Risk-based supervisory control for autonomous ship navigation

This paper proposes a novel method to transform the results of qualitative risk analysis into a numeric optimal control problem for autonomous ship navigation. Today, making autonomous high-level decisions replacing a crew onboard is considered difficult, in some part due to the complexity of managi...

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Bibliographic Details
Published in:Journal of marine science and technology 2023-09, Vol.28 (3), p.624-648
Main Authors: Blindheim, Simon, Johansen, Tor Arne, Utne, Ingrid Bouwer
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Automotive Engineering
Autonomous navigation
Coastal environments
Coastal zones
Complexity
Control centres
Control systems
Crew
Decision analysis
Decision making
Engineering
Engineering Design
Engineering Fluid Dynamics
Liability
Mechanical Engineering
Navigation
Offshore Engineering
Optimal control
Optimization
Original Article
Qualitative analysis
Remote control
Risk analysis
Risk assessment
Risk management
Supervisors
Supervisory control
Systems theory
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Summary:This paper proposes a novel method to transform the results of qualitative risk analysis into a numeric optimal control problem for autonomous ship navigation. Today, making autonomous high-level decisions replacing a crew onboard is considered difficult, in some part due to the complexity of managing the operational risks involved. Although human supervisors, e.g., located in remote operating control centers are still needed for safety and liability reasons, there is a growing demand for complex decisions to be made by the onboard control system itself, both during normal operations and in emergencies. This paper suggests general principles for how the results from systems-theoretic process analysis (STPA) can be transformed into a quantitative and computationally tractable optimization problem, solved by a MPC-based decision-making algorithm for autonomous navigation. The proposed method is demonstrated and evaluated by simulating an autonomous ship navigating in a coastal environment. It is concluded that the proposed method may serve as a reasonable and valuable bridge between the realms of qualitative risk analysis and numerical optimal control for risk-aware autonomous control and decision-making.
ISSN:0948-4280
1437-8213
DOI:10.1007/s00773-023-00945-6