The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges
Abstract
Abstract Path planning is one of the most important problems to be explored in unmanned aerial vehicles (UAVs) for finding an optimal path between source and destination. Although, in literature, a lot of research proposals exist on the path planning problems of UAVs but still issues of target location and identification persist keeping in view of the high mobility of UAVs. To solve these issues in UAVs path planning, optimal decisions need to be taken for various mission-critical operations performed by UAVs. These decisions require a map or graph of the mission environment so that UAVs are aware of their locations with respect to the map or graph. Keeping focus on the aforementioned points, this paper analyzes various UAVs path planning techniques used over the past many years. The aim of path planning techniques is not only to find an optimal and shortest path but also to provide the collision-free environment to the UAVs. It is important to have path planning techniques to compute a safe path in the shortest possible time to the final destination. In this paper, various path planning techniques for UAVs are classified into three broad categories, i.e., representative techniques, cooperative techniques, and non-cooperative techniques. With these techniques, coverage and connectivity of the UAVs network communication are discussed and analyzed. Based on each category of UAVs path planning, a critical analysis of the existing proposals has also been done. For better understanding, various comparison tables using parameters such as-path length, optimality, completeness, cost-efficiency, time efficiency, energy-efficiency, robustness and collision avoidance are also included in the text. In addition, a number of open research problems based on UAVs path planning and UAVs network communication are explored to provide deep insights to the readers.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
A Formal Basis for the Heuristic Determination of Minimum Cost Paths
Hart, P., Nilsson, N., & Raphael, B. (1968) · IEEE Transactions on Systems Science and Cybernetics · doi:10.1109/tssc.1968.300136 · Cited by 12,738
Real-Time Obstacle Avoidance for Manipulators and Mobile Robots
Khatib, O. (1986) · The International Journal of Robotics Research · doi:10.1177/027836498600500106 · Cited by 7,894
Probabilistic roadmaps for path planning in high-dimensional configuration spaces
Kavraki, L. E., Svestka, P., Latombe, J.-C., & Overmars, M. H. (1996) · IEEE Transactions on Robotics and Automation · doi:10.1109/70.508439 · Cited by 6,343
Principles of Artificial Intelligence
Nilsson, N. J. (1982) · doi:10.1007/978-3-662-09438-9 · Cited by 3,457
A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems
Mozaffari, M., Saad, W., Bennis, M., Nam, Y.-H., & Debbah, M. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2902862 · Cited by 299
Research works printed but not linked: 102
- Y. Huo, X. Dong, T. Lu, W. Xu, M. Yuen, Distributed and multi-layer uav network for the next-generation wireless communication, arXiv preprint arXiv:1805.01534. · arXiv
- Mozaffari, 2017, Wireless communication using unmanned aerial vehicles (uavs): Optimal transport theory for hover time optimization, IEEE Trans. Wireless Commun., 16, 8052, 10.1109/TWC.2017.2756644 · doi:10.1109/twc.2017.2756644
- Mozaffari, 2017, Mobile unmanned aerial vehicles (uavs) for energy-efficient internet of things communications, IEEE Trans. Wireless Commun., 16, 7574, 10.1109/TWC.2017.2751045 · DOI · Paper page
