The recent advance in autonomous underwater robotics facilitates autonomous inspection tasks of offshore infrastructure. However, current inspection missions rely on predefined plans created offline, hampering the flexibility and autonomy of the inspection vehicle and the mission's success in case of unexpected events. In this work, we address these challenges by proposing a framework encompassing the modeling and verification of mission plans through Behavior Trees (BTs). This framework leverages the modularity of BTs to model onboard reactive behaviors, thus enabling autonomous plan executions, and uses BehaVerify to verify the mission's safety. Moreover, as a use case of this framework, we present a novel AI-enabled algorithm that aims for efficient, autonomous pipeline camera data collection. In a simulated environment, we demonstrate the framework's application to our proposed pipeline inspection algorithm. Our framework marks a significant step forward in the field of autonomous underwater robotics, promising to enhance the safety and success of underwater missions in practical, real-world applications. https://github.com/remaro-network/pipe_inspection_mission

翻译：自主水下机器人技术的进步为海上基础设施的自主检测任务提供了便利。然而，当前检测任务仍依赖离线制定的预设规划方案，这限制了检测载具的灵活性与自主性，在突发事件下可能影响任务成功率。针对上述挑战，本文提出了一套基于行为树（BTs）实现任务规划建模与验证的框架。该框架利用行为树的模块化特性对机载反应式行为进行建模，从而实现自主规划执行，并借助BehaVerify工具验证任务安全性。此外，作为该框架的应用实例，我们提出了一种新型AI驱动算法，旨在实现高效自主的管道相机数据采集。通过仿真环境验证了该框架在我们所提出的管道检测算法中的应用效果。此项工作标志着自主水下机器人领域的重要进展，有望在实际工程应用中提升水下任务的安全性与成功率。https://github.com/remaro-network/pipe_inspection_mission