构建无人机自主系统测试全流程，保障安全可靠落地。
A Step-by-Step Guide to Creating a Robust Autonomous Drone Testing Pipeline
- 分四阶段构建测试流程：软件仿真、软硬联调、受控实测、野外验证。
- 通过标记点自动着陆案例验证系统行为与集成问题，提升可靠性。
- 融合神经符号系统与数字孪生技术，适合研发和工程团队参考。
自主无人机正快速重塑从空中配送、基础设施巡检到环境监测和灾害响应等多个领域。随着其从研究原型迈向关键任务平台，确保系统的安全性、可靠性和效率至关重要。本文提出一个分步指南，用于建立稳健的自主无人机测试流程，涵盖四个关键阶段：软件在环（SIL）仿真测试、硬件在环（HIL）测试、受控真实世界测试以及野外测试。结合标记点自主着陆系统的实际案例，展示了如何系统性验证无人机行为、识别集成问题并优化性能。此外，文章还展望了未来无人机测试的发展趋势，包括神经符号系统与大语言模型的融合、协同仿真环境的构建，以及基于数字孪生的仿真测试技术。遵循该测试流程，开发者与研究人员可实现全面验证，降低部署风险，为无人机在真实场景中的安全可靠运行做好准备。
原文摘要 · Abstract (English)
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.
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