Development of a Conceptual Model for the Information and Control System of an Autonomous Underwater Vehicle for Solving Problems in the Mineral and Raw Materials Complex

Abstract

This study presents the development of a conceptual model for an autonomous underwater vehicle (AUV) information and control system (ICS) tailored for the mineral and raw materials complex (MRMC). To address the challenges of underwater mineral exploration, such as harsh conditions, high costs, and personnel risks, a comprehensive model was designed. This model was built using correlation analysis and expert evaluations to identify critical parameters affecting AUV efficiency and reliability. Key elements, including pressure resistance, communication stability, energy efficiency, and maneuverability, were prioritized. The results indicate that enhancing these elements can significantly improve AUV performance in deep-sea environments. The proposed model optimizes the ICS, providing a foundation for designing advanced AUVs capable of efficiently executing complex underwater tasks. By integrating these innovations, the model aims to boost operational productivity, ensure safety, and open new avenues for mineral resource exploration. This study’s findings highlight the importance of focusing on critical AUV parameters for developing effective and reliable solutions, thus addressing the pressing needs of the MRMC while promoting sustainable resource management.

1. Introduction

The modern mineral and raw materials complex (MRMC) is increasingly driven by the need for exploration and development of underwater mineral deposits [1]. The depletion of readily available land-based resources and the growing global demand for minerals and rare earth elements are compelling the industry to explore deeper ocean environments [2,3]. However, conducting operations in high-pressure, low-temperature, and remote environments requires innovative technologies and approaches [4].

Autonomous underwater vehicles (AUVs) are emerging as a critical tool to meet these challenges [5]. They enable detailed exploration, monitoring of subsea infrastructure, and execution of technical operations without direct human involvement, thereby enhancing both the safety and efficiency of operations [6]. Nevertheless, the development of AUVs specifically adapted to the unique requirements of the mineral and raw materials complex remains a complex engineering challenge [7,8].

The purpose of this study is to develop a conceptual model for an information and control system of an AUV optimized for performing tasks that benefit the mineral and raw materials complex. This study examines modern technical solutions, analyzes industry requirements, and proposes approaches to designing a vehicle capable of efficient operation in complex underwater conditions.

The conceptual model described in this study primarily focuses on the information architecture and critical parameters necessary for AUV operation. While it does not directly include control strategies, it establishes a foundation for developing advanced control systems in future work.

1.1. Existing Challenges in the Field

The mineral and raw materials complex faces several significant challenges related to the exploration, production, and monitoring of subsea mineral resources [9]. The key challenges include the following:

• Complexity of subsea exploration: Traditional exploration methods are limited by the depth and complexity of the subsea environment. The lack of accurate data on geological structures complicates the search for and evaluation of new deposits [10,11].
• High risks for human personnel: The use of manned submersibles presents risks to the life and health of operators due to the extreme conditions of high depth, pressure, and temperature [12].
• Limited access to deepwater resources: Many promising mineral resources are located at depths inaccessible to conventional mining and exploration technologies [13].
• Low efficiency and high operational costs: Traditional exploration and production methods demand significant financial and time investments, often yielding low returns due to the limited information and technology [14,15].
• Limited real-time capabilities: Delays in data acquisition and processing hinder the ability to make timely operational decisions, thereby reducing the overall efficiency of operations [16,17].

The development of an information and control system for AUVs is a key step in addressing these challenges faced by the mineral and raw materials complex [18,19]. The integration of modern technologies in sensorics, artificial intelligence, and autonomous control systems will significantly enhance the efficiency, safety, and environmental sustainability of underwater operations, opening new possibilities for deep-sea exploration [20,21].

1.2. Problem Statement

Considering the challenges, there is a pressing need to develop innovative solutions that can overcome the existing limitations within the mineral and raw materials complex (MRMC) [22,23]. Developing a conceptual model for the information and control system of an autonomous underwater vehicle, optimized to perform tasks that benefit the MRMC, is a crucial step in this direction.

The conceptual model of the information and control system for an AUV should account for the following key elements presented in Figure 1 [24,25,26,27,28,29,30,31,32,33].

The development of an information and control system based on this conceptual model will provide the high-quality output parameters necessary for making strategic decisions within the mineral and raw materials complex. This will enable prompt responses to changes and facilitate effective planning for future actions.

