Hybrid Strategy Improved Grey Wolf Optimization Algorithm for Plume Tracking and Localization Method in Indoor Weak Wind Environment
Reliable tracking of the plume by the robot is the key to achieving plume source localization. To address the problem of low success rate and long search time of robot source location due to the unavailability of reliable information on gas diffusion flow direction and flow velocity in an indoor wea...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9893818/ |
| _version_ | 1828172247441866752 |
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| author | Xiaoqiang Jin Xiangfeng Zhang Hong Jiang Jiaquan Tian |
| author_facet | Xiaoqiang Jin Xiangfeng Zhang Hong Jiang Jiaquan Tian |
| author_sort | Xiaoqiang Jin |
| collection | DOAJ |
| description | Reliable tracking of the plume by the robot is the key to achieving plume source localization. To address the problem of low success rate and long search time of robot source location due to the unavailability of reliable information on gas diffusion flow direction and flow velocity in an indoor weak wind environment, a hybrid strategy is proposed to improve the grey wolf optimization algorithm for robot plume tracking and location. The plume is modeled using Computational Fluid Dynamics (CFD) in a two-dimensional indoor weak wind environment, and the plume concentration value is used as the individual adaptation degree of the algorithm. Without carrying plume velocity and flow direction sensors, the source-finding robot simulates the grey wolf population’s social mechanism and hunting behavior to update its position. The improved Grey Wolf Optimization algorithm is compared with the traditional Grey Wolf Optimization (GWO), Particle Swarm Algorithm (PSO), and Genetic Algorithm (GA) in simulation experiments. The simulation experiments show that the average number of iterations of the improved GWO is 7, 108, and 118 times shorter than the four source finding algorithms of GWO, PSO, and GA. The average planning path is reduced by 0.91 meters, 2.35meters, and 2.90 meters. 3s, 10.3s, and 9.3s reduce the average running time. The average positioning success rate is improved by 30%, 32%, and 40%. The applicability and Stability of the improved GWO algorithm in solving the plume tracking and localization problem in an indoor weak wind environment are verified. |
| first_indexed | 2024-04-12T03:38:08Z |
| format | Article |
| id | doaj.art-d46befa29263467e86442a7867850616 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T03:38:08Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d46befa29263467e86442a78678506162022-12-22T03:49:22ZengIEEEIEEE Access2169-35362022-01-011010097610098610.1109/ACCESS.2022.32073009893818Hybrid Strategy Improved Grey Wolf Optimization Algorithm for Plume Tracking and Localization Method in Indoor Weak Wind EnvironmentXiaoqiang Jin0https://orcid.org/0000-0003-0958-9571Xiangfeng Zhang1https://orcid.org/0000-0002-5852-7230Hong Jiang2Jiaquan Tian3College of Mechanical Engineering, Xinjiang University, Ürümqi, ChinaCollege of Mechanical Engineering, Xinjiang University, Ürümqi, ChinaCollege of Mechanical Engineering, Xinjiang University, Ürümqi, ChinaCollege of Mechanical Engineering, Xinjiang University, Ürümqi, ChinaReliable tracking of the plume by the robot is the key to achieving plume source localization. To address the problem of low success rate and long search time of robot source location due to the unavailability of reliable information on gas diffusion flow direction and flow velocity in an indoor weak wind environment, a hybrid strategy is proposed to improve the grey wolf optimization algorithm for robot plume tracking and location. The plume is modeled using Computational Fluid Dynamics (CFD) in a two-dimensional indoor weak wind environment, and the plume concentration value is used as the individual adaptation degree of the algorithm. Without carrying plume velocity and flow direction sensors, the source-finding robot simulates the grey wolf population’s social mechanism and hunting behavior to update its position. The improved Grey Wolf Optimization algorithm is compared with the traditional Grey Wolf Optimization (GWO), Particle Swarm Algorithm (PSO), and Genetic Algorithm (GA) in simulation experiments. The simulation experiments show that the average number of iterations of the improved GWO is 7, 108, and 118 times shorter than the four source finding algorithms of GWO, PSO, and GA. The average planning path is reduced by 0.91 meters, 2.35meters, and 2.90 meters. 3s, 10.3s, and 9.3s reduce the average running time. The average positioning success rate is improved by 30%, 32%, and 40%. The applicability and Stability of the improved GWO algorithm in solving the plume tracking and localization problem in an indoor weak wind environment are verified.https://ieeexplore.ieee.org/document/9893818/RobotplumeCFDtracking and positioningindoor weak wind environment |
| spellingShingle | Xiaoqiang Jin Xiangfeng Zhang Hong Jiang Jiaquan Tian Hybrid Strategy Improved Grey Wolf Optimization Algorithm for Plume Tracking and Localization Method in Indoor Weak Wind Environment IEEE Access Robot plume CFD tracking and positioning indoor weak wind environment |
| title | Hybrid Strategy Improved Grey Wolf Optimization Algorithm for Plume Tracking and Localization Method in Indoor Weak Wind Environment |
| title_full | Hybrid Strategy Improved Grey Wolf Optimization Algorithm for Plume Tracking and Localization Method in Indoor Weak Wind Environment |
| title_fullStr | Hybrid Strategy Improved Grey Wolf Optimization Algorithm for Plume Tracking and Localization Method in Indoor Weak Wind Environment |
| title_full_unstemmed | Hybrid Strategy Improved Grey Wolf Optimization Algorithm for Plume Tracking and Localization Method in Indoor Weak Wind Environment |
| title_short | Hybrid Strategy Improved Grey Wolf Optimization Algorithm for Plume Tracking and Localization Method in Indoor Weak Wind Environment |
| title_sort | hybrid strategy improved grey wolf optimization algorithm for plume tracking and localization method in indoor weak wind environment |
| topic | Robot plume CFD tracking and positioning indoor weak wind environment |
| url | https://ieeexplore.ieee.org/document/9893818/ |
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