Construction of Novel Self-Adaptive Dynamic Window Approach Combined With Fuzzy Neural Network in Complex Dynamic Environments
The traditional Dynamic Window Approach (DWA) with constant weight values of the evaluation function leads to the inability of obstacle avoidance for the Automated Guided Vehicles (AGV) to perform obstacle avoidance and path planning in the complex environment. Effective avoidance of complex obstacl...
| 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/9904534/ |
| _version_ | 1797996669558587392 |
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| author | Dian Yang Chen Su Hang Wu Xinxi Xu Xiuguo Zhao |
| author_facet | Dian Yang Chen Su Hang Wu Xinxi Xu Xiuguo Zhao |
| author_sort | Dian Yang |
| collection | DOAJ |
| description | The traditional Dynamic Window Approach (DWA) with constant weight values of the evaluation function leads to the inability of obstacle avoidance for the Automated Guided Vehicles (AGV) to perform obstacle avoidance and path planning in the complex environment. Effective avoidance of complex obstacles requires adaptive weight adjustment to address the evaluation function’s challenges. This paper proposes an adaptive DWA (ADWA), which introduces neural network training on the basis of the Mamdani DWA (MDWA). Firstly, the Mamdani type fuzzy controller is designed, and then the adaptive neuro-fuzzy controller is obtained by neural network training. Then, experiments are carried out through the MATLAB simulation environment. The simulation experiment results show that the improved DWA compared to traditional DWA can make the AGV pass the obstacle environment with a better trajectory and reduce the time. The improved DWA improves the autonomous obstacle avoidance capability of AGVs, which not only perfectly fits our task requirements, but also has apparent scientific and practical significance in developing AGV autonomous obstacle avoidance technology. |
| first_indexed | 2024-04-11T10:19:59Z |
| format | Article |
| id | doaj.art-eaaa14c2ad354cffa2f60d2980d99787 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T10:19:59Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-eaaa14c2ad354cffa2f60d2980d997872022-12-22T04:29:47ZengIEEEIEEE Access2169-35362022-01-011010437510438310.1109/ACCESS.2022.32102519904534Construction of Novel Self-Adaptive Dynamic Window Approach Combined With Fuzzy Neural Network in Complex Dynamic EnvironmentsDian Yang0https://orcid.org/0000-0003-2200-9818Chen Su1Hang Wu2https://orcid.org/0000-0002-6000-8026Xinxi Xu3Xiuguo Zhao4Institute of Medical Support Technology, Academy of System Engineering, Academy of Military Sciences, Tianjin, ChinaInstitute of Medical Support Technology, Academy of System Engineering, Academy of Military Sciences, Tianjin, ChinaInstitute of Medical Support Technology, Academy of System Engineering, Academy of Military Sciences, Tianjin, ChinaInstitute of Medical Support Technology, Academy of System Engineering, Academy of Military Sciences, Tianjin, ChinaInstitute of Medical Support Technology, Academy of System Engineering, Academy of Military Sciences, Tianjin, ChinaThe traditional Dynamic Window Approach (DWA) with constant weight values of the evaluation function leads to the inability of obstacle avoidance for the Automated Guided Vehicles (AGV) to perform obstacle avoidance and path planning in the complex environment. Effective avoidance of complex obstacles requires adaptive weight adjustment to address the evaluation function’s challenges. This paper proposes an adaptive DWA (ADWA), which introduces neural network training on the basis of the Mamdani DWA (MDWA). Firstly, the Mamdani type fuzzy controller is designed, and then the adaptive neuro-fuzzy controller is obtained by neural network training. Then, experiments are carried out through the MATLAB simulation environment. The simulation experiment results show that the improved DWA compared to traditional DWA can make the AGV pass the obstacle environment with a better trajectory and reduce the time. The improved DWA improves the autonomous obstacle avoidance capability of AGVs, which not only perfectly fits our task requirements, but also has apparent scientific and practical significance in developing AGV autonomous obstacle avoidance technology.https://ieeexplore.ieee.org/document/9904534/Fuzzy controldynamic window approachneural networkautomatic guided vehicle |
| spellingShingle | Dian Yang Chen Su Hang Wu Xinxi Xu Xiuguo Zhao Construction of Novel Self-Adaptive Dynamic Window Approach Combined With Fuzzy Neural Network in Complex Dynamic Environments IEEE Access Fuzzy control dynamic window approach neural network automatic guided vehicle |
| title | Construction of Novel Self-Adaptive Dynamic Window Approach Combined With Fuzzy Neural Network in Complex Dynamic Environments |
| title_full | Construction of Novel Self-Adaptive Dynamic Window Approach Combined With Fuzzy Neural Network in Complex Dynamic Environments |
| title_fullStr | Construction of Novel Self-Adaptive Dynamic Window Approach Combined With Fuzzy Neural Network in Complex Dynamic Environments |
| title_full_unstemmed | Construction of Novel Self-Adaptive Dynamic Window Approach Combined With Fuzzy Neural Network in Complex Dynamic Environments |
| title_short | Construction of Novel Self-Adaptive Dynamic Window Approach Combined With Fuzzy Neural Network in Complex Dynamic Environments |
| title_sort | construction of novel self adaptive dynamic window approach combined with fuzzy neural network in complex dynamic environments |
| topic | Fuzzy control dynamic window approach neural network automatic guided vehicle |
| url | https://ieeexplore.ieee.org/document/9904534/ |
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