- Qi, 2019, Secrecy energy efficiency performance in communication networks with mobile sinks, Phys. Commun., 32, 41, 10.1016/j.phycom.2018.06.009 · DOI
- Zeng, 2018, Cellular-connected uav: Potential, challenges, and promising technologies, IEEE Wirel. Commun., 26, 120, 10.1109/MWC.2018.1800023 · DOI · Paper page
- Mozaffari, 2018, Beyond 5g with uavs: Foundations of a 3d wireless cellular network, IEEE Trans. Wireless Commun., 18, 357, 10.1109/TWC.2018.2879940 · DOI
- Chase, 2013, The evolution of the internet of things, Texas Instrum., 1
- Fotouhi, 2019, Survey on uav cellular communications: Practical aspects, standardization advancements, regulation, and security challenges, IEEE Commun. Surv. Tutor., 10.1109/COMST.2019.2906228 · DOI · Paper page
- Lozano-Pérez, 1979, An algorithm for planning collision-free paths among polyhedral obstacles, Commun. ACM, 22, 560, 10.1145/359156.359164 · DOI · Paper page
- Nikolos, 2001, Evolutionary algorithm based off-line path planner for uav navigation, Automatika-Zagreb-, 42, 143
- Kitamura, 1995, 3-d path planning in a dynamic environment using an octree and an artificial potential field, vol. 2, 474
- Hwang, 1992, A potential field approach to path planning, IEEE Trans. Robot. Autom., 8, 23, 10.1109/70.127236 · DOI
- Brooks, 1985, A subdivision algorithm in configuration space for findpath with rotation, IEEE Trans. Syst. Man Cybern., 224, 10.1109/TSMC.1985.6313352 · DOI · Paper page
- Noborio, 1990, A quadtree-based path-planning algorithm for a mobile robot, J. Robot. Syst., 7, 555, 10.1002/rob.4620070404 · doi:10.1002/rob.4620070404
- Sud, 2008, Real-time path planning in dynamic virtual environments using multiagent navigation graphs, IEEE Trans. Vis. Comput. Graphics, 14, 526, 10.1109/TVCG.2008.27 · doi:10.1109/tvcg.2008.27
- Thompson, 1977, Bthe navigation system of the jpl robot, [in proc. 5th int], 749
- Li, 2011, Coverage path planning for uavs based on enhanced exact cellular decomposition method, Mechatronics, 21, 876, 10.1016/j.mechatronics.2010.10.009 · DOI · Paper page
- Torres, 2016, Coverage path planning with unmanned aerial vehicles for 3d terrain reconstruction, Expert Syst. Appl., 55, 441, 10.1016/j.eswa.2016.02.007 · DOI · Paper page
- Balampanis, 2017, Area partition for coastal regions with multiple uas, J. Intell. Robot. Syst., 88, 751, 10.1007/s10846-017-0559-9 · DOI
- Balampanis, 2017, Coastal areas division and coverage with multiple uavs for remote sensing, Sensors, 17, 808, 10.3390/s17040808 · doi:10.3390/s17040808
- Acevedo, 2013, Cooperative large area surveillance with a team of aerial mobile robots for long endurance missions, J. Intell. Robot. Syst., 70, 329, 10.1007/s10846-012-9716-3 · DOI
- Hayat, 2016, Survey on unmanned aerial vehicle networks for civil applications: A communications viewpoint, IEEE Commun. Surv. Tutor., 18, 2624, 10.1109/COMST.2016.2560343 · DOI · Paper page
- Gupta, 2016, Survey of important issues in uav communication networks, IEEE Commun. Surv. Tutor., 18, 1123, 10.1109/COMST.2015.2495297 · DOI · Paper page
- Cao, 2018, Airborne communication networks: A survey, IEEE J. Sel. Areas Commun., 36, 1907, 10.1109/JSAC.2018.2864423 · DOI · Paper page
- Motlagh, 2016, Low-altitude unmanned aerial vehicles-based internet of things services: Comprehensive survey and future perspectives, IEEE Internet Things J., 3, 899, 10.1109/JIOT.2016.2612119 · DOI · Paper page
- Li, 2018, Uav communications for 5g and beyond: Recent advances and future trends, IEEE Internet Things J., 6, 2241, 10.1109/JIOT.2018.2887086 · DOI · Paper page
