Related papers: A Step-by-Step Guide to Creating a Robust Autonomous Drone Testing Pipeline

A Step-by-Step Guide to Creating a Robust Autonomous Drone Testing Pipeline

URL: http://arxiv.org/abs/2506.11400v1
Date: Fri, 13 Jun 2025 01:55:23 GMT
Title: A Step-by-Step Guide to Creating a Robust Autonomous Drone Testing Pipeline
Authors: Yupeng Jiang, Yao Deng, Sebastian Schroder, Linfeng Liang, Suhaas Gambhir, Alice James, Avishkar Seth, James Pirrie, Yihao Zhang, Xi Zheng,
Abstract summary: This paper presents a step-by-step guide to establishing a robust autonomous drone testing pipeline.<n>It covers each critical stage: Software-in-the-Loop (SIL) Simulation Testing, Hardware-in-the-Loop (HIL) Testing, Controlled Real-World Testing, and In-Field Testing.<n>We highlight emerging trends shaping the future of drone testing, including the integration of Neurosymbolic and LLMs.
Score: 7.898388030666187
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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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