A Step-by-Step Guide to Creating a Robust Autonomous Drone Testing Pipeline
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913890979282944 |
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| author | Jiang, Yupeng Deng, Yao Schroder, Sebastian Liang, Linfeng Gambhir, Suhaas James, Alice Seth, Avishkar Pirrie, James Zhang, Yihao Zheng, Xi |
| author_facet | Jiang, Yupeng Deng, Yao Schroder, Sebastian Liang, Linfeng Gambhir, Suhaas James, Alice Seth, Avishkar Pirrie, James Zhang, Yihao Zheng, Xi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_11400 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Step-by-Step Guide to Creating a Robust Autonomous Drone Testing Pipeline Jiang, Yupeng Deng, Yao Schroder, Sebastian Liang, Linfeng Gambhir, Suhaas James, Alice Seth, Avishkar Pirrie, James Zhang, Yihao Zheng, Xi Software Engineering Robotics 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. |
| title | A Step-by-Step Guide to Creating a Robust Autonomous Drone Testing Pipeline |
| topic | Software Engineering Robotics |
| url | https://arxiv.org/abs/2506.11400 |