The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
An improved PSO algorithm for smooth path planning of mobile robots using continuous high-degree Bezier curve
Open accessAbstract
Abstract In this paper, a new strategy is developed to plan the smooth path for mobile robots through an improved PSO algorithm in combination with the continuous high-degree Bezier curve. Rather than connecting several low-degree Bezier curve segments, the use of continuous high-degree Bezier curves facilitates the fulfilment of the requirement of high-order continuity such as the continuous curvature derivative, which is critical for the motion control of the mobile robots. On the other hand, the smooth path planning of mobile robots is mathematically an optimization problem that can be dealt with by evolutionary computation algorithms. In this regard, an improved particle swarm optimization (PSO) algorithm is proposed to tackle the local trapping and premature convergence issues. In the improved PSO algorithm, an adaptive fractional-order velocity is introduced to enforce some disturbances on the particle swarm according to its evolutionary state, thereby enhancing its capability of jumping out of the local minima and exploring the searching space more thoroughly. The superiority of the improved PSO algorithm is verified by comparing with several standard and modified PSO algorithms on some benchmark functions, and the advantages of the new strategy is also confirmed by several comprehensive simulation experiments for the smooth path planning of mobile robots.
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
Kennedy, J., & Eberhart, R. (2002) · Proceedings of ICNN'95 - International Conference on Neural Networks · doi:10.1109/icnn.1995.488968 · Cited by 58,192
A modified particle swarm optimizer
Shi, Y., & Eberhart, R. (2002) · 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360) · doi:10.1109/icec.1998.699146 · Cited by 10,229
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
Rapidly-exploring random trees : a new tool for path planning
LaValle, S. M. (1998) · Cited by 3,704
Self-Organizing Hierarchical Particle Swarm Optimizer With Time-Varying Acceleration Coefficients
Ratnaweera, A., Halgamuge, S. K., & Watson, H. C. (2004) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2004.826071 · Cited by 3,034
Adaptive Particle Swarm Optimization
Zhan, Z.-H., Zhang, J., Li, Y., & Chung, H. S.-H. (2009) · IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) · doi:10.1109/tsmcb.2009.2015956 · Cited by 2,075
A Review of Motion Planning Techniques for Automated Vehicles
Gonzalez, D., Perez, J., Milanes, V., & Nashashibi, F. (2016) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2015.2498841 · Cited by 1,670
Sampling-Based Robot Motion Planning: A Review
Elbanhawi, M., & Simic, M. (2014) · IEEE Access · doi:10.1109/access.2014.2302442 · Cited by 786
Evolutionary Multi-Objective Workflow Scheduling in Cloud
Zhu, Z., Zhang, G., Li, M., & Liu, X. (2016) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2015.2446459 · Cited by 401
A Novel Sigmoid-Function-Based Adaptive Weighted Particle Swarm Optimizer
Liu, W., Wang, Z., Yuan, Y., Zeng, N., Hone, K., & Liu, X. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2019.2925015 · Cited by 346
Self-citation
Cited by
Review of Autonomous Path Planning Algorithms for Mobile Robots
Qin, H., Shao, S., Wang, T., Yu, X., Jiang, Y., & Cao, Z. (2023) · Drones · doi:10.3390/drones7030211 · Cited by 322
An intelligence-based hybrid PSO-SA for mobile robot path planning in warehouse
Lin, S., Liu, A., Wang, J., & Kong, X. (2023) · Journal of Computational Science · doi:10.1016/j.jocs.2022.101938 · Cited by 150
Metadata
- Authors
- Song, B., Wang, Z., & Zou, L.
- Venue
- Applied Soft Computing
- Publisher
- Elsevier BV
- Published
- 2021-03
- Volume
- 100
- Pages
- 106960
- ISSN
- 1568-4946 (ISSN-L)
- OpenAlex
- W3108081931
- Cited by
- 441
- References
- 30
- FWCI
- 21.97
- Type
- journal article
- Language
- en
- Open access
- green
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: An improved PSO algorithm for smooth path planning of mobile robots using continuous high-degree Bezier curve. https://app.cochat.ai/papers/10.1016/j.asoc.2020.106960
- The paper
- Song, B., Wang, Z., & Zou, L. (2021). An improved PSO algorithm for smooth path planning of mobile robots using continuous high-degree Bezier curve. Applied Soft Computing. https://doi.org/10.1016/j.asoc.2020.106960
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.
