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With the continuous development of information and intelligent manufacturing technology, agriculture has entered the era of intelligent and automated production. Unmanned smart farms, as an important direction of modern agricultural development, are facing opportunities and challenges. As one of the key supporting technologies for unmanned farms, unmanned aerial vehicle (UAV) swarm flight control technology provides crucial technical support for agricultural production in fields such as field inspection, livestock management, and irrigation control. This study aimed to analyze the data interaction methods of various components in UAV swarm simulation flight control, establish and optimize a multi-rotor UAV swarm flight simulation environment to meet the application requirements of UAV swarm flight control in unmanned smart farms. In this paper, by analyzing the interaction logic among the ROS system, PX4 flight controller, MAVROS communication module, and Gazebo simulation environment in UAV swarm simulation flight control, we built a multi-rotor UAV swarm flight simulation environment based on the open-source XTDone simulation platform. We also realized model construction and flight control of UAV swarm based on the ROS system, PX4 flight controller, and Gazebo. Furthermore, using a laser radar to collect environmental information, we optimized software algorithms in the ROS distributed framework, achieving simultaneous localization and mapping (SLAM) based on scan matching algorithm and navigation based on optimal path planning algorithm. Theoretical simulation and experimental results demonstrated that the platform had advantages such as open-source, low cost, scalability, and modularity. The constructed simulation environment can achieve UAV swarm flight control, construction of 2D maps in enclosed environments, and autonomous navigation flight. Analysis revealed that the unit flight accuracy of the UAV swarm under this simulation platform was approximately 76%, and the regression model constructed from cumulative flight distance and flight error had an R2 of 0.830 9. The research results demonstrated the feasibility of using UAV formation collaborative flight operations to meet the common agricultural operation needs in unmanned smart farms, demonstrated the application effect and advantages of UAV formation in unmanned smart farms, and provided technical ideas for deep optimization of simulation flight environments and expansion of modern agricultural production application scenarios. It also showed certain reference value and reference value in research and practice in related fields.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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