Mission Planning and Safety Assessment for Pipeline Inspection Using Autonomous Underwater Vehicles: A Framework based on Behavior Trees
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866909095467941888 |
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| author | Aubard, Martin Quijano, Sergio Álvarez-Tuñón, Olaya Antal, László Costa, Maria Brodskiy, Yury |
| author_facet | Aubard, Martin Quijano, Sergio Álvarez-Tuñón, Olaya Antal, László Costa, Maria Brodskiy, Yury |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_04045 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mission Planning and Safety Assessment for Pipeline Inspection Using Autonomous Underwater Vehicles: A Framework based on Behavior Trees Aubard, Martin Quijano, Sergio Álvarez-Tuñón, Olaya Antal, László Costa, Maria Brodskiy, Yury Robotics 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 |
| title | Mission Planning and Safety Assessment for Pipeline Inspection Using Autonomous Underwater Vehicles: A Framework based on Behavior Trees |
| topic | Robotics |
| url | https://arxiv.org/abs/2402.04045 |