Mobile Robot Path Planning Based on Improved Particle Swarm Optimization and Improved Dynamic Window Approach
To enable mobile robots to effectively complete path planning in dynamic environments, a hybrid path planning method based on particle swarm optimization (PSO) and dynamic window approach (DWA) is proposed in this paper. First, an improved particle swarm optimization (IPSO) is proposed to enhance th...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
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| Series: | Journal of Robotics |
| Online Access: | http://dx.doi.org/10.1155/2023/6619841 |
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| _version_ | 1832547942765953024 |
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| author | Zhenjian Yang Ning Li Yunjie Zhang Jin Li |
| author_facet | Zhenjian Yang Ning Li Yunjie Zhang Jin Li |
| author_sort | Zhenjian Yang |
| collection | DOAJ |
| description | To enable mobile robots to effectively complete path planning in dynamic environments, a hybrid path planning method based on particle swarm optimization (PSO) and dynamic window approach (DWA) is proposed in this paper. First, an improved particle swarm optimization (IPSO) is proposed to enhance the exploration capability and search accuracy of the algorithm by improving the velocity update method and inertia weight. Secondly, a particle initialization strategy is used to increase population diversity, and an addressing local optimum strategy is used to make the algorithm overcome the local optimum. Thirdly, a method of selecting navigation points is proposed to guide local path planning. The robot selects the appropriate navigation points as the target points for local path planning based on the position of the robot and the risk of collision with dynamic obstacles. Finally, an improved dynamic window approach (IDWA) is proposed by combining the velocity obstacle (VO) with the DWA, and the evaluation function of the DWA is improved to enhance trajectory tracking and dynamic obstacle avoidance capabilities. The simulation and experimental results show that IPSO has greater exploration capability and search accuracy; IDWA is more effective in trajectory tracking and dynamic obstacle avoidance; and the hybrid algorithm enables the robot to efficiently complete path planning in dynamic environments. |
| format | Article |
| id | doaj-art-b76cd05db6bd4b9486ae7296d3fec313 |
| institution | Kabale University |
| issn | 1687-9619 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Robotics |
| spelling | doaj-art-b76cd05db6bd4b9486ae7296d3fec3132025-02-03T06:42:48ZengWileyJournal of Robotics1687-96192023-01-01202310.1155/2023/6619841Mobile Robot Path Planning Based on Improved Particle Swarm Optimization and Improved Dynamic Window ApproachZhenjian Yang0Ning Li1Yunjie Zhang2Jin Li3School of Computer and Information EngineeringSchool of Computer and Information EngineeringSchool of Computer and Information EngineeringSchool of Computer and Information EngineeringTo enable mobile robots to effectively complete path planning in dynamic environments, a hybrid path planning method based on particle swarm optimization (PSO) and dynamic window approach (DWA) is proposed in this paper. First, an improved particle swarm optimization (IPSO) is proposed to enhance the exploration capability and search accuracy of the algorithm by improving the velocity update method and inertia weight. Secondly, a particle initialization strategy is used to increase population diversity, and an addressing local optimum strategy is used to make the algorithm overcome the local optimum. Thirdly, a method of selecting navigation points is proposed to guide local path planning. The robot selects the appropriate navigation points as the target points for local path planning based on the position of the robot and the risk of collision with dynamic obstacles. Finally, an improved dynamic window approach (IDWA) is proposed by combining the velocity obstacle (VO) with the DWA, and the evaluation function of the DWA is improved to enhance trajectory tracking and dynamic obstacle avoidance capabilities. The simulation and experimental results show that IPSO has greater exploration capability and search accuracy; IDWA is more effective in trajectory tracking and dynamic obstacle avoidance; and the hybrid algorithm enables the robot to efficiently complete path planning in dynamic environments.http://dx.doi.org/10.1155/2023/6619841 |
| spellingShingle | Zhenjian Yang Ning Li Yunjie Zhang Jin Li Mobile Robot Path Planning Based on Improved Particle Swarm Optimization and Improved Dynamic Window Approach Journal of Robotics |
| title | Mobile Robot Path Planning Based on Improved Particle Swarm Optimization and Improved Dynamic Window Approach |
| title_full | Mobile Robot Path Planning Based on Improved Particle Swarm Optimization and Improved Dynamic Window Approach |
| title_fullStr | Mobile Robot Path Planning Based on Improved Particle Swarm Optimization and Improved Dynamic Window Approach |
| title_full_unstemmed | Mobile Robot Path Planning Based on Improved Particle Swarm Optimization and Improved Dynamic Window Approach |
| title_short | Mobile Robot Path Planning Based on Improved Particle Swarm Optimization and Improved Dynamic Window Approach |
| title_sort | mobile robot path planning based on improved particle swarm optimization and improved dynamic window approach |
| url | http://dx.doi.org/10.1155/2023/6619841 |
| work_keys_str_mv | AT zhenjianyang mobilerobotpathplanningbasedonimprovedparticleswarmoptimizationandimproveddynamicwindowapproach AT ningli mobilerobotpathplanningbasedonimprovedparticleswarmoptimizationandimproveddynamicwindowapproach AT yunjiezhang mobilerobotpathplanningbasedonimprovedparticleswarmoptimizationandimproveddynamicwindowapproach AT jinli mobilerobotpathplanningbasedonimprovedparticleswarmoptimizationandimproveddynamicwindowapproach |