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This paper presents the latest advancements in automated planning and acting (AI planning) for robots in inspection and maintenance (I&M) within the ROBPLAN project. Offshore and onshore oil and gas (O&G) facilities, characterized by hazardous environments and remote locations, benefit significantly from robotic assistance. The market for I&M robots is growing and is projected to reach 72.5 billion USD by 2029. Despite progress in autonomous robotics functions such as path planning and object detection, a gap remains in coordinating these functions through high-level reasoning systems, especially in the challenging environments of O&G facilities. This paper details methods, field trials, and use cases for Uncrewed Ground Vehicles (UGVs) and Uncrewed Aerial Vehicles (UAVs) in I&M, focusing on AI planning. Key contributions include the analysis of industry-relevant use cases, the development of ISAR, a tool for integrating robots into plant operator systems, and Norns, a tool for in-situ planning and execution. Additional contributions involve the integration of AI planning with Guidance, Navigation, and Control systems, the development of methods for AI planning of inspection and maintenance (I&M) operations based on various AI planning methods (e.g., timeline-based, temporal), and the validation of these AI planning methods through simulations and field trials at Equinor’s K-Lab facility in Norway.

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ROBPLAN: Autonomous Inspection and Maintenance Robotics with Automated Planning and Acting

  • 章节

  • 发表日期：

    2026年

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  • Aksel A. Transeth,
  • Miguel A. Hinostroza,
  • Anastasios M. Lekkas,
  • Bjørnar Luteberget,
  • Synne Fossøy,
  • Frederic Py,
  • Christian de Jonge,
  • Svein Ivar Sagatun,
  • Øystein Skotheim,
  • Ola Christoffer Våge

摘要

This paper presents the latest advancements in automated planning and acting (AI planning) for robots in inspection and maintenance (I&M) within the ROBPLAN project. Offshore and onshore oil and gas (O&G) facilities, characterized by hazardous environments and remote locations, benefit significantly from robotic assistance. The market for I&M robots is growing and is projected to reach 72.5 billion USD by 2029. Despite progress in autonomous robotics functions such as path planning and object detection, a gap remains in coordinating these functions through high-level reasoning systems, especially in the challenging environments of O&G facilities. This paper details methods, field trials, and use cases for Uncrewed Ground Vehicles (UGVs) and Uncrewed Aerial Vehicles (UAVs) in I&M, focusing on AI planning. Key contributions include the analysis of industry-relevant use cases, the development of ISAR, a tool for integrating robots into plant operator systems, and Norns, a tool for in-situ planning and execution. Additional contributions involve the integration of AI planning with Guidance, Navigation, and Control systems, the development of methods for AI planning of inspection and maintenance (I&M) operations based on various AI planning methods (e.g., timeline-based, temporal), and the validation of these AI planning methods through simulations and field trials at Equinor’s K-Lab facility in Norway.