Despite advancements in AUV technology, several limitations persist in the design of information and control systems (ICSs) for deep-sea exploration. Current AUV systems struggle with limited autonomy, delayed real-time data processing, and insufficient adaptive maneuverability to handle dynamic underwater environments. For instance, issues with data latency and power consumption [34], which compromise mission success in deep-sea conditions. Our proposed conceptual model addresses these gaps by prioritizing parameters such as energy efficiency, communication stability, and pressure resistance, providing a novel framework for enhancing ICS reliability and performance in challenging underwater scenarios.

Thus, the development of a conceptual model for the information and control system of an AUV, optimized to perform tasks for the benefit of the MRMC, not only addresses the current challenges of the industry but also lays the foundation for its future growth. By accounting for disturbing influences, internal parameters, external factors, and the environment, and by managing them effectively to yield valuable output parameters, the industry will be able to achieve new levels of efficiency, safety, and environmental sustainability in underwater operations.

2. Materials and Methods

Correlation analysis was employed to build the conceptual model of the AUV’s information and control system to determine the relationships between variables and their significance. The assessments were individual and based on the opinions of selected experts. The selection criteria for the expert panel were as follows: experts were required to have practical and research experience in the fields of underwater robotics and marine technologies. It was also important to consider their professional competence, personal interest in the outcomes of the expert evaluation, and their adherence to standards of conformity.

The expert committee for this study included four experts with over 10 years of experience in AUV and AUV ICS development, as well as one expert with experience in the MRMC.

Each expert received an email containing a table with technical parameters of the AUV and an accompanying message like the following:

• “Good afternoon! I am, Dmitry Dmitriyevich Kotov, a graduate student at St. Petersburg Mining University, Department of System Analysis and Control, specializing in System Analysis, Control, Information Processing, and Statistics. As a part of my studies, I am conducting research in the field of developing autonomous underwater vehicles (AUVs). For my thesis, I am developing an Information and Control System (ICS) for AUVs. Before proceeding, I need to develop a conceptual model by determining the influence of various parameters through expert judgment, which will be used to calculate the correlation coefficient. I value the opinions of experts like you who are familiar with these challenges, and I would be grateful for your assistance in evaluating the attached table of parameters. Please assign a ranking to each parameter according to its influence on the technological process. If you believe I have missed any important parameters, I welcome your comments. Thank you in advance!”

A completed table and/or comments on the parameters were expected in response. When assessing the significance of the parameters, experts assigned each one a ranking on a scale from 1 to 10, where the scores were defined as shown in

Table 1. Criteria for assessment of the experts.

Parameters	Scoring Band
Minor Importance	1–3
Moderate Importance	4–5
Significant	6–7
Important	8–10

.

Experts were allowed to assign the same ranking number to multiple factors if they considered them equivalent in importance.

The selection of parameters was based on a comprehensive review of literature, expert consultations, and relevance to AUV performance in deep-sea conditions (

Table A1. Parameters based on a comprehensive review of literature, expert consultations, and relevance to AUV performance.

