Multi-scale Map Path Planning Based on Fuzzy Logic Genetic Ant Colony Optimization

Siyuan Yang; Dongguang Li; Yuze Wang; Yue Wang

doi:10.1007/978-981-97-1099-7_40

Multi-scale Map Path Planning Based on Fuzzy Logic Genetic Ant Colony Optimization

Siyuan Yang
, Dongguang Li
, Yuze Wang
, Yue Wang^*

^*此作品的通讯作者

机电学院

Beijing Institute of Technology

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

摘要

Path planning is a core to improve the autonomy of Unmanned Ground Vehicle (UGV). In autonomous navigation applications, the use of Ant Colony Optimization (ACO) in solving the path planning problem is difficult to obtain the global optimal solution, which make the waste of resources. Focus on fast optimization search, this paper proposes Fuzzy Logic Genetic Ant Colony Optimization (FLGACO), which adopt crossover and mutation operations in genetic algorithms. By using the fuzzy logic system, dynamic adjustment for pheromone and heuristic values can be realized. Simulation experiments on path planning for fast arrival were conducted using ACO,GA and FLGACO under the same map. The results show that FLGACO reduces the path length by 15% compared to ACO and 9% compared to genetic algorithm, which can effectively reduce the energy consumption and verify the feasibility and effectiveness of the improved method.

源语言	英语
主期刊名	Proceedings of 3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023 - Volume 6
编辑	Yi Qu, Mancang Gu, Yifeng Niu, Wenxing Fu
出版商	Springer Science and Business Media Deutschland GmbH
页	418-429
页数	12
ISBN（印刷版）	9789819710980
DOI	https://doi.org/10.1007/978-981-97-1099-7_40
出版状态	已出版 - 2024
活动	3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023 - Nanjing, 中国期限: 9 9月 2023 → 11 9月 2023

丛书

姓名	Lecture Notes in Electrical Engineering
卷	1176 LNEE
ISSN（印刷版）	1876-1100
ISSN（电子版）	1876-1119

会议

会议	3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023
国家/地区	中国
市	Nanjing
时期	9/09/23 → 11/09/23

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

可持续发展目标 7 经济适用的清洁能源

访问文件

10.1007/978-981-97-1099-7_40

其它文件与链接

链接到 Scopus 的出版物

引用此

Yang, S., Li, D., Wang, Y., & Wang, Y. (2024). Multi-scale Map Path Planning Based on Fuzzy Logic Genetic Ant Colony Optimization. 在 Y. Qu, M. Gu, Y. Niu, & W. Fu (版本), Proceedings of 3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023 - Volume 6 (页码 418-429). (Lecture Notes in Electrical Engineering; 卷 1176 LNEE). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-97-1099-7_40

Yang, Siyuan ; Li, Dongguang ; Wang, Yuze 等. / Multi-scale Map Path Planning Based on Fuzzy Logic Genetic Ant Colony Optimization. Proceedings of 3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023 - Volume 6. 编辑 / Yi Qu ; Mancang Gu ; Yifeng Niu ; Wenxing Fu. Springer Science and Business Media Deutschland GmbH, 2024. 页码 418-429 (Lecture Notes in Electrical Engineering).

@inproceedings{8f3d0b0813464fe185c6bbe4b31d0c7e,

title = "Multi-scale Map Path Planning Based on Fuzzy Logic Genetic Ant Colony Optimization",

abstract = "Path planning is a core to improve the autonomy of Unmanned Ground Vehicle (UGV). In autonomous navigation applications, the use of Ant Colony Optimization (ACO) in solving the path planning problem is difficult to obtain the global optimal solution, which make the waste of resources. Focus on fast optimization search, this paper proposes Fuzzy Logic Genetic Ant Colony Optimization (FLGACO), which adopt crossover and mutation operations in genetic algorithms. By using the fuzzy logic system, dynamic adjustment for pheromone and heuristic values can be realized. Simulation experiments on path planning for fast arrival were conducted using ACO,GA and FLGACO under the same map. The results show that FLGACO reduces the path length by 15\% compared to ACO and 9\% compared to genetic algorithm, which can effectively reduce the energy consumption and verify the feasibility and effectiveness of the improved method.",

keywords = "Ant Colony Optimization, Fuzzy Logic, Mobile Robot, Path Planning",

author = "Siyuan Yang and Dongguang Li and Yuze Wang and Yue Wang",

note = "Publisher Copyright: {\textcopyright} Beijing HIWING Scientific and Technological Information Institute 2024.; 3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023 ; Conference date: 09-09-2023 Through 11-09-2023",

year = "2024",

doi = "10.1007/978-981-97-1099-7\_40",

language = "English",

isbn = "9789819710980",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "418--429",