- Valente, 2013, Aerial coverage optimization in precision agriculture management: A musical harmony inspired approach, Comput. Electron. Agric., 99, 153, 10.1016/j.compag.2013.09.008 · DOI
- Krishna, 2017, A review on cybersecurity vulnerabilities for unmanned aerial vehicles, 194
- Schroer, 2003, Uavs: the future.[a century of powered flight: 1903-2003], IEEE Aerosp. Electron. Syst. Mag., 18, 61, 10.1109/MAES.2003.1226537 · doi:10.1109/maes.2003.1226537
- Keane, 2013, A brief history of early unmanned aircraft, Johns Hopkins APL Tech. Dig., 32, 558
- Niethammer, 2012, Uav-based remote sensing of the super-sauze landslide: Evaluation and results, Eng. Geol., 128, 2, 10.1016/j.enggeo.2011.03.012 · DOI · Paper page
- Jaakkola, 2010, A low-cost multi-sensoral mobile mapping system and its feasibility for tree measurements, ISPRS J. Photogramm. Remote Sens., 65, 514, 10.1016/j.isprsjprs.2010.08.002 · DOI · Paper page
- Calderón, 2014, Detection of downy mildew of opium poppy using high-resolution multi-spectral and thermal imagery acquired with an unmanned aerial vehicle, Precis. Agric., 15, 639, 10.1007/s11119-014-9360-y · doi:10.1007/s11119-014-9360-y
- Tattaris, 2016, A direct comparison of remote sensing approaches for high-throughput phenotyping in plant breeding, Front. Plant Sci., 7, 1131, 10.3389/fpls.2016.01131 · DOI · Paper page
- Paredes, 2018, 3d indoor positioning of uavs with spread spectrum ultrasound and time-of-flight cameras, Sensors, 18, 89, 10.3390/s18010089 · doi:10.3390/s18010089
- Gupta, 2015, Survey of important issues in uav communication networks, IEEE Commun. Surv. Tutor., 18, 1123, 10.1109/COMST.2015.2495297 · DOI · Paper page
- Tuna, 2014, Unmanned aerial vehicle-aided communications system for disaster recovery, J. Netw. Comput. Appl., 41, 27, 10.1016/j.jnca.2013.10.002 · DOI · Paper page
- Chen, 2016, A novel wireless multifunctional electronic current transformer based on zigbee-based communication, IEEE Trans. Smart Grid, 8, 1888, 10.1109/TSG.2015.2510325 · doi:10.1109/tsg.2015.2510325
- Wang, 2014, Implementation of a wireless ecg acquisition soc for ieee 802.15. 4 (zigbee) applications, IEEE J. Biomed. Health Inf., 19, 247, 10.1109/JBHI.2014.2311232 · DOI
- Geem, 2001, A new heuristic optimization algorithm: harmony search, Simulation, 76, 60, 10.1177/003754970107600201 · DOI · Paper page
- Kavraki, 1998, Randomized query processing in robot path planning, J. Comput. System Sci., 57, 50, 10.1006/jcss.1998.1578 · DOI
- Masehian, 2010, Multi-objective robot motion planning using a particle swarm optimization model, J. Zhejiang Univ. Sci. C, 11, 607, 10.1631/jzus.C0910525 · doi:10.1631/jzus.c0910525
- Meng, 2017, Decentralized multi-uav flight autonomy for moving convoys search and track, IEEE Trans. Control Syst. Technol., 25, 1480, 10.1109/TCST.2016.2601287 · DOI
- Wen, 2015, Uav online path planning algorithm in a low altitude dangerous environment, IEEE/CAA J. Autom. Sin., 2, 173, 10.1109/JAS.2015.7081657 · DOI
- Lin, 2017, Sampling-based path planning for uav collision avoidance, IEEE Trans. Intell. Transp. Syst., 18, 3179, 10.1109/TITS.2017.2673778 · DOI · Paper page
- Li, 2016, A hybrid path planning method in unmanned air/ground vehicle (uav/ugv) cooperative systems, IEEE Trans. Veh. Technol., 65, 9585, 10.1109/TVT.2016.2623666 · DOI · Paper page
- Kwak, 2018, Autonomous uav flight control for gps-based navigation, IEEE Access, 6, 37947, 10.1109/ACCESS.2018.2854712 · DOI
- Sun, 2015, Triple-stage path prediction algorithm for real-time mission planning of multi-uav, Electron. Lett., 51, 1490, 10.1049/el.2015.1244 · doi:10.1049/el.2015.1244