No.	AUV Parameters	Model Equation	Description	Ref.
1	Underwater currents	$\rho \left({\displaystyle \frac{\partial \mathbf{v}}{\partial t}}+\mathbf{v}·\nabla \mathbf{v}\right)=$ $-\nabla p+\mu {\nabla }^2\mathbf{v}+\mathbf{f},$	where $\rho$ is the fluid density, $\mathbf{v}$ is the velocity field, $t$ is time, $p$ is pressure, $\mu$ is the dynamic viscosity, $\nabla p$ is the pressure gradient, ${\nabla }^2\mathbf{v}$ is the Laplacian of the velocity field, and $\mathbf{f}$ is the external body force per unit volume.	[41,42,43]
2	Water density	${\rho }_{water}=$ ${\rho }_0\left(1-\beta T+\gamma S\right),$	${\mathrm{w}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{e} \rho }_{water}$ is the density of water, ${\rho }_0$ is the reference density, $T$ is temperature, $\beta$ is the thermal expansion coefficient, $S$ is salinity, and $\gamma$ is the salinity coefficient.	[44,45,46,47]
3	Water salinity	$S={\displaystyle \frac{m_{salt}}{m_{water}}}\times 1000$	where $S$ is salinity in parts per thousand, $m_{salt}$ is the mass of salt, and $m_{water}$ is the mass of water.	[48,49]
4	Water temperature	$T=T_0+\Delta T,$	where $T$ is the current temperature, $T_0$ is the reference temperature, and $\Delta T$ is the temperature change.	[50,51]
5	Hydrostatic loads	$P_{hydro}={\rho }_{water}·g·h,$	$\mathrm{w}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{e} P_{hydro}$ is hydrostatic pressure, ${\rho }_{water}$ is water density, $g$ is gravitational acceleration, and $h$ is depth.	[52,53]
6	Shock loads	$F_{shock}={\displaystyle \frac{\Delta p}{\Delta t}},$	${\mathrm{w}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{e} F}_{shock}$ is the shock force, $\Delta p$ is the pressure change, and $\Delta t$ is the time interval.	[54,55]
7	Chemical impact of water	$R_{corrosion}=$ $k·\left({\displaystyle \frac{I}{A}}\right)·\left(1-e^{-{\displaystyle \frac{t}{\tau }}}\right),$	${\mathrm{w}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{e} R}_{corrosion}$ is the corrosion rate, $k$ is a material constant, $I$ is current, $A$ is the exposed surface area, $t$ is time, and $\tau$ is the characteristic time constant.	[56]
8	Biological impact of water	$R_{biofoul}=$ $r·N·\left(1-{\displaystyle \frac{N}{K}}\right),$	$\mathrm{w}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{e} R_{biofoul}$ is the biofouling growth rate, $r$ is the growth rate constant, $N$ is the current population, and $K$ is the carrying capacity.	[57,58]
9	Water resistance	$F_{drag}={\displaystyle \frac{1}{2}}·C_d·{\rho }_{water}·A·v^2,$	where $F_{drag}$ is the drag force, $C_d$ is the drag coefficient, ${\rho }_{water}$ is the water density, $A$ is the cross-sectional area, and $v$ is the velocity.	[59,60]
10	Weight	$F_b=F_g⟹{\rho }_w·V·g=m·g,$	where $F_b$ is the buoyant force, $F_g$ is the gravitational force, ${\rho }_w$ is the density of water, $V$ is the submerged volume, and $m$ is the object’s mass.	[61,62]
11	Inertia	$\left\{\begin{array}{r}\sum m_i·r_i^2,\\ \mathbf{I}=\left[\begin{array}{ccc}{I}_{xx}& I_{xy}& I_{xz}\\ I_{yx}& I_{yy}& I_{yz}\\ I_{zx}& I_{zy}& I_{zz}\end{array}\right],\\ \mathbf{I}·\stackrel{.}{\mathsf{\omega }}\hspace{0.17em}+\mathsf{\omega }\times \left(\mathbf{I}·\mathsf{\omega }\right)=\mathsf{\tau }\end{array}\right.$	where $\sum m_i·r_i^2$ represents the scalar moment of inertia with $m_i$ as the mass of the $i$-th element and $r_i$ as its distance from the axis of rotation, $\mathbf{I}$ is the inertia tensor with components $I_{xx}, I_{xy}, I_{xz}, \dots$ representing moments and products of inertia, $\mathsf{\omega }$ is angular velocity, $\dot{\mathsf{\omega }}$ is angular acceleration, and $\mathsf{\tau }$ is torque.	[63,64]
12	Resistance coefficient	$F_D={\displaystyle \frac{1}{2}}{C}_D\rho v^{2}A$	${\mathrm{w}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{e} F}_D$ is drag force, $C_D$ is drag coefficient, $\rho$ is fluid density, $v$ is velocity, and $A$ is area.	[65]
13	Lift coefficient	$F_L={\displaystyle \frac{1}{2}}{C}_L\rho v^{2}A,$	${\mathrm{w}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{e} F}_L$ is lift force, $C_L$ is lift coefficient, $\rho$ is fluid density, $v$ is velocity, and $A$ is area.	[66,67]
14	Addition factors	$F_{\mathrm{added}}=-C_{\mathrm{added}}\rho V\ddot{x},$	${\mathrm{w}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{e} F}_{\mathrm{added}}$ is added mass force, $C_{\mathrm{added}}$ is added mass coefficient, $\rho$ is density, $V$ is volume, and $\ddot{x}$ is acceleration.	[68,69]
