基于ROS系统的多机协同虚实结合系统设计与实现

Design and Implementation of a ROS-Based Multi-UAV Cooperative Virtual-Physical Integrated System

摘要: 无人机(Unmanned Aerial Vehicle, UAV)集群技术在现代军事侦察、地质勘探及物流运输等领域的应用日益广泛。然而，在大规模集群系统的研发过程中，单纯依赖软件在环仿真(Software-in-the-Loop, SITL)存在模型精度不足、无法复现真实物理特性等问题，而全实物硬件在环(Hardware-in-the-Loop, HIL)测试则面临成本高昂、场地受限及安全风险大等挑战。针对上述痛点，本研究报告详细阐述了一种基于机器人操作系统(Robot Operating System, ROS)的多机协同虚实结合仿真系统。该系统创新性地融合了数字孪生技术，构建了包含多架实体无人机与多架虚拟无人机的混合编队架构。本文深入剖析了系统的分层架构设计，详细论述了利用ROS通信机制、PX4自动驾驶仪及Gazebo物理引擎实现虚实节点数据交互的关键技术。通过构建基于UDP协议的高效通信链路，系统成功实现了异构节点间的状态同步与协同控制。实验数据表明，该平台在保证仿真结果与真实飞行高度一致(实时因子k ≈ 1)的前提下，相比传统全实物测试降低了约77%的实验成本。本报告旨在为无人机集群协同控制算法的验证提供一套兼具高置信度、低成本与可扩展性的综合解决方案，并对相关理论基础、系统实现细节及性能评估进行详尽的阐述。

Abstract: Unmanned Aerial Vehicle (UAV) swarm technology is increasingly applied in modern military reconnaissance, geological exploration, logistics transportation, and other fields. However, during the development of large-scale swarm systems, reliance solely on Software-in-the-Loop (SITL) simulation faces issues such as insufficient model accuracy and the inability to reproduce real physical characteristics, while full Hardware-in-the-Loop (HIL) testing presents challenges including high costs, limited space, and significant safety risks. To address these challenges, this research report elaborates on a multi-UAV cooperative simulation system integrating virtual and real components based on the Robot Operating System (ROS). The system innovatively incorporates digital twin technology to construct a hybrid formation architecture comprising multiple physical UAVs and multiple virtual UAVs. This paper provides an in-depth analysis of the system’s layered architecture design and discusses in detail the key technologies for data interaction between virtual and real nodes using ROS communication mechanisms, the PX4 autopilot system, and the Gazebo physics engine. By establishing an efficient communication link based on the UDP protocol, the system successfully achieves state synchronization and cooperative control among heterogeneous nodes. Experimental data indicate that the platform reduces experimental costs by approximately 77% compared to traditional full physical testing while ensuring high consistency between simulation results and actual flight conditions (real-time factor k ≈ 1). This report aims to provide a comprehensive solution with high fidelity, low cost, and scalability for validating UAV swarm cooperative control algorithms, offering detailed explanations of the theoretical foundations, system implementation, and performance evaluation.