自主水下机器人管道巡检的任务规划与安全评估：基于行为树的框架
Mission Planning and Safety Assessment for Pipeline Inspection Using Autonomous Underwater Vehicles: A Framework based on Behavior Trees
AI总结：
针对自主水下机器人管道巡检依赖离线计划、缺乏灵活性的问题，提出基于行为树的框架，结合BehaVerify验证安全，并开发AI算法实现高效数据采集，提升任务安全与成功率。
摘要：
自主水下机器人技术的近期进展促进了海上基础设施的自主巡检任务。然而，当前的巡检任务依赖于离线创建的预定义计划，这削弱了巡检车辆的灵活性和自主性，以及在意外事件发生时任务的成功率。在本工作中，我们通过提出一个涵盖基于行为树（BTs）的任务计划建模与验证的框架来应对这些挑战。该框架利用行为树的模块化特性来建模机载反应式行为，从而实现自主计划执行，并使用BehaVerify来验证任务的安全性。此外，作为该框架的一个用例，我们提出了一种新颖的基于人工智能的算法，旨在实现高效、自主的管道摄像头数据采集。在模拟环境中，我们展示了该框架在我们提出的管道巡检算法中的应用。我们的框架标志着自主水下机器人领域迈出了重要一步，有望在实际应用中增强水下任务的安全性和成功率。
英文摘要：
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
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。