editor = "Yi Qu and Mancang Gu and Yifeng Niu and Wenxing Fu",

booktitle = "Proceedings of 3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023 - Volume 6",

address = "Germany",

}

Yang, S, Li, D, Wang, Y & Wang, Y 2024, Multi-scale Map Path Planning Based on Fuzzy Logic Genetic Ant Colony Optimization. 在 Y Qu, M Gu, Y Niu & W Fu (版本), Proceedings of 3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023 - Volume 6. Lecture Notes in Electrical Engineering, 卷 1176 LNEE, Springer Science and Business Media Deutschland GmbH, 页码 418-429, 3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023, Nanjing, 中国, 9/09/23. https://doi.org/10.1007/978-981-97-1099-7_40

Multi-scale Map Path Planning Based on Fuzzy Logic Genetic Ant Colony Optimization. / Yang, Siyuan; Li, Dongguang; Wang, Yuze 等.
Proceedings of 3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023 - Volume 6. 编辑 / Yi Qu; Mancang Gu; Yifeng Niu; Wenxing Fu. Springer Science and Business Media Deutschland GmbH, 2024. 页码 418-429 (Lecture Notes in Electrical Engineering; 卷 1176 LNEE).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Multi-scale Map Path Planning Based on Fuzzy Logic Genetic Ant Colony Optimization

AU - Yang, Siyuan

AU - Li, Dongguang

AU - Wang, Yuze

AU - Wang, Yue

N1 - Publisher Copyright: © Beijing HIWING Scientific and Technological Information Institute 2024.

PY - 2024

Y1 - 2024

N2 - Path planning is a core to improve the autonomy of Unmanned Ground Vehicle (UGV). In autonomous navigation applications, the use of Ant Colony Optimization (ACO) in solving the path planning problem is difficult to obtain the global optimal solution, which make the waste of resources. Focus on fast optimization search, this paper proposes Fuzzy Logic Genetic Ant Colony Optimization (FLGACO), which adopt crossover and mutation operations in genetic algorithms. By using the fuzzy logic system, dynamic adjustment for pheromone and heuristic values can be realized. Simulation experiments on path planning for fast arrival were conducted using ACO,GA and FLGACO under the same map. The results show that FLGACO reduces the path length by 15% compared to ACO and 9% compared to genetic algorithm, which can effectively reduce the energy consumption and verify the feasibility and effectiveness of the improved method.

AB - Path planning is a core to improve the autonomy of Unmanned Ground Vehicle (UGV). In autonomous navigation applications, the use of Ant Colony Optimization (ACO) in solving the path planning problem is difficult to obtain the global optimal solution, which make the waste of resources. Focus on fast optimization search, this paper proposes Fuzzy Logic Genetic Ant Colony Optimization (FLGACO), which adopt crossover and mutation operations in genetic algorithms. By using the fuzzy logic system, dynamic adjustment for pheromone and heuristic values can be realized. Simulation experiments on path planning for fast arrival were conducted using ACO,GA and FLGACO under the same map. The results show that FLGACO reduces the path length by 15% compared to ACO and 9% compared to genetic algorithm, which can effectively reduce the energy consumption and verify the feasibility and effectiveness of the improved method.

KW - Ant Colony Optimization

KW - Fuzzy Logic

KW - Mobile Robot

KW - Path Planning

UR - https://www.scopus.com/pages/publications/85192874277

U2 - 10.1007/978-981-97-1099-7_40

DO - 10.1007/978-981-97-1099-7_40

M3 - Conference contribution

AN - SCOPUS:85192874277

SN - 9789819710980

T3 - Lecture Notes in Electrical Engineering

SP - 418

EP - 429

BT - Proceedings of 3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023 - Volume 6

A2 - Qu, Yi

A2 - Gu, Mancang

A2 - Niu, Yifeng

A2 - Fu, Wenxing

PB - Springer Science and Business Media Deutschland GmbH

T2 - 3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023

Y2 - 9 September 2023 through 11 September 2023

ER -

Yang S, Li D, Wang Y, Wang Y. Multi-scale Map Path Planning Based on Fuzzy Logic Genetic Ant Colony Optimization. 在 Qu Y, Gu M, Niu Y, Fu W, 编辑, Proceedings of 3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023 - Volume 6. Springer Science and Business Media Deutschland GmbH. 2024. 页码 418-429. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-97-1099-7_40

Multi-scale Map Path Planning Based on Fuzzy Logic Genetic Ant Colony Optimization

摘要

丛书

会议

联合国可持续发展目标

访问文件

其它文件与链接

学术指纹

引用此