An Improved Method of Particle Swarm Optimization for Path Planning of Mobile Robot
The existing particle swarm optimization (PSO) algorithm has the disadvantages of application limitations and slow convergence speed when solving the problem of mobile robot path planning. This paper proposes an improved PSO integration scheme based on improved details, which integrates uniform dist...
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| Published in | Journal of control science and engineering Vol. 2020; no. 2020; pp. 1 - 12 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Cairo, Egypt
Hindawi Publishing Corporation
2020
Hindawi John Wiley & Sons, Inc Wiley |
| Subjects | |
| Online Access | Get full text |
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| Abstract | The existing particle swarm optimization (PSO) algorithm has the disadvantages of application limitations and slow convergence speed when solving the problem of mobile robot path planning. This paper proposes an improved PSO integration scheme based on improved details, which integrates uniform distribution, exponential attenuation inertia weight, cubic spline interpolation function, and learning factor of enhanced control. Compared with other standard functions, our improved PSO (IPSO) can achieve better optimal results with less number of iteration steps than the different four path planning algorithms developed in the existing literature. IPSO makes the optimal path length with less than 20 iteration steps and reduces the path length and simulation time by 2.8% and 1.1 seconds, respectively. |
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| AbstractList | The existing particle swarm optimization (PSO) algorithm has the disadvantages of application limitations and slow convergence speed when solving the problem of mobile robot path planning. This paper proposes an improved PSO integration scheme based on improved details, which integrates uniform distribution, exponential attenuation inertia weight, cubic spline interpolation function, and learning factor of enhanced control. Compared with other standard functions, our improved PSO (IPSO) can achieve better optimal results with less number of iteration steps than the different four path planning algorithms developed in the existing literature. IPSO makes the optimal path length with less than 20 iteration steps and reduces the path length and simulation time by 2.8% and 1.1 seconds, respectively. |
| Author | Liping, Ma Wu, Dandan Li, Xun He, Jingjing Bashir, Muhammad |
| Author_xml | – sequence: 1 fullname: Bashir, Muhammad – sequence: 2 fullname: He, Jingjing – sequence: 3 fullname: Wu, Dandan – sequence: 4 fullname: Li, Xun – sequence: 5 fullname: Liping, Ma |
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| CitedBy_id | crossref_primary_10_1016_j_engappai_2023_105898 crossref_primary_10_1002_rob_22466 crossref_primary_10_1109_TGCN_2024_3379960 crossref_primary_10_21595_rsa_2023_23090 crossref_primary_10_1515_ijeeps_2021_0443 crossref_primary_10_1109_TITS_2024_3489018 crossref_primary_10_3390_math10234539 crossref_primary_10_1051_itmconf_20235701003 crossref_primary_10_1109_ACCESS_2021_3112069 crossref_primary_10_1088_1742_6596_1995_1_012044 crossref_primary_10_3390_robotics13100141 crossref_primary_10_3390_en15041358 crossref_primary_10_1109_ACCESS_2024_3389152 crossref_primary_10_3390_robotics13030046 crossref_primary_10_3390_computation12010001 crossref_primary_10_1080_10803548_2024_2444807 crossref_primary_10_3390_app12178392 crossref_primary_10_3390_electronics11152339 crossref_primary_10_1155_2022_3755754 crossref_primary_10_1109_ACCESS_2024_3362638 crossref_primary_10_3103_S014641162301008X crossref_primary_10_1016_j_jjimei_2022_100086 crossref_primary_10_1007_s42405_022_00461_8 |
| Cites_doi | 10.1016/j.ejor.2019.01.007 10.1109/tie.2018.2874587 10.1016/j.robot.2019.02.002 10.1155/2018/3702916 10.1007/s13042-017-0703-7 10.3724/sp.j.1087.2013.02265 10.3390/app9040672 10.1016/j.asoc.2012.11.033 10.1155/2016/7426913 10.1016/j.compag.2018.10.031 10.1177/1729881419859083 10.1007/s11277-019-06180-w 10.3934/mbe.2019113 10.1177/0142331218824393 10.1108/aa-03-2016-024 |
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| Copyright | Copyright © 2020 Xun Li et al. Copyright © 2020 Xun Li et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. http://creativecommons.org/licenses/by/4.0 |
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| SubjectTerms | Algorithms Attenuation Computer simulation Efficiency Interpolation Methods Optimization algorithms Optimization techniques Particle swarm optimization Path planning Robots |
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| Title | An Improved Method of Particle Swarm Optimization for Path Planning of Mobile Robot |
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