15	Pitch and yaw coefficients	$C={\displaystyle \frac{M}{\frac{1}{2}\rho V^{2}SL}}$	where $C$ is coefficient, $M$ is moment, $V$ is velocity, $S$ is reference area, and $L$ is characteristic length.	[70]
16	Traction	$F_{\mathrm{thrust}}=\dot{m}{v}_{\mathrm{exhaust}},$	where $F_{\mathrm{thrust}}$ is thrust force, $\dot{m}$ is mass flow rate, and $v_{\mathrm{exhaust}}$ is exhaust velocity.	[71]
17	Propulsion efficiency	$\eta ={\displaystyle \frac{T·v}{P}},$	where $\eta$ is efficiency, $T$ is thrust, $v$ is velocity, and $P$ is power.	[72]
18	Maneuverability	$\left\{\begin{array}{rr}R& ={\displaystyle \frac{v^2}{g·\mathrm{t}\mathrm{a}\mathrm{n}\left(\varphi \right)}},\\ \dot{\theta }& ={\displaystyle \frac{M_{\mathrm{angle}}}{I_{yy}}}.\end{array}\right.$	where $R$ is the turn radius, $v$ is velocity, $g$ is gravitational acceleration, $\varphi$ is the turning angle, $\dot{\theta }$ is the angular velocity, $M_{\mathrm{angle}}$ is the applied moment, and $I_{yy}$ is the moment of inertia about the $y$-axis.	[73,74,75]
19	Battery capacity	$E=C\times V,$	where $E$ is energy, $C$ is capacitance, $V$ is voltage,	[76,77]
20	Power consumption	$P=I\times V,$	where $P$ is power, $I$ is current, and $V$ is voltage	[78]
21	Accuracy and repeatability	$$\begin{gathered} \left\{\begin{array}{ll}A& =1-{\displaystyle \frac{\left|x_m-x_t\right|}{x_t}},\\ R& =\sqrt{{\displaystyle \frac{1}{n}}{\displaystyle \sum _{i=1}^n}{\left(x_i-\bar{x}\right)}^2}, \\ \\ \bar{x}& ={\displaystyle \frac{1}{n}}{\displaystyle \sum _{i=1}^n}{x}_i,\\ P& =w_1·A+w_2·{\displaystyle \frac{1}{R}},\end{array}\right. \end{gathered}$$	where $A$ is accuracy, $x_m$ is the measured value, $x_t$ is the true value, $R$ is the root-mean-square deviation, $x_i$ are individual values, $\bar{x}$ is the mean, $n$ is the number of observations, $P$ is the performance score, and $w_1,w_2$ are weighting coefficients.	[79,80]
22	Sensitivity	$S={\displaystyle \frac{\Delta O}{\Delta I}},$	where $S$ is sensitivity, $\Delta O$ is the change in output, and $\Delta I$ is the change in input.	[81,82,83]
23	Range and resolution	$\mathrm{R}={\displaystyle \frac{M}{2^n}},$	where $R$ is resolution, $M$ is the measurement range, and $n$ is the number of bits.	[84]
24	Response time	$\tau =-{\displaystyle \frac{t}{\mathrm{l}\mathrm{n}\left(1-P\right)}},$	where $\tau$ is the time constant, $t$ is time, and $P$ is probability.	[85]
25	Robustness	$\mathrm{R}=\underset{\Delta \in \mathcal{D}}{\mathrm{m}\mathrm{i}\mathrm{n}}\Vert H\left(\Delta \right)\Vert ,$	where $R$ is robustness, $\Delta$ represents uncertainties in the set $\mathcal{D}$, and $H\left(\Delta \right)$ is the transfer function.	[86]
26	Strength	$\sigma ={\displaystyle \frac{F}{A}},$	where $\sigma$ is stress, $F$ is force, and $A$ is the cross-sectional area.	[87]
27	Fatigue	$N_f={\left({\displaystyle \frac{{\sigma }_a}{\sigma {\mathrm{’}}_f}}\right)}^{-b}·{10}^c,$	where $N_f$ is fatigue life, ${\sigma }_a$ is the alternating stress, $\sigma {\mathrm{’}}_f$ is fatigue strength coefficient, $b$ is the fatigue strength exponent, and $c$ is a material constant.	[88]
28	Buckling resistance	$P_{cr}={\displaystyle \frac{{\pi }^{2}EI}{{\left(KL\right)}^2}},$	where $P_{cr}$ is the critical buckling load, $E$ is the modulus of elasticity, $I$ is the moment of inertia, $K$ is the effective length factor, and $L$ is the length.	[89,90]
29	Resistance to high pressure	${\sigma }_h={\displaystyle \frac{p_{d}D}{2t}},$	where ${\sigma }_h$ is hoop stress, $p_d$ is the internal pressure, $D$ is the diameter, and $t$ is the wall thickness.	[91]
30	Vibration resistance	${\omega }_n=\sqrt{{\displaystyle \frac{k}{m}}},$	where ${\omega }_n$ is the natural frequency, $k$ is stiffness, and $m$ is mass.	[92]
31	Signal attenuation	$A\left(d,f\right)=A_0{e}^{-\alpha \left(f\right)d},$	where $A\left(d,f\right)$ is amplitude, $A_0$ is the initial amplitude, $\alpha \left(f\right)$ is the attenuation factor, $d$ is the distance, and $f$ is frequency.	[93]
32	Capacity constraints	$B\left(f\right)={\mathrm{l}\mathrm{o}\mathrm{g}}_2\left(1+{\displaystyle \frac{S}{N\left(f\right)}}\right),$	where $B\left(f\right)$ is the channel capacity, $S$ is the signal power, and $N\left(f\right)$ is the noise power.	[94,95]
33	Multipath distribution	$y\left(t\right)={\displaystyle \sum _{i=1}^n}x\left(t-{\tau }_i\right)h_i+n\left(t\right),$	where $y\left(t\right)$ is the output, $x\left(t-{\tau }_i\right)$ is the delayed input, $h_i$ is the impulse response, $n\left(t\right)$ is noise, and $n$ is the number of components.	[96]
