Technology Category
Planning, Routing, and Scheduling: Optimization of Spatio-temporal SystemsIntelligent Robots: Motion and Path PlanningSearch and Optimization: Sampling/Simulation-based Search
MUTE-DSS: A Digital-Twin-Based Decision Support System for Minimizing Underwater Radiated Noise in Ship Voyage Planning
🤖 AI Summary
This study addresses the ecological impact of underwater radiated noise (URN) from commercial vessels on marine mammals—particularly killer whales (Orcinus orca). We propose a digital twin–enabled intelligent low-noise voyage planning framework. Methodologically, it employs a two-stage co-optimization architecture: Stage I integrates collision-free path planning with ecological sensitivity constraints; Stage II incorporates adaptive speed optimization. The framework unifies a semi-empirical near-field acoustic model, a 3D ray-tracing far-field propagation model, and a data-driven killer whale distribution prediction model. Implemented on the ROS2 platform, it jointly applies Batch Informed Trees (BIT*) and a genetic algorithm for solution synthesis. Validation on real-world shipping routes demonstrates an average URN exposure reduction of 4.90 dB (peak: 7.14 dB), corresponding to an 80.68% and 67.6% reduction in noise-induced ecological impact under simplified and dynamic scenarios, respectively—substantially enhancing the ecological sustainability of maritime operations.
Technology Category
Application Category
📝 Abstract
We present a novel MUTE-DSS, a digital-twin-based decision support system for minimizing underwater radiated noise (URN) during ship voyage planning. It is a ROS2-centric framework that integrates state-of-the-art acoustic models combining a semi-empirical reference spectrum for near-field modeling with 3D ray tracing for propagation losses for far-field modeling, offering real-time computation of the ship noise signature, alongside a data-driven Southern resident killer whale distribution model. The proposed DSS performs a two-stage optimization pipeline: Batch Informed Trees for collision-free ship routing and a genetic algorithm for adaptive ship speed profiling under voyage constraints that minimizes cumulative URN exposure to marine mammals. The effectiveness of MUTE-DSS is demonstrated through case studies of ships operating between the Strait of Georgia and the Strait of Juan de Fuca, comparing optimized voyages against baseline trajectories derived from automatic identification system data. Results show substantial reductions in noise exposure level, up to 7.14 dB, corresponding to approximately an 80.68% reduction in a simplified scenario, and an average 4.90 dB reduction, corresponding to approximately a 67.6% reduction in a more realistic dynamic setting. These results illustrate the adaptability and practical utility of the proposed decision support system.
Problem
Research questions and friction points this paper is trying to address.
Minimizes underwater radiated noise in ship voyage planning
Optimizes ship routing and speed to reduce marine mammal noise exposure
Integrates acoustic models and real-time computation for noise signature
Innovation
Methods, ideas, or system contributions that make the work stand out.
Digital-twin-based system for noise minimization
ROS2 framework with acoustic and distribution models
Two-stage optimization for routing and speed profiling
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