Research on Path Planning in 3D Complex Environments Based on Improved Ant Colony Algorithm
Aiming at the problems of complex space, long planning time, and insufficient path security of 3D path planning, an improved ant colony algorithm (TGACO) is proposed, which can be used to solve symmetric and asymmetric path planning problems. Firstly, the 3D array is used to access the environment i...
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| Published in | Symmetry (Basel) Vol. 14; no. 9; p. 1917 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Basel
MDPI AG
01.09.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2073-8994 2073-8994 |
| DOI | 10.3390/sym14091917 |
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| Abstract | Aiming at the problems of complex space, long planning time, and insufficient path security of 3D path planning, an improved ant colony algorithm (TGACO) is proposed, which can be used to solve symmetric and asymmetric path planning problems. Firstly, the 3D array is used to access the environment information, which can record the flight environment and avoid the inefficiency of planning. Secondly, a multi-objective function of distance and angle is established to improve the efficiency and safety of the path. Then, a target-guided heuristic function is proposed, and an anti-deadlock mechanism is introduced to improve the efficiency of the ant colony algorithm. Next, the node pheromone update rules are improved to further improve the efficiency of the algorithm. Finally, experiments prove the effectiveness of the improved algorithm, TGACO, and its efficiency in complex environments has obvious advantages. In the 20×20×20 environment, compared with the ant colony algorithm (ACO), the improved algorithm (TGACO) in this paper improves the path length, total turning angle, and running time by 17.8%, 78.4%, and 95.3%, respectively. |
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| AbstractList | Aiming at the problems of complex space, long planning time, and insufficient path security of 3D path planning, an improved ant colony algorithm (TGACO) is proposed, which can be used to solve symmetric and asymmetric path planning problems. Firstly, the 3D array is used to access the environment information, which can record the flight environment and avoid the inefficiency of planning. Secondly, a multi-objective function of distance and angle is established to improve the efficiency and safety of the path. Then, a target-guided heuristic function is proposed, and an anti-deadlock mechanism is introduced to improve the efficiency of the ant colony algorithm. Next, the node pheromone update rules are improved to further improve the efficiency of the algorithm. Finally, experiments prove the effectiveness of the improved algorithm, TGACO, and its efficiency in complex environments has obvious advantages. In the 20×20×20 environment, compared with the ant colony algorithm (ACO), the improved algorithm (TGACO) in this paper improves the path length, total turning angle, and running time by 17.8%, 78.4%, and 95.3%, respectively. |
| Author | Jiang, Ziqi Cai, Fanger Xue, Yuting Li, Weicong Li, Yunchen Zhou, Hang |
| Author_xml | – sequence: 1 givenname: Hang surname: Zhou fullname: Zhou, Hang – sequence: 2 givenname: Ziqi surname: Jiang fullname: Jiang, Ziqi – sequence: 3 givenname: Yuting surname: Xue fullname: Xue, Yuting – sequence: 4 givenname: Weicong orcidid: 0000-0002-4366-4312 surname: Li fullname: Li, Weicong – sequence: 5 givenname: Fanger surname: Cai fullname: Cai, Fanger – sequence: 6 givenname: Yunchen surname: Li fullname: Li, Yunchen |
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| Cites_doi | 10.3389/fnbot.2019.00015 10.1007/978-981-13-6504-1_144 10.1109/ICM46511.2021.9385695 10.1016/j.eswa.2022.116605 10.1016/j.cie.2021.107230 10.1109/TSMCB.2003.821450 10.1002/dac.5090 10.1109/ICPECA51329.2021.9362724 |
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| Copyright | 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| StartPage | 1917 |
| SubjectTerms | Accuracy Algorithms Ant colony optimization Coordinate transformations Design Efficiency Flight operations Path planning Planning Run time (computers) |
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