34	Acoustic bandwidth	$C=$ ${\int }_{f_{\min }}^{f_{\max }}{\mathrm{l}\mathrm{o}\mathrm{g}}_2\left(1+{\displaystyle \frac{S\left(f\right)}{N\left(f\right)}}\right)df,$	where $C$ is the channel capacity, $S\left(f\right)$ is the signal power spectrum, and $N\left(f\right)$ is the noise power spectrum.	[97]
35	Probability of errors in digital communication	$P_e=Q\left(\sqrt{{\displaystyle \frac{2E_b}{N_0}}}\right),$	where $P_e$ is the bit error probability, $E_b$ is the bit energy, and $N_0$ is the noise power spectral density.	[98]
36	Redundancy	$R_s=1-{\displaystyle \prod _{i=1}^n}\left(1-R_i\right),$	where $R_s$ is the system reliability and $R_i$ is the reliability of the $i$-th component.	[99,100,101,102]
37	Variety	$R_d=$ $1-{\displaystyle \prod _{j=1}^m}\left(1-{\displaystyle \prod _{i=1}^{n_j}}{R}_{ij}\right),$	where $R_d$ is the reliability of a dependent system, $R_{ij}$ is the reliability of the $i$-th component in the $j$-th subsystem, and $m$ is the number of subsystems.	[103]
38	Fault detection and isolation	$\left\{\begin{array}{l}y=Cx+Du+v,\\ \widehat{x}=Ax+Bu+L\left(y-\widehat{y}\right),\end{array}\right.$	where $y$ is the output, $x$ is the state vector, $u$ is the control input, $v$ is noise, $A$ is the state matrix, $B$ is the input matrix, $C$ is the output matrix, $D$ is the direct transmission matrix, and $L$ is the observer gain.	[104]
39	Disaster recovery	$x\left(t+1\right)=$ $Ax\left(t\right)+Bu\left(t\right)+Kf\left(t\right),$	where $x\left(t+1\right)$ is the state at the next time step, $A$ is the state transition matrix, $B$ is the control input matrix, $u\left(t\right)$ is the control input, $K$ is the disturbance gain, and $f\left(t\right)$ is the disturbance.	[105]
40	Reliability Engineering	$\mathrm{MTBF}={\displaystyle \frac{1}{\lambda }},$	where $\mathrm{MTBF}$ is the mean time between failures, and $\lambda$ is the failure rate.	[106]
41	State of production technology	$T\left(S\right)=$ ${\omega }_{1}R\left(S\right)+{\omega }_2\left({\displaystyle \frac{1}{C\left(S\right)}}\right)+{\omega }_{3}E\left(S\right),$	where $T\left(S\right)$ is the total performance, $R\left(S\right)$ is reliability, $C\left(S\right)$ is cost, $E\left(S\right)$ is efficiency, and ${\omega }_1, {\omega }_2, {\omega }_3$ are weighting factors.	[107]
42	Lithium reserves	$B\left(L\right)={\displaystyle \frac{\mu L}{1+\nu \left(L_0-L\right)}},$	where $B\left(L\right)$ is the behavior as a function of $L$, $\mu$ is a proportionality constant, $\nu$ is the adjustment factor, $L$ is the current level, and $L_0$ is the reference level.	[108,109,110]
43	Environmental safety	$S\left(E,C\right)=\eta {\left({\displaystyle \frac{E}{C}}\right)}^{-\sigma },$	where $S\left(E,C\right)$ is score, $\eta$ is efficiency, $E$ is energy, and $C$ is capacity	[111]
44	Motion vector	$m{\displaystyle \frac{d\overrightarrow{v}}{dt}}=$ $\overrightarrow{F}-\nabla P+{\overrightarrow{f}}_{\mathrm{drag}}+\overrightarrow{b},$	where $m$ is mass, $\overrightarrow{v}$ is velocity, $\overrightarrow{F}$ is external force, $\nabla P$ is the pressure gradient, ${\overrightarrow{f}}_{\mathrm{drag}}$ is the drag force, and $\overrightarrow{b}$ is the body force.	[112]
45	Diving depth	${\displaystyle \frac{dD}{dt}}=v_z-{\displaystyle \frac{\left({\rho }_{w}V-m\right)g}{m}},$	where ${\displaystyle \frac{dD}{dt}}$ is the rate of displacement, $v_z$ is vertical velocity, ${\rho }_w$ is water density, $V$ is volume, $m$ is mass, and $g$ is gravitational acceleration.	[113,114]
46	Orientation in space	$\dot{\mathbf{q}}={\displaystyle \frac{1}{2}}\mathbf{q}\otimes \mathsf{\omega },$	where $\dot{\mathbf{q}}$ is the time derivative of the quaternion, $\mathbf{q}$ is the quaternion, and $\mathsf{\omega }$ is the angular velocity vector.	[115]
47	System status	$\dot{\mathbf{s}}\left(t\right)=\mathbf{A}\mathbf{s}\left(t\right)+\mathbf{B}\mathbf{u}\left(t\right)$	where $\dot{\mathbf{s}}\left(t\right)$ is the time derivative of the state vector $\mathbf{s}\left(t\right)$, $\mathbf{A}$ is the system matrix describing the dynamics, $\mathbf{B}$ is the input matrix, and $\mathbf{u}\left(t\right)$ is the input vector at time $t$.	[116]
48	Communication signals	$\mathbf{c}\left(t\right)=$ ${\displaystyle \sum _{i=1}^N}{h}_i\left(t\right)\times {\mathbf{x}}_i\left(t\right)+n\left(t\right),$	where $\mathbf{c}\left(t\right)$ is the observed signal, $h_i\left(t\right)$ is the impulse response of the $i$-th channel, ${\mathbf{x}}_i\left(t\right)$ is the input signal for the $i$-th channel, $N$ is the total number of channels, and $n\left(t\right)$ is the noise component at time $t$.	[117]

