Mobile Robot Path Planning Based on Time Taboo Ant Colony Optimization in Dynamic Environment
This article aims to improve the problem of slow convergence speed, poor global search ability, and unknown time-varying dynamic obstacles in the path planning of ant colony optimization in dynamic environment. An improved ant colony optimization algorithm using time taboo strategy is proposed, name...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-03-01
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| Series: | Frontiers in Neurorobotics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnbot.2021.642733/full |
| _version_ | 1818347986740051968 |
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| author | Ni Xiong Xinzhi Zhou Xiuqing Yang Xiuqing Yang Yong Xiang Yong Xiang Junyong Ma Junyong Ma |
| author_facet | Ni Xiong Xinzhi Zhou Xiuqing Yang Xiuqing Yang Yong Xiang Yong Xiang Junyong Ma Junyong Ma |
| author_sort | Ni Xiong |
| collection | DOAJ |
| description | This article aims to improve the problem of slow convergence speed, poor global search ability, and unknown time-varying dynamic obstacles in the path planning of ant colony optimization in dynamic environment. An improved ant colony optimization algorithm using time taboo strategy is proposed, namely, time taboo ant colony optimization (TTACO), which uses adaptive initial pheromone distribution, rollback strategy, and pheromone preferential limited update to improve the algorithm's convergence speed and global search ability. For the poor global search ability of the algorithm and the unknown time-varying problem of dynamic obstacles in a dynamic environment, a time taboo strategy is first proposed, based on which a three-step arbitration method is put forward to improve its weakness in global search. For the unknown time-varying dynamic obstacles, an occupancy grid prediction model is proposed based on the time taboo strategy to solve the problem of dynamic obstacle avoidance. In order to improve the algorithm's calculation speed when avoiding obstacles, an ant colony information inheritance mechanism is established. Finally, the algorithm is used to conduct dynamic simulation experiments in a simulated factory environment and is compared with other similar algorithms. The experimental results show that the TTACO can obtain a better path and accelerate the convergence speed of the algorithm in a static environment and can successfully avoid dynamic obstacles in a dynamic environment. |
| first_indexed | 2024-12-13T17:42:53Z |
| format | Article |
| id | doaj.art-808b948c91d746d2a7a152d2adff7ca8 |
| institution | Directory Open Access Journal |
| issn | 1662-5218 |
| language | English |
| last_indexed | 2024-12-13T17:42:53Z |
| publishDate | 2021-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurorobotics |
| spelling | doaj.art-808b948c91d746d2a7a152d2adff7ca82022-12-21T23:36:42ZengFrontiers Media S.A.Frontiers in Neurorobotics1662-52182021-03-011510.3389/fnbot.2021.642733642733Mobile Robot Path Planning Based on Time Taboo Ant Colony Optimization in Dynamic EnvironmentNi Xiong0Xinzhi Zhou1Xiuqing Yang2Xiuqing Yang3Yong Xiang4Yong Xiang5Junyong Ma6Junyong Ma7College of Electronics and Information Engineering, Sichuan University, Chengdu, ChinaCollege of Electronics and Information Engineering, Sichuan University, Chengdu, ChinaThe Second Research Institute of Civil Aviation Administration of China, Chengdu, ChinaCivil Aviation Logistics Technology Company Limited, Chengdu, ChinaThe Second Research Institute of Civil Aviation Administration of China, Chengdu, ChinaCivil Aviation Logistics Technology Company Limited, Chengdu, ChinaThe Second Research Institute of Civil Aviation Administration of China, Chengdu, ChinaCivil Aviation Logistics Technology Company Limited, Chengdu, ChinaThis article aims to improve the problem of slow convergence speed, poor global search ability, and unknown time-varying dynamic obstacles in the path planning of ant colony optimization in dynamic environment. An improved ant colony optimization algorithm using time taboo strategy is proposed, namely, time taboo ant colony optimization (TTACO), which uses adaptive initial pheromone distribution, rollback strategy, and pheromone preferential limited update to improve the algorithm's convergence speed and global search ability. For the poor global search ability of the algorithm and the unknown time-varying problem of dynamic obstacles in a dynamic environment, a time taboo strategy is first proposed, based on which a three-step arbitration method is put forward to improve its weakness in global search. For the unknown time-varying dynamic obstacles, an occupancy grid prediction model is proposed based on the time taboo strategy to solve the problem of dynamic obstacle avoidance. In order to improve the algorithm's calculation speed when avoiding obstacles, an ant colony information inheritance mechanism is established. Finally, the algorithm is used to conduct dynamic simulation experiments in a simulated factory environment and is compared with other similar algorithms. The experimental results show that the TTACO can obtain a better path and accelerate the convergence speed of the algorithm in a static environment and can successfully avoid dynamic obstacles in a dynamic environment.https://www.frontiersin.org/articles/10.3389/fnbot.2021.642733/fullpath planningmobile robotant colony algorithmdynamic environmenttime taboo strategy |
| spellingShingle | Ni Xiong Xinzhi Zhou Xiuqing Yang Xiuqing Yang Yong Xiang Yong Xiang Junyong Ma Junyong Ma Mobile Robot Path Planning Based on Time Taboo Ant Colony Optimization in Dynamic Environment Frontiers in Neurorobotics path planning mobile robot ant colony algorithm dynamic environment time taboo strategy |
| title | Mobile Robot Path Planning Based on Time Taboo Ant Colony Optimization in Dynamic Environment |
| title_full | Mobile Robot Path Planning Based on Time Taboo Ant Colony Optimization in Dynamic Environment |
| title_fullStr | Mobile Robot Path Planning Based on Time Taboo Ant Colony Optimization in Dynamic Environment |
| title_full_unstemmed | Mobile Robot Path Planning Based on Time Taboo Ant Colony Optimization in Dynamic Environment |
| title_short | Mobile Robot Path Planning Based on Time Taboo Ant Colony Optimization in Dynamic Environment |
| title_sort | mobile robot path planning based on time taboo ant colony optimization in dynamic environment |
| topic | path planning mobile robot ant colony algorithm dynamic environment time taboo strategy |
| url | https://www.frontiersin.org/articles/10.3389/fnbot.2021.642733/full |
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