A Data-Driven Dynamic Obstacle Avoidance Method for Liquid-Carrying Plant Protection UAVs
Autonomous sprayer UAVs are one of the most used aerial machines in modern agriculture. During flight missions, some common narrow obstacles appear in the flying zone. These are non-detectable from satellite images and one of the biggest challenges for autonomous sprayer UAVs in farmland. This work...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/12/4/873 |
| _version_ | 1797437323421417472 |
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| author | Shibbir Ahmed Baijing Qiu Chun-Wei Kong Huang Xin Fiaz Ahmad Jinlong Lin |
| author_facet | Shibbir Ahmed Baijing Qiu Chun-Wei Kong Huang Xin Fiaz Ahmad Jinlong Lin |
| author_sort | Shibbir Ahmed |
| collection | DOAJ |
| description | Autonomous sprayer UAVs are one of the most used aerial machines in modern agriculture. During flight missions, some common narrow obstacles appear in the flying zone. These are non-detectable from satellite images and one of the biggest challenges for autonomous sprayer UAVs in farmland. This work introduces an obstacle avoidance architecture specifically for sprayer UAVs. This architecture has generality in the spraying UAV problem, and it reduces the reliance on the global mapping of farmland. This approach computes the avoiding path based on the onboard sensor fusion system in real-time. Moreover, it autonomously determines the transition of several maneuver states using the current spraying liquid data and the UAV dynamics data obtained by offline system identification. This approach accurately tracks the avoidance path for the nonlinear time-variant spraying UAV systems. To verify the performance of the approach, we performed multiple simulations with different spraying missions, and the method demonstrated a high spraying coverage of more than 98% while successfully avoiding all vertical obstacles. We also demonstrated the adaptability of our control architecture; the safe distance between the UAV and obstacles can be changed by specifying the value of a high-level parameter on the controller. The proposed method adds value to precision agriculture, reduces mission time, and maximizes the spraying area coverage. |
| first_indexed | 2024-03-09T11:17:34Z |
| format | Article |
| id | doaj.art-b73a79f1fa444fd294fcf7ccf9c37931 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-09T11:17:34Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-b73a79f1fa444fd294fcf7ccf9c379312023-12-01T00:27:25ZengMDPI AGAgronomy2073-43952022-04-0112487310.3390/agronomy12040873A Data-Driven Dynamic Obstacle Avoidance Method for Liquid-Carrying Plant Protection UAVsShibbir Ahmed0Baijing Qiu1Chun-Wei Kong2Huang Xin3Fiaz Ahmad4Jinlong Lin5School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Aerospace Engineering, University of Michigan, Ann Arbor, MI 1320, USASchool of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, ChinaAutonomous sprayer UAVs are one of the most used aerial machines in modern agriculture. During flight missions, some common narrow obstacles appear in the flying zone. These are non-detectable from satellite images and one of the biggest challenges for autonomous sprayer UAVs in farmland. This work introduces an obstacle avoidance architecture specifically for sprayer UAVs. This architecture has generality in the spraying UAV problem, and it reduces the reliance on the global mapping of farmland. This approach computes the avoiding path based on the onboard sensor fusion system in real-time. Moreover, it autonomously determines the transition of several maneuver states using the current spraying liquid data and the UAV dynamics data obtained by offline system identification. This approach accurately tracks the avoidance path for the nonlinear time-variant spraying UAV systems. To verify the performance of the approach, we performed multiple simulations with different spraying missions, and the method demonstrated a high spraying coverage of more than 98% while successfully avoiding all vertical obstacles. We also demonstrated the adaptability of our control architecture; the safe distance between the UAV and obstacles can be changed by specifying the value of a high-level parameter on the controller. The proposed method adds value to precision agriculture, reduces mission time, and maximizes the spraying area coverage.https://www.mdpi.com/2073-4395/12/4/873obstacle avoidanceplant protection UAVprecision agriculturedata-driven dynamic avoidance approachspray coverage |
| spellingShingle | Shibbir Ahmed Baijing Qiu Chun-Wei Kong Huang Xin Fiaz Ahmad Jinlong Lin A Data-Driven Dynamic Obstacle Avoidance Method for Liquid-Carrying Plant Protection UAVs Agronomy obstacle avoidance plant protection UAV precision agriculture data-driven dynamic avoidance approach spray coverage |
| title | A Data-Driven Dynamic Obstacle Avoidance Method for Liquid-Carrying Plant Protection UAVs |
| title_full | A Data-Driven Dynamic Obstacle Avoidance Method for Liquid-Carrying Plant Protection UAVs |
| title_fullStr | A Data-Driven Dynamic Obstacle Avoidance Method for Liquid-Carrying Plant Protection UAVs |
| title_full_unstemmed | A Data-Driven Dynamic Obstacle Avoidance Method for Liquid-Carrying Plant Protection UAVs |
| title_short | A Data-Driven Dynamic Obstacle Avoidance Method for Liquid-Carrying Plant Protection UAVs |
| title_sort | data driven dynamic obstacle avoidance method for liquid carrying plant protection uavs |
| topic | obstacle avoidance plant protection UAV precision agriculture data-driven dynamic avoidance approach spray coverage |
| url | https://www.mdpi.com/2073-4395/12/4/873 |
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