- Ma, 2018, 3-d decentralized prioritized motion planning and coordination for high-density operations of micro aerial vehicles, IEEE Trans. Control Syst. Technol., 26, 939, 10.1109/TCST.2017.2699165 · DOI
- Penin, 2019, Minimum-time trajectory planning under intermittent measurements, IEEE Robot. Autom. Lett., 4, 153, 10.1109/LRA.2018.2883375 · doi:10.1109/lra.2018.2883375
- Park, 2018, Dronenetx: Network reconstruction through connectivity probing and relay deployment by multiple uavs in ad hoc networks, IEEE Trans. Veh. Technol., 67, 11192, 10.1109/TVT.2018.2870397 · DOI
- Parker, 1999, Cooperative robotics for multi-target observation, Intell. Autom. Soft Comput., 5, 5, 10.1080/10798587.1999.10750747 · doi:10.1080/10798587.1999.10750747
- Zhou, 2018, When mobile crowd sensing meets uav: Energy-efficient task assignment and route planning, IEEE Trans. Commun., 66, 5526, 10.1109/TCOMM.2018.2857461 · DOI · Paper page
- J. Liu, X. Wang, B. Bai, H. Dai, Age-optimal trajectory planning for uav-assisted data collection, arXiv preprint arXiv:1804.09356. · arXiv
- Perazzo, 2017, Drone path planning for secure positioning and secure position verification, IEEE Trans. Mob. Comput., 1
- Masehian, 2007, Robot path planning in 3d space using binary integer programming, Int. J. Mech. Syst. Sci. Eng., 23, 26
- Chamseddine, 2012, Flatness-based trajectory planning/replanning for a quadrotor unmanned aerial vehicle, IEEE Trans. Aerosp. Electron. Syst., 48, 2832, 10.1109/TAES.2012.6324664 · doi:10.1109/taes.2012.6324664
- Mathew, 2015, Multirobot rendezvous planning for recharging in persistent tasks, IEEE Trans. Robot., 31, 128, 10.1109/TRO.2014.2380593 · DOI · Paper page
- Qin, 2003, A survey of industrial model predictive control technology, Control Eng. Pract., 11, 733, 10.1016/S0967-0661(02)00186-7 · DOI · Paper page
- Zhang, 2018, Design and information architectures for an unmanned aerial vehicle cooperative formation tracking controller, IEEE Access, 6, 45821, 10.1109/ACCESS.2018.2865807 · doi:10.1109/access.2018.2865807
- Shah, 2015, Guidance of air vehicles: A sliding mode approach, IEEE Trans. Control Syst. Technol., 23, 231, 10.1109/TCST.2014.2322773 · doi:10.1109/tcst.2014.2322773
- Oliveira, 2016, Moving path following for unmanned aerial vehicles with applications to single and multiple target tracking problems, IEEE Trans. Robot., 32, 1062, 10.1109/TRO.2016.2593044 · doi:10.1109/tro.2016.2593044
- Yu, 2015, Cooperative path planning for target tracking in urban environments using unmanned air and ground vehicles, IEEE/ASME Trans. Mechatronics, 20, 541, 10.1109/TMECH.2014.2301459 · DOI · Paper page
- Schlotfeldt, 2018, Anytime planning for decentralized multirobot active information gathering, IEEE Robot. Autom. Lett., 3, 1025, 10.1109/LRA.2018.2794608 · DOI
- Koohifar, 2017, Receding horizon multi-uav cooperative tracking of moving rf source, IEEE Commun. Lett., 21, 1433, 10.1109/LCOMM.2016.2603977 · DOI
- Hausman, 2017, Observability-aware trajectory optimization for self-calibration with application to uavs, IEEE Robot. Autom. Lett., 2, 1770, 10.1109/LRA.2017.2647799 · DOI
- Wallar, 2015, Reactive motion planning for unmanned aerial surveillance of risk-sensitive areas, IEEE Trans. Autom. Sci. Eng., 12, 969, 10.1109/TASE.2015.2443033 · DOI
- Zhang, 2018, Sequential convex programming for nonlinear optimal control problems in uav path planning, Aerosp. Sci. Technol., 76, 280, 10.1016/j.ast.2018.01.040 · DOI
- Kyriakis, 2019, Terrain following for fixed-wing unmanned aerial vehicles using feedback equivalence, IEEE Control Syst. Lett., 3, 150, 10.1109/LCSYS.2018.2854239 · doi:10.1109/lcsys.2018.2854239
