The Full-Field Path Tracking of Agricultural Machinery Based on PSO-Enhanced Fuzzy Stanley Model
The unmanned operation of agriculture machinery in the full field of farmland is an important part of unmanned farm and smart agriculture. Although the autonomous navigation for agriculture robot has been widely studied in literature, research on the full-field path tracking problem of agriculture m...
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| Format: | Article |
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MDPI AG
2022-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/15/7683 |
| _version_ | 1827626005970288640 |
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| author | Yu Sun Bingbo Cui Feng Ji Xinhua Wei Yongyun Zhu |
| author_facet | Yu Sun Bingbo Cui Feng Ji Xinhua Wei Yongyun Zhu |
| author_sort | Yu Sun |
| collection | DOAJ |
| description | The unmanned operation of agriculture machinery in the full field of farmland is an important part of unmanned farm and smart agriculture. Although the autonomous navigation for agriculture robot has been widely studied in literature, research on the full-field path tracking problem of agriculture machinery is rare. In this paper, in order to enhance the adaptivity of path tracking algorithm, an improved fuzzy Stanley model (SM) is proposed based on particle swarm optimization (PSO), where the control gain is modified adaptively according to the tracking error, velocity and steering actuator saturation. The PSO-enhanced fuzzy SM (PSO-FSM) is verified by experiments on numerical simulation and self-driving of mobile vehicle. Simulation results indicate that the PSO-FSM achieves a better result than SM and FSM, where PSO-FSM changes the control gain adaptively under different velocities and actuator saturation conditions, and the maximum lateral errors of SM and PSO-FSM for mobile vehicle autonomous turning are 0. 32 m and 0.03 m, respectively. When the location of the mobile vehicle deviates from the expected path at 4 m in a lateral direction, the distance of the guided trajectory for the mobile vehicle to reach the expected path is no more than 5 m. A preliminary experiment is also carried out for a wheeled combine harvester working on slippery soil, and the result indicates that the maximum lateral tracking error of PSO-FSM is 0.63 m, which is acceptable for the path tracking of a combine harvester with a large operation width. |
| first_indexed | 2024-03-09T12:47:44Z |
| format | Article |
| id | doaj.art-35b5004308ba49ad837842f60fef974e |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T12:47:44Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-35b5004308ba49ad837842f60fef974e2023-11-30T22:10:34ZengMDPI AGApplied Sciences2076-34172022-07-011215768310.3390/app12157683The Full-Field Path Tracking of Agricultural Machinery Based on PSO-Enhanced Fuzzy Stanley ModelYu Sun0Bingbo Cui1Feng Ji2Xinhua Wei3Yongyun Zhu4School of Agriculture Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Agriculture Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Agriculture Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Agriculture Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Agriculture Engineering, Jiangsu University, Zhenjiang 212013, ChinaThe unmanned operation of agriculture machinery in the full field of farmland is an important part of unmanned farm and smart agriculture. Although the autonomous navigation for agriculture robot has been widely studied in literature, research on the full-field path tracking problem of agriculture machinery is rare. In this paper, in order to enhance the adaptivity of path tracking algorithm, an improved fuzzy Stanley model (SM) is proposed based on particle swarm optimization (PSO), where the control gain is modified adaptively according to the tracking error, velocity and steering actuator saturation. The PSO-enhanced fuzzy SM (PSO-FSM) is verified by experiments on numerical simulation and self-driving of mobile vehicle. Simulation results indicate that the PSO-FSM achieves a better result than SM and FSM, where PSO-FSM changes the control gain adaptively under different velocities and actuator saturation conditions, and the maximum lateral errors of SM and PSO-FSM for mobile vehicle autonomous turning are 0. 32 m and 0.03 m, respectively. When the location of the mobile vehicle deviates from the expected path at 4 m in a lateral direction, the distance of the guided trajectory for the mobile vehicle to reach the expected path is no more than 5 m. A preliminary experiment is also carried out for a wheeled combine harvester working on slippery soil, and the result indicates that the maximum lateral tracking error of PSO-FSM is 0.63 m, which is acceptable for the path tracking of a combine harvester with a large operation width.https://www.mdpi.com/2076-3417/12/15/7683agriculture machineryautonomous navigationadaptive path trackingStanley model |
| spellingShingle | Yu Sun Bingbo Cui Feng Ji Xinhua Wei Yongyun Zhu The Full-Field Path Tracking of Agricultural Machinery Based on PSO-Enhanced Fuzzy Stanley Model Applied Sciences agriculture machinery autonomous navigation adaptive path tracking Stanley model |
| title | The Full-Field Path Tracking of Agricultural Machinery Based on PSO-Enhanced Fuzzy Stanley Model |
| title_full | The Full-Field Path Tracking of Agricultural Machinery Based on PSO-Enhanced Fuzzy Stanley Model |
| title_fullStr | The Full-Field Path Tracking of Agricultural Machinery Based on PSO-Enhanced Fuzzy Stanley Model |
| title_full_unstemmed | The Full-Field Path Tracking of Agricultural Machinery Based on PSO-Enhanced Fuzzy Stanley Model |
| title_short | The Full-Field Path Tracking of Agricultural Machinery Based on PSO-Enhanced Fuzzy Stanley Model |
| title_sort | full field path tracking of agricultural machinery based on pso enhanced fuzzy stanley model |
| topic | agriculture machinery autonomous navigation adaptive path tracking Stanley model |
| url | https://www.mdpi.com/2076-3417/12/15/7683 |
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