Mission Planning and Decision Support for Underwater Glider Networks: A Sampling on-Demand Approach

Area of Science:

• Oceanography
• Marine robotics
• Geospatial data analysis

Background:

• Optimal sampling strategies are crucial for efficient underwater glider missions.
• Traditional methods focus on minimizing overall uncertainty, which may not align with specific scientific objectives.
• Existing geoscientific optimality criteria (A, G, E) offer different sampling approaches.

Purpose of the Study:

• To develop a flexible optimal sampling approach for underwater glider fleets.
• To enable mission planning based on user-defined uncertainty objectives rather than solely minimizing overall uncertainty.
• To introduce a new optimality criterion (A η) and a practical decision support tool for glider operations.

Main Methods:

• Proposed a "sampling on-demand" strategy where users define target uncertainty maps.
• Introduced a novel A η optimality criterion for path planning.
• Employed a Simulated Annealing-based optimizer to solve the minimization problem, considering glider navigation and ocean current constraints.
• Developed the Sampling on-Demand and Decision Support (SoDDS) MATLAB toolbox.

Main Results:

• The SoDDS toolbox successfully implemented the sampling on-demand approach.
• Demonstrated the tool's effectiveness through three distinct operational scenarios.
• The approach allows for achieving mission-specific uncertainty targets, moving beyond general uncertainty minimization.

Conclusions:

• The sampling on-demand approach and SoDDS toolbox significantly enhance systematic mission planning for underwater glider fleets.
• This method reduces the effort required by glider operators and improves mission efficiency.
• The tool is currently utilized at NATO STO Centre for Maritime Research and Experimentation (CMRE).