- Cabecinhas, 2015, A globally stabilizing path following controller for rotorcraft with wind disturbance rejection, IEEE Trans. Control Syst. Technol., 23, 708, 10.1109/TCST.2014.2326820 · doi:10.1109/tcst.2014.2326820
- Yang, 2018, Optimal uav path planning: Sensing data acquisition over iot sensor networks using multi-objective bio-inspired algorithms, IEEE Access, 6, 13671, 10.1109/ACCESS.2018.2812896 · doi:10.1109/access.2018.2812896
- Tokekar, 2016, Sensor planning for a symbiotic uav and ugv system for precision agriculture, IEEE Trans. Robot., 32, 1498, 10.1109/TRO.2016.2603528 · DOI · Paper page
- M. Popovic, T. Vidal-Calleja, G. Hitz, I. Sa, R. Siegwart, J. Nieto, Multiresolution mapping and informative path planning for uav-based terrain monitoring, arXiv preprint arXiv:1703.02854. · arXiv
- Slegers, 2003, Aspects of control for a parafoil and payload system, J. Guid. Control Dyn., 26, 898, 10.2514/2.6933 · doi:10.2514/2.6933
- Chamseddine, 2017, Communication relay for multiground units with unmanned aerial vehicle using only signal strength and angle of arrival, IEEE Trans. Control Syst. Technol., 25, 286, 10.1109/TCST.2016.2552461 · doi:10.1109/tcst.2016.2552461
- Foo, 2009, Path planning of unmanned aerial vehicles using b-splines and particle swarm optimization, J. Aerosp. Comput. Inf. Commun., 6, 271, 10.2514/1.36917 · DOI
- Duan, 2015, Interactive learning environment for bio-inspired optimization algorithms for uav path planning, IEEE Trans. Educ., 58, 276, 10.1109/TE.2015.2402196 · DOI
- Sun, 2016, Path planning for geo-uav bistatic sar using constrained adaptive multiobjective differential evolution, IEEE Trans. Geosci. Remote Sens., 54, 6444, 10.1109/TGRS.2016.2585184 · DOI
- Yang, 2015, Path planning for single unmanned aerial vehicle by separately evolving waypoints, IEEE Trans. Robot., 31, 1130, 10.1109/TRO.2015.2459812 · DOI
- Jiang, 2018, Online path planning of autonomous uavs for bearing-only standoff multi-target following in threat environment, IEEE Access, 6, 22531, 10.1109/ACCESS.2018.2824849 · doi:10.1109/access.2018.2824849
- Wu, 2018, Path planning of uavs based on collision probability and kalman filter, IEEE Access, 6, 34237, 10.1109/ACCESS.2018.2817648 · doi:10.1109/access.2018.2817648
- Faigl, 2018, Surveillance planning with bézier curves, IEEE Robot. Autom. Lett., 3, 750, 10.1109/LRA.2018.2789844 · doi:10.1109/lra.2018.2789844
- Farmani, 2017, A scalable multitarget tracking system for cooperative unmanned aerial vehicles, IEEE Trans. Aerosp. Electron. Syst., 53, 1947, 10.1109/TAES.2017.2677746 · DOI
- Teuliere, 2015, 3-d model-based tracking for uav indoor localization, IEEE Trans. Cybernet., 45, 869, 10.1109/TCYB.2014.2337652 · DOI
- Heyer, 1999, Exploring expression data: identification and analysis of coexpressed genes, Genome Res., 9, 1106, 10.1101/gr.9.11.1106 · DOI · Paper page
- Kemp, 2008, Multi-modal tracking using texture changes, Image Vis. Comput., 26, 442, 10.1016/j.imavis.2006.12.014 · doi:10.1016/j.imavis.2006.12.014
- L. Tai, M. Liu, Towards cognitive exploration through deep reinforcement learning for mobile robots, arXiv preprint arXiv:1610.01733. · arXiv
- Wang, 2018, Two-echelon location-routing optimization with time windows based on customer clustering, Expert Syst. Appl., 104, 244, 10.1016/j.eswa.2018.03.018 · DOI
- Yin, 2018, Offline and online search: Uav multiobjective path planning under dynamic urban environment, IEEE Internet Things J., 5, 546, 10.1109/JIOT.2017.2717078 · DOI
- Angley, 2018, Search for targets in a risky environment using multi-objective optimisation, IET Radar Sonar Navig., 13, 123, 10.1049/iet-rsn.2018.5184 · doi:10.1049/iet-rsn.2018.5184
