means algorithm and simulated annealing algorithm, in 2018 37th Chinese Control Conference (CCC). IEEE (2018) 2290–2295. [Google Scholar]
  • Z. Zeng, K. Sammut, L. Lian, F. He, A. Lammas and Y. Tang, A comparison of optimization techniques for AUV path planning in environments with ocean currents. Rob. Auton. Syst. 82 (2016) 61–72. [CrossRef] [Google Scholar]
  • Zephyr Sim. https://zephyr-sim.com/. [Google Scholar]
  • X. Zhang, J. Chen, B. Xin and H. Fang, Online path planning for UAV using an improved differential evolution algorithm. IFAC Proc. Vol. 44 (2011) 6349–6354. [CrossRef] [MathSciNet] [Google Scholar]
  • X. Zhang, J. Chen, B. Xin and Z. Peng, A memetic algorithm for path planning of curvature-constrained UAVs performing surveillance of multiple ground targets. Chin. J. Aeron. 27 (2014) 622–633. [CrossRef] [MathSciNet] [Google Scholar]
  • B. Zhang, W. Liu, Z. Mao, J. Liu and L. Shen, Cooperative and geometric learning algorithm (CGLA) for path planning of UAVs with limited information. Automatica 50 (2014) 809–820. [CrossRef] [MathSciNet] [Google Scholar]
  • B. Zhang, Z. Mao, W. Liu and J. Liu, Geometric reinforcement learning for path planning of UAVs. J. Intell. Rob. Syst. 77 (2015) 391–409. [CrossRef] [Google Scholar]
  • C. Zhang, X. Zhou, H. Zhao, A. Dai and H. Zhou, Three-dimensional fuzzy control of mini quadrotor UAV trajectory tracking under impact of wind disturbance, in 2016 International Conference on Advanced Mechatronic Systems (ICAMechS). IEEE (2016) 372–377. [Google Scholar]
  • C. Zhang, H. Liu and Y. Tang, Analysis for UAV heuristic tracking path planning based on target matching, in 2018 9th International Conference on Mechanical and Aerospace Engineering (ICMAE). IEEE (2018) 34–39. [Google Scholar]
  • D. Zhang, Y. Xu and X. Yao, An improved path planning algorithm for unmanned aerial vehicle based on RRT-connect, in 2018 37th Chinese Control Conference (CCC). IEEE (2018) 4854–4858. [Google Scholar]
  • H.Y. Zhang, W.M. Lin and A.X. Chen, Path planning for the mobile robot: a review. Symmetry 10 (2018) 450. [CrossRef] [Google Scholar]
  • X. Zhang, X. Lu, S. Jia and X. Li, A novel phase angle-encoded fruit fly optimization algorithm with mutation adaptation mechanism applied to UAV path planning. Appl. Soft Comput. 70 (2018) 371–388. [CrossRef] [Google Scholar]
  • S. Zhang, T. Xu, H. Cheng and F. Liang, Collision avoidance of fixed-wing UAVs in dynamic environments based on spline-RRT and velocity obstacle, in 2020 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE (2020) 48–58. [Google Scholar]
  • Y. Zhao, Z. Zheng and Y. Liu, Survey on computational-intelligence-based UAV path planning. Knowl.-Based Syst. 158 (2018) 54–64. [CrossRef] [Google Scholar]
  • Z. Zhou, H. Duan, P. Li and B. Di, Chaotic differential evolution approach for 3D trajectory planning of unmanned aerial vehicle, in 2013 10th IEEE International Conference on Control and Automation (ICCA). IEEE (2013) 368–372. [CrossRef] [Google Scholar]
  • X. Zhou, K. Xie, K. Huang, Y. Liu, Y. Zhou, M. Gong and H. Huang, Offsite aerial path planning for efficient urban scene reconstruction. ACM Trans. Graphics (Proc. SIGGRAPH ASIA 2020) 39 (2020) 192:1–192:16. [Google Scholar]
  • X. Zhou, Z. Yi, Y. Liu, K. Huang and H. Huang, Survey on path and view planning for UAVs. Virtual Reality Intell. Hardware 2 (2020) 56–69. [CrossRef] [Google Scholar]
  • D. Zhuoning, Z. Rulin, C. Zongji and Z. Rui, Study on UAV path planning approach based on fuzzy virtual force. Chin. J. Aeron. 23 (2010) 341–350. [CrossRef] [Google Scholar]
  • Zipline, Protecting Ghana’s election: instant agility with Zipline’s autonomous delivery network (2021). https://assets.ctfassets.net/pbn2i2zbvp41/3yrQaMNdJ1u1J2aSEucjzt/4412ea5d12896d15b7eb41a2212d0295/Zipline_Ghana_PPE_Global_Healthcare_Feb-2021.pdf. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.