).

Based on the assessments received from the experts, a summary matrix was created (

Table 2. Summary of matrix of expert evaluations.

No.	Expert Number
	1	2	3	4	5
1	9	6	5	7	7
2	8	5	7	4	8
3	7	4	5	3	9
4	7	5	6	4	10
5	10	7	9	8	6
6	9	7	8	7	5
7	6	4	7	3	8
8	5	3	6	2	7
9	9	6	5	7	4
10	8	8	10	9	5
11	7	8	7	10	4
12	9	7	6	8	5
13	8	6	5	9	3
14	7	6	5	8	4
15	7	5	4	9	3
16	8	10	7	10	5
17	8	10	6	10	4
18	8	9	6	10	5
19	7	10	5	8	4
20	6	9	4	7	3
21	5	8	4	8	3
22	5	8	4	8	3
23	5	8	4	8	3
24	4	9	3	7	2
25	5	8	6	8	4
26	5	4	10	6	6
27	4	3	9	5	5
28	5	4	10	6	5
29	6	4	10	6	6
30	5	4	9	5	5
31	4	7	3	6	2
32	4	6	3	6	2
33	4	6	3	6	2
34	4	7	3	6	2
35	4	7	3	6	2
36	6	9	5	10	4
37	5	8	4	9	3
38	5	9	4	10	3
39	6	8	4	10	4
40	7	6	5	9	5
41	6	7	6	8	4
42	3	4	2	3	9
43	3	2	3	2	10
44	8	7	5	9	6
45	10	7	8	9	7
46	8	6	5	10	4
47	6	8	5	9	5
48	5	8	4	8	4

).

