Energy-efficient route planning for optimizing underwater pipeline inspections using Resident Autonomous Underwater Vehicles

This paper addresses the Resident Autonomous Underwater Vehicle Routing Problem, and proposes an algorithm for optimizing the routing of a single electric Resident Autonomous Underwater Vehicle (RAUV) used for subsea pipeline inspection. Introducing automated route planning to inspections for underwater vehicles will increase their autonomy enabling persistent installations using docking stations for oil and gas fields on the continental shelf. Algorithms for both a static and a dynamic version of the proposed route planning problem are considered. The algorithm for the static version describes a situation where the RAUV commences its operation from a Subsea Docking Plate, which serves as a facility for both energy replenishment and data transfer. The primary goal is initially addressed by computing an efficient route before the mission starts, which encompasses inspection paths for all pipelines within the given network to ensure the shortest possible distance (or time) while maintaining the vehicle's battery level above the minimum threshold. Upon receiving a request fora higher priority pipeline inspection task, a dynamic re-routing algorithm is then initiated, enabling real-time adjustments to the underwater vehicles' routes to accommodate these new priorities. The performance of the algorithms is evaluated by simulated and real-world field tests. The findings of the study suggest that the proposed algorithm is capable of providing efficient routing solutions.