构建稳健自主无人机测试流程的分步指南
A Step-by-Step Guide to Creating a Robust Autonomous Drone Testing Pipeline
AI总结：
本文提出涵盖软件在环、硬件在环等四阶段的自主无人机测试分步指南，结合着陆系统案例说明验证方法，介绍相关新兴测试趋势，助力全面验证无人机系统、降低部署风险。
摘要：
自主无人机正快速重塑从空中配送、基础设施检测到环境监测与灾害响应等各类行业。随着这些系统从研究原型转向关键任务平台，确保其安全性、可靠性与效率至关重要。本文提出了一套建立稳健自主无人机测试流程的分步指南，覆盖每个关键阶段：软件在环（Software-in-the-Loop, SIL）仿真测试、硬件在环（Hardware-in-the-Loop, HIL）测试、受控真实环境测试以及实地测试。我们借助基于标记点的自主着陆系统等实际案例，展示了如何系统性地验证无人机系统行为、识别集成问题并优化性能。此外，本文还重点介绍了塑造无人机测试未来的新兴趋势，包括神经符号系统与大语言模型（LLMs）的融合、协同仿真环境的构建，以及数字孪生赋能的仿真测试技术。遵循该流程，开发者与研究人员可实现全面验证，最大限度降低部署风险，为自主无人机安全可靠的真实世界运行做好准备。
英文摘要：
Autonomous drones are rapidly reshaping industries ranging from aerial delivery and infrastructure inspection to environmental monitoring and disaster response. Ensuring the safety, reliability, and efficiency of these systems is paramount as they transition from research prototypes to mission-critical platforms. This paper presents a step-by-step guide to establishing a robust autonomous drone testing pipeline, covering each critical stage: Software-in-the-Loop (SIL) Simulation Testing, Hardware-in-the-Loop (HIL) Testing, Controlled Real-World Testing, and In-Field Testing. Using practical examples, including the marker-based autonomous landing system, we demonstrate how to systematically verify drone system behaviors, identify integration issues, and optimize performance. Furthermore, we highlight emerging trends shaping the future of drone testing, including the integration of Neurosymbolic and LLMs, creating co-simulation environments, and Digital Twin-enabled simulation-based testing techniques. By following this pipeline, developers and researchers can achieve comprehensive validation, minimize deployment risks, and prepare autonomous drones for safe and reliable real-world operations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
