The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
UAV path planning using artificial potential field method updated by optimal control theory
Abstract
The unmanned aerial vehicle (UAV) path planning problem is an important assignment in the UAV mission planning. Based on the artificial potential field (APF) UAV path planning method, it is reconstructed into the constrained optimisation problem by introducing an additional control force. The constrained optimisation problem is translated into the unconstrained optimisation problem with the help of slack variables in this paper. The functional optimisation method is applied to reform this problem into an optimal control problem. The whole transformation process is deduced in detail, based on a discrete UAV dynamic model. Then, the path planning problem is solved with the help of the optimal control method. The path following process based on the six degrees of freedom simulation model of the quadrotor helicopters is introduced to verify the practicability of this method. Finally, the simulation results show that the improved method is more effective in planning path. In the planning space, the length of the calculated path is shorter and smoother than that using traditional APF method. In addition, the improved method can solve the dead point problem effectively.
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
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
Research works printed but not linked: 17
- 10.1016/j.comnet.2010.07.017 · DOI
- 10.3724/SP.J.1218.2012.00519 · DOI
- Caves A.D.J., 2010, Human-automation collaborative RRT for UAV mission path planning
- 10.3724/SP.J.1218.2010.00568 · doi:10.3724/sp.j.1218.2010.00568
- 10.1007/0-387-28356-0_10 · DOI
- Fang Q., 2006, Acta Automatica Sinica, 32, 767
- Li B., 2011, Optimal control problems with constraints on the state and control and their applications
- 10.1243/09544100JAERO665 · doi:10.1243/09544100jaero665
- Liu B., 2000, The modern control theory
- 10.1016/j.simpat.2008.08.005 · doi:10.1016/j.simpat.2008.08.005
- 10.1177/0278364908090949 · DOI · Paper page
- 10.1007/s10846-011-9586-0 · DOI
- Sun Jinsheng L.J., 1994, Journal of Nanjing University of Aeronautics & Astronautics, 26, 48
- Wu S.Z., 2008, Optimal control theory and application
- Xiao-Qun C., 2012, Acta Physica Sinica, 61, 1
- 10.1007/s11771-008-0104-x · doi:10.1007/s11771-008-0104-x
- Yin L., 2009, Journal of System Simulation, 21, s3325
Cited by
Safety-enhanced UAV path planning with spherical vector-based particle swarm optimization
Phung, M. D., & Ha, Q. P. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107376 · Cited by 565
Qu, C., Gai, W., Zhong, M., & Zhang, J. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106099 · Cited by 334
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
Singletary, A., Klingebiel, K., Bourne, J., Browning, A., Tokumaru, P., & Ames, A. (2021) · 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636670 · Cited by 182
Application of Deep Reinforcement Learning to UAV Swarming for Ground Surveillance
Arranz, R., Carramiñana, D., Miguel, G. D., Besada, J. A., & Bernardos, A. M. (2023) · Sensors · doi:10.3390/s23218766 · Cited by 58
Metadata
- Authors
- Chen, Y.-B., Luo, G.-C., Mei, Y.-S., Yu, J.-Q., & Su, X.-L.
- Venue
- International Journal of Systems Science
- Publisher
- Informa UK Limited
- Published
- 2014-06-18
- Volume
- 47
- Issue
- 6
- Pages
- 1407-1420
- ISSN
- 0020-7721 (ISSN-L)
- 1464-5319
- OpenAlex
- W2075956742
- Cited by
- 442
- References
- 1
- FWCI
- 9.79
- Type
- journal article
- Language
- en
- Open access
- closed
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: UAV path planning using artificial potential field method updated by optimal control theory. https://app.cochat.ai/papers/10.1080/00207721.2014.929191
- The paper
- Chen, Y.-B., Luo, G.-C., Mei, Y.-S., Yu, J.-Q., & Su, X.-L. (2016). UAV path planning using artificial potential field method updated by optimal control theory. International Journal of Systems Science. https://doi.org/10.1080/00207721.2014.929191
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.