In the matrix, there are tied ranks in the experts’ assessments, making it necessary to adjust these ranks. The process of rank adjustment is carried out without altering the opinions of the experts; that is, the relative order of the ranks (greater than, less than, or equal to) must be preserved. It is recommended that the rank values remain between 1 and the total number of parameters, where n = 48 in this case.

The statistical methods used in this study, including Pareto diagrams, Pearson’s chi-squared criterion, and Kendall’s coefficient of concordance, are standard techniques for ranking and validating expert opinions [35,36]. These methods ensure consistency and reliability in the parameter selection process.

Let us demonstrate the process of re-ranking for one of the experts, specifically Expert #1. The re-ranking process involves the following steps:

1. Sorting the parameters according to the expert’s assigned scores.

2. Identifying tied scores and calculating their mean ranks.

Initially, we have the following assessments from Expert #1:

Expert #1:

[9,8,7,7,7,10,9,9,6,5,9,8,8,8,7,7,7,7,7,8,8,8,8,7,6,5,5,5,5,5,4,4,5,5,5,6,6,6,5,6,6,7,6,3,3,8,8,10,8,8,6,6,5]

We first sort these scores and then calculate the ranks for tied scores.

Example calculation for the first few parameters:

Parameters with a score of 3 (positions 43, 44) will be assigned the ranks:

(1 + 2)/2 = 1.5

Parameters with a score of 4 (positions 24, 27, 31, 32, 33, 34, 35) will have the ranks:

(3 + 4 + 5 + 6 + 7 + 8 + 9)/7 ≈ 6

We shall now conduct a comprehensive computation for all parameters. The adjusted values represent the ranks and are allocated to all parameters within the identical score group.

Table 3. Determination of ranks based on scores.

No.	AUV Parameters	Significance	Rank
42	Lithium reserves	3	1
43	Environmental safety	3
24	Response time	4	3
27	Fatigue	4
31	Signal attenuation	4
32	Capacity constraints	4
33	Multipath distribution	4
34	Acoustic bandwidth	4
35	Probability of errors in digital communication	4
8	Biological impact of water	5	10
21	Accuracy and repeatability	5
22	Sensitivity	5
23	Range and resolution	5
25	Robustness	5
26	Strength	5
28	Buckling resistance	5
30	Vibration resistance	5
37	Variety	5
38	Fault detection and isolation	5
48	Communication signals	5
7	Chemical impact of water	6	21
20	Power consumption	6
29	Resistance to high pressure	6
36	Redundancy	6
39	Disaster recovery	6
41	State of production technology	6
47	System status	6
3	Water salinity	7	28
4	Water temperature	7
11	Inertia	7
14	Addition factors	7
15	Pitch and yaw coefficients	7
19	Battery capacity	7
40	Reliability Engineering	7
2	Water density	8	35
10	Weight	8
13	Lift coefficient	8
16	Traction	8
17	Propulsion efficiency	8
18	Maneuverability	8
44	Motion vector	8
46	Orientation in space	8
1	Underwater currents	9	43
6	Shock loads	9
9	Water resistance	9
12	Resistance coefficient	9
5	Hydrostatic loads	10	47
45	Diving depth	10

displays the re-ranked values for a single expert.

In a similar manner, the scores for all experts are converted into ranks, and a summary table of the adjusted ranks is then generated (

Table 4. Summary table of expert ranks.

No.	Expert Number
	1	2	3	4	5
1	43	14	18	18	40
2	35	11	36	6	43
3	28	4	18	3	45
4	28	11	29	6	47
5	47	22	42	23	36
6	43	22	40	18	26
7	21	4	36	3	43
8	10	2	29	1	40
9	43	14	18	18	15
10	35	31	45	33	26
11	28	31	36	41	15
12	43	22	29	23	26
13	35	14	18	33	7
14	28	14	18	23	15
15	28	11	9	33	7
16	35	46	36	41	26
17	35	46	29	41	15
18	35	41	29	41	26
19	28	46	18	23	15
20	21	41	9	18	7
21	10	31	9	23	7
22	10	31	9	23	7
23	10	31	9	23	7
24	3	41	2	18	1
25	10	31	29	23	15
26	10	4	45	10	36
27	3	2	42	8	26
28	10	4	45	10	26
29	21	4	45	10	36
30	10	4	42	8	26
31	3	22	2	10	1
32	3	14	2	10	1
33	3	14	2	10	1
34	3	22	2	10	1
35	3	22	2	10	1
36	21	41	18	41	15
37	10	31	9	33	7
38	10	41	9	41	7
39	21	31	9	41	15
40	28	14	18	33	26
41	21	22	29	23	15
42	1	4	1	3	45
43	1	1	2	1	47
44	35	22	18	33	36
45	47	22	40	33	40
46	35	14	18	41	15
47	21	31	18	33	26
48	10	31	9	23	15

).

