Local Path Planning with Multiple Constraints for USV Based on Improved Bacterial Foraging Optimization Algorithm
The quality of unmanned surface vehicle (USV) local path planning directly affects its safety and autonomy performance. The USV local path planning might easily be trapped into local optima. The swarm intelligence optimization algorithm is a novel and effective method to solve the path-planning prob...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-02-01
|
| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/11/3/489 |
| _version_ | 1797610918322896896 |
|---|---|
| author | Yang Long Song Liu Da Qiu Changzhen Li Xuan Guo Binghua Shi Mahmoud S. AbouOmar |
| author_facet | Yang Long Song Liu Da Qiu Changzhen Li Xuan Guo Binghua Shi Mahmoud S. AbouOmar |
| author_sort | Yang Long |
| collection | DOAJ |
| description | The quality of unmanned surface vehicle (USV) local path planning directly affects its safety and autonomy performance. The USV local path planning might easily be trapped into local optima. The swarm intelligence optimization algorithm is a novel and effective method to solve the path-planning problem. Aiming to address this problem, a hybrid bacterial foraging optimization algorithm with a simulated annealing mechanism is proposed. The proposed algorithm preserves a three-layer nested structure, and a simulated annealing mechanism is incorporated into the outermost nested dispersal operator. The proposed algorithm can effectively escape the local optima. Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) rules and dynamic obstacles are considered as the constraints for the proposed algorithm to design different obstacle avoidance strategies for USVs. The coastal port is selected as the working environment of the USV in the visual test platform. The experimental results show the USV can successfully avoid the various obstacles in the coastal port, and efficiently plan collision-free paths. |
| first_indexed | 2024-03-11T06:21:40Z |
| format | Article |
| id | doaj.art-f48bded36e11416e8c9a7d7aa25130c1 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-11T06:21:40Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-f48bded36e11416e8c9a7d7aa25130c12023-11-17T11:56:21ZengMDPI AGJournal of Marine Science and Engineering2077-13122023-02-0111348910.3390/jmse11030489Local Path Planning with Multiple Constraints for USV Based on Improved Bacterial Foraging Optimization AlgorithmYang Long0Song Liu1Da Qiu2Changzhen Li3Xuan Guo4Binghua Shi5Mahmoud S. AbouOmar6School of Intelligent Systems Science and Engineering, Hubei Minzu University, Enshi 445000, ChinaSchool of Intelligent Systems Science and Engineering, Hubei Minzu University, Enshi 445000, ChinaSchool of Intelligent Systems Science and Engineering, Hubei Minzu University, Enshi 445000, ChinaSchool of Information Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Automation, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Information Engineering, Hubei University of Economics, Wuhan 430205, ChinaIndustrial Electronics and Control Engineering Department, Faculty of Electronic Engineering, Menoufia University, Shibin el Kom 32952, EgyptThe quality of unmanned surface vehicle (USV) local path planning directly affects its safety and autonomy performance. The USV local path planning might easily be trapped into local optima. The swarm intelligence optimization algorithm is a novel and effective method to solve the path-planning problem. Aiming to address this problem, a hybrid bacterial foraging optimization algorithm with a simulated annealing mechanism is proposed. The proposed algorithm preserves a three-layer nested structure, and a simulated annealing mechanism is incorporated into the outermost nested dispersal operator. The proposed algorithm can effectively escape the local optima. Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) rules and dynamic obstacles are considered as the constraints for the proposed algorithm to design different obstacle avoidance strategies for USVs. The coastal port is selected as the working environment of the USV in the visual test platform. The experimental results show the USV can successfully avoid the various obstacles in the coastal port, and efficiently plan collision-free paths.https://www.mdpi.com/2077-1312/11/3/489unmanned surface vehiclelocal path planningCOLREGsbacterial foraging algorithmsimulated annealing algorithm |
| spellingShingle | Yang Long Song Liu Da Qiu Changzhen Li Xuan Guo Binghua Shi Mahmoud S. AbouOmar Local Path Planning with Multiple Constraints for USV Based on Improved Bacterial Foraging Optimization Algorithm Journal of Marine Science and Engineering unmanned surface vehicle local path planning COLREGs bacterial foraging algorithm simulated annealing algorithm |
| title | Local Path Planning with Multiple Constraints for USV Based on Improved Bacterial Foraging Optimization Algorithm |
| title_full | Local Path Planning with Multiple Constraints for USV Based on Improved Bacterial Foraging Optimization Algorithm |
| title_fullStr | Local Path Planning with Multiple Constraints for USV Based on Improved Bacterial Foraging Optimization Algorithm |
| title_full_unstemmed | Local Path Planning with Multiple Constraints for USV Based on Improved Bacterial Foraging Optimization Algorithm |
| title_short | Local Path Planning with Multiple Constraints for USV Based on Improved Bacterial Foraging Optimization Algorithm |
| title_sort | local path planning with multiple constraints for usv based on improved bacterial foraging optimization algorithm |
| topic | unmanned surface vehicle local path planning COLREGs bacterial foraging algorithm simulated annealing algorithm |
| url | https://www.mdpi.com/2077-1312/11/3/489 |
| work_keys_str_mv | AT yanglong localpathplanningwithmultipleconstraintsforusvbasedonimprovedbacterialforagingoptimizationalgorithm AT songliu localpathplanningwithmultipleconstraintsforusvbasedonimprovedbacterialforagingoptimizationalgorithm AT daqiu localpathplanningwithmultipleconstraintsforusvbasedonimprovedbacterialforagingoptimizationalgorithm AT changzhenli localpathplanningwithmultipleconstraintsforusvbasedonimprovedbacterialforagingoptimizationalgorithm AT xuanguo localpathplanningwithmultipleconstraintsforusvbasedonimprovedbacterialforagingoptimizationalgorithm AT binghuashi localpathplanningwithmultipleconstraintsforusvbasedonimprovedbacterialforagingoptimizationalgorithm AT mahmoudsabouomar localpathplanningwithmultipleconstraintsforusvbasedonimprovedbacterialforagingoptimizationalgorithm |