基于ROS和PX4的无人机编队协同飞行模型的仿真研究  

Simulation research on cooperative flight model of UAV formation based on ROS and PX4

作　　者：路梦源 王天一 陈新昌[3] 张宇卓 宫泽奇 张星山 LU Mengyuan;WANG Tianyi;CHEN Xinchang;ZHANG Yuzhuo;GONG Zeqi;ZHANG Xingshan(College of Engineering,China Agricultural University,Beijing 100083,China;College of Agricultural Unmanned System,China Agricultural University,Beijing 100193,China;College of Mechanical&Electrical Engineering,Henan Agricultural University,Zhengzhou 450053,China;Aerospace Information research Institute,Chinese Academy of Sciences,Beijing 100094,China)

机构地区：[1]中国农业大学工学院,北京市100083 [2]中国农业大学农业无人机系统研究院,北京市100193 [3]河南农业大学机电工程学院,河南郑州450053 [4]中国科学院空天信息创新研究院,北京市100094

出　　处：《智能化农业装备学报（中英文）》2023年第3期32-41,共10页Journal of Intelligent Agricultural Mechanization

基　　金：国家重点研发计划课题(2022YFD2202102);内蒙古自治区科技计划项目(2022YFSJ0039);中国农业大学2115人才培育发展支持计划。

摘　　要：随着信息和智能制造技术的不断发展,农业生产进入自动化和智能化的时代。无人化智慧农场作为现代农业发展的重要方向,正面临机遇与挑战。无人机编队飞行控制技术作为无人农场重要支撑技术之一,在农田巡查、畜群管理、灌溉控制等领域为农业生产提供关键技术支持。本研究旨在分析无人机编队仿真飞行控制中各组成部分的数据交互方式,搭建并优化多旋翼无人机编队飞行仿真环境,以满足无人机编队的飞行控制在无人化智慧农场中的应用需求。通过分析无人机编队仿真飞行控制中ROS系统、PX4飞控,MAVROS通讯模块,以及Gazebo仿真环境之间的交互逻辑,搭建了基于XTDone开源仿真平台的多旋翼无人机编队飞行仿真环境,并实现了基于ROS系统、PX4飞控和Gazebo无人机编队的模型构建与飞行控制。在此基础上,利用激光雷达采集环境信息,在ROS分布式框架下进行软件算法的优化,实现了基于扫描匹配算法的SLAM功能和基于最优路径规划算法的导航功能。理论仿真及试验结果表明,该平台具有开源、低成本、可扩展、模块化等优点,所搭建的仿真环境可实现无人机的编队飞行控制、封闭环境二维地图的构建和自主导航飞行,并分析得出该仿真平台下无人机编队的单位飞行精度约为76%,累计飞行距离和飞行误差所构建回归模型的R^(2)为0.8309。研究结果证明了利用无人机编队协同飞行作业满足无人化智慧农场中常见农田作业需求的可行性,展示了无人机编队在无人化智慧农场中的应用效果和优势,为深层优化仿真飞行环境及拓展现代农业生产应用场景提供技术思路,在相关领域的研究和实践中也具有一定的借鉴和参考价值。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 err

关 键 词：无人农场 无人机编队 数据交互 飞行控制 激光SLAM 导航 

分 类 号：S24[农业科学—农业电气化与自动化] S252.9[农业科学—农业工程]