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This research applies mixed-integer linear programming to optimize the deployment of autonomous underwater vehicles and docking stations for subsea pipeline inspection. By jointly minimizing maximum and average response times under spatial uncertainty, the study provides a mathematical framework for designing resilient robotic networks in harsh environments. While the application is industrial, the core contribution lies in advanced optimization algorithms governing multi-agent robot allocation.
Optimal Placement of Docking Stations and Resident AUVs for Subsea Pipeline Inspection
arXiv:2607.19944v1 Announce Type: cross Abstract: A two-stage mixed-integer linear programming framework is introduced for subsea pipeline incident response planning, jointly optimizing Subsea Docking Plate (SDP) placement and resident autonomous underwater vehicle allocation to minimize both maximum and average response times under spatial uncertainty. Phase 1 minimizes the maximum response time across all potential leak locations. Phase 2 reduces the average response time subject to the maximum bound. A case study based on the Johan Sverdrup oil and gas field in Norway, with 9 SDP options and 33 pipelines, shows that just a few well-placed SDPs are enough to achieve top performance. A cost versus time Pareto analysis reveals the trade-off between deployment expenditure and response efficiency, providing actionable guidance for designing resilient and cost-effective subsea inspection networks.
Abstract:A two-stage mixed-integer linear programming framework is introduced for subsea pipeline incident response planning, jointly optimizing Subsea Docking Plate (SDP) placement and resident autonomous underwater vehicle allocation to minimize both maximum and average response times under spatial uncertainty. Phase 1 minimizes the maximum response time across all potential leak locations. Phase 2 reduces the average response time subject to the maximum bound. A case study based on the Johan Sverdrup oil and gas field in Norway, with 9 SDP options and 33 pipelines, shows that just a few well-placed SDPs are enough to achieve top performance. A cost versus time Pareto analysis reveals the trade-off between deployment expenditure and response efficiency, providing actionable guidance for designing resilient and cost-effective subsea inspection networks.
Author Accepted Manuscript. Published version available in IEEE Journal of Oceanic Engineering. 12 pages, 9 figures
Optimization and Control (math.OC); Robotics (cs.RO)
arXiv:2607.19944 [math.OC]
(or arXiv:2607.19944v1 [math.OC] for this version)
https://doi.org/10.48550/arXiv.2607.19944 arXiv-issued DOI via DataCite (pending registration)
G. Kasparavičiūtė, P. Grippa, K. Skaugset, A. J. Sørensen, and M. Ludvigsen, "Optimal Placement of Docking Stations and Resident AUVs for Subsea Pipeline Inspection," IEEE Journal of Oceanic Engineering, vol. 51, no. 1, 2026
https://doi.org/10.1109/JOE.2026.3694005 DOI(s) linking to related resources
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