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Article | Number of agents | Properties | Limitations |
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[23] | Multiple | Uses CUSUM algorithm, presents the boundary estimation problem as a HMM, and recast as an optimization problem | Considered only ellipse-shaped, no path optimization, simulation only, intervehicle communication issues not addressed, and AUV dynamics not considered |
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[24] | Multiple | Decentralized, gradient-free algorithm, convergent and stable | AUV dynamics not considered, high computation cost, intervehicle communication issues not addressed, and only very simple plume shapes considered |
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[3] | Single, multiple | Cooperative, generating polygons to follow based on ocean model predictions, simulation, and practical implementation | Trajectory based on (roughly) approximated polygons, temporal constraints not considered, unable to react to the fast moving features, and ignoring the dynamics of the glider |
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[25] | Single | Behavior-based approach for plume mapping, subsumption architecture, showing experimental results | Limitations of behavior-based approach, no description on adaptive mapping, and implementing simple preplanned lawn-mower strategy |
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[26] | Single | Uses colored dissolved organic matter (CDOM) sensor for planed missions, adaptive planning using in situ current, and temperature measurements, and gets the 3D track of the AUV, practical implementation | No information on path optimization and no adaptive tracking |
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[27] | Single | Based on peak-capture algorithm, it generates a sawtooth trajectory and uses depth information and practical implementation | No information on path optimization |
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[28] | Multiple | Adaptive behavior-based system, acoustic communication within AUVs, representing the plume using Fourier orders when reconstructing | No information on path optimization and communication overhead |
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[29] | Single | Uses a plume indicator function and real-time implementation and uses adaptive transects; transect length depends on number of consecutive samples; distance between transects is a percentage of previous samples | Less path optimization, not using an AUV, no information about the convergence, and coverage of the used algorithm |
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[30] | Single | Uses remote sensing data to detect hotspots, uses surface current to project plumes spatiotemporally, and runs in a lawnmower type pattern, practical implementation | No path optimization, only using predefined pattern, and no adaptive tracking |
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Proposed | Single | Path optimization based on gradient information, adaptive plume tracking, and centralized approach | Only simulation results, no comparison data available, relying on remote sensing data for locating the plume region initially, low performance to noisy plume boundaries, and using only one fluorometer |
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