- Koohifar, 2018, Autonomous tracking of intermittent rf source using a uav swarm, IEEE Access, 6, 2169, 10.1109/ACCESS.2018.2810599 · DOI
- Zeng, 2018, Trajectory design for completion time minimization in uav-enabled multicasting, IEEE Trans. Wirel. Commun., 17, 2233, 10.1109/TWC.2018.2790401 · DOI · Paper page
- Jeong, 2018, Mobile edge computing via a uav-mounted cloudlet: Optimization of bit allocation and path planning, IEEE Trans. Veh. Technol., 67, 2049, 10.1109/TVT.2017.2706308 · DOI · Paper page
- Lee, 2017, Optimal path planning of solar-powered uav using gravitational potential energy, IEEE Trans. Aerosp. Electron. Syst., 53, 1442, 10.1109/TAES.2017.2671522 · DOI
- Razzaq, 2018, Three-dimensional uav routing with deconfliction, IEEE Access, 6, 21536, 10.1109/ACCESS.2018.2824558 · doi:10.1109/access.2018.2824558
- Bogdanowicz, 2017, Flying swarm of drones over circulant digraph, IEEE Trans. Aerosp. Electron. Syst., 53, 2662, 10.1109/TAES.2017.2709858 · doi:10.1109/taes.2017.2709858
- Akyildiz, 2005, Underwater acoustic sensor networks: research challenges, Ad Hoc Netw., 3, 257, 10.1016/j.adhoc.2005.01.004 · DOI · Paper page
- Ammari, 2010, A study of k-coverage and measures of connectivity in 3d wireless sensor networks, IEEE Trans. Comput., 59, 243, 10.1109/TC.2009.166 · doi:10.1109/tc.2009.166
- Cabreira, 2018, Energy-aware spiral coverage path planning for uav photogrammetric applications, IEEE Robot. Autom. Lett., 3, 3662, 10.1109/LRA.2018.2854967 · DOI · Paper page
- Lin, 2018, Security and privacy for the internet of drones: Challenges and solutions, IEEE Commun. Mag., 56, 64, 10.1109/MCOM.2017.1700390 · DOI · Paper page
- A. Sanjab, W. Saad, T. Başar, Prospect theory for enhanced cyber-physical security of drone delivery systems: A network interdiction game, arXiv preprint arXiv:1702.04240. · arXiv
- Gharibi, 2016, Internet of drones, IEEE Access, 4, 1148, 10.1109/ACCESS.2016.2537208 · DOI · Paper page
Cited by
A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges
McEnroe, P., Wang, S., & Liyanage, M. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3176400 · Cited by 482
Recent Advances in Unmanned Aerial Vehicles: A Review
Ahmed, F., Mohanta, J. C., Keshari, A., & Yadav, P. S. (2022) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-022-06738-0 · Cited by 328
UAV Path Planning Using Optimization Approaches: A Survey
Ait Saadi, A., Soukane, A., Meraihi, Y., Benmessaoud Gabis, A., Mirjalili, S., & Ramdane-Cherif, A. (2022) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-022-09742-7 · Cited by 269
Path-Planning for Unmanned Aerial Vehicles with Environment Complexity Considerations: A Survey
Jones, M., Djahel, S., & Welsh, K. (2023) · ACM Computing Surveys · doi:10.1145/3570723 · Cited by 258
Nikooghadam, M., Amintoosi, H., Islam, S. H., & Moghadam, M. F. (2021) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2020.101955 · Cited by 140
Metadata
- Authors
- Aggarwal, S., & Kumar, N.
- Venue
- Computer Communications
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 149
- Pages
- 270-299
- ISSN
- 0140-3664 (ISSN-L)
- OpenAlex
- W2981460880
- Cited by
- 971
- References
- 6
- FWCI
- 26.81
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges. https://app.cochat.ai/papers/10.1016/j.comcom.2019.10.014
- The paper
- Aggarwal, S., & Kumar, N. (2020). Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges. Computer Communications. https://doi.org/10.1016/j.comcom.2019.10.014
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.