Next, the deviation of the sum of ranks from the arithmetic-mean of the rank sums is calculated using the following expression:

$$d^2={\left(R_i-{\displaystyle \frac{\sum _{i=1}^n{R}_i}{n}}\right)}^2,$$

(1)

The deviation results for each category are presented in

Table 5. Summary table of standard deviation average.

No.	Expert Number					$\sum$	d	d2
	No. 1	No. 2	No. 3	No. 4	No. 5
1	43	14	18	18	40	134	2.63	6.89
2	35	11	36	6	43	133	1.63	2.64
3	28	4	18	3	45	101	−30.38	922.64
4	28	11	29	6	47	125	−6.38	40.64
5	47	22	42	23	36	175	43.63	1903.14
6	43	22	40	18	26	155	23.63	558.14
7	21	4	36	3	43	114	−17.38	301.89
8	10	2	29	1	40	90	−41.38	1711.89
9	43	14	18	18	15	117	−14.38	206.64
10	35	31	45	33	26	180	48.63	2364.39
11	28	31	36	41	15	162	30.63	937.89
12	43	22	29	23	26	155	23.63	558.14
13	35	14	18	33	7	120	−11.38	129.39
14	28	14	18	23	15	112	−19.38	375.39
15	28	11	9	33	7	103	−28.38	805.14
16	35	46	36	41	26	200	68.63	4709.39
17	35	46	29	41	15	183	51.63	2665.14
18	35	41	29	41	26	190	58.63	3436.89
19	28	46	18	23	15	149	17.63	310.64
20	21	41	9	18	7	116	−15.38	236.39
21	10	31	9	23	7	101	−30.38	922.64
22	10	31	9	23	7	102	−29.38	862.89
23	10	31	9	23	7	103	−28.38	805.14
24	3	41	2	18	1	89	−42.38	1795.64
25	10	31	29	23	15	133	1.63	2.64
26	10	4	45	10	36	131	−0.38	0.14
27	3	2	42	8	26	108	−23.38	546.39
28	10	4	45	10	26	123	−8.38	70.14
29	21	4	45	10	36	145	13.63	185.64
30	10	4	42	8	26	120	−11.38	129.39
31	3	22	2	10	1	69	−62.38	3890.64
32	3	14	2	10	1	62	−69.38	4812.89
33	3	14	2	10	1	63	−68.38	4675.14
34	3	22	2	10	1	72	−59.38	3525.39
35	3	22	2	10	1	73	−58.38	3407.64
36	21	41	18	41	15	172	40.63	1650.39
37	10	31	9	33	7	127	−4.38	19.14
38	10	41	9	41	7	146	14.63	213.89
39	21	31	9	41	15	156	24.63	606.39
40	28	14	18	33	26	159	27.63	763.14
41	21	22	29	23	15	151	19.63	385.14
42	1	4	1	3	45	96	−35.38	1251.39
43	1	1	2	1	47	95	−36.38	1323.14
44	35	22	18	33	36	188	56.63	3206.39
45	47	22	40	33	40	227	95.63	9144.14
46	35	14	18	41	15	169	37.63	1415.64
47	21	31	18	33	26	176	44.63	1991.39
48	10	31	9	23	15	136	4.63	21.39
Sum	1022	1029	1027	1031	1021	6306	0	69,807.25

.

The factors are ranked by importance based on the total ranking of each parameter. The results are summarized in

Table 6. Ranking factors by their importance.

Parameter Name	No.	Sum of Ranks
Capacity constraints	32	62
Multipath distribution	33	63
Signal attenuation	31	69
Acoustic bandwidth	34	72
Probability of errors in digital communication	35	73
Response time	24	89
Biological impact of water	8	90
Environmental safety	43	95
Lithium reserves	42	96
Water salinity	3	101
Accuracy and repeatability	21	101
Sensitivity	22	102
Pitch and yaw coefficients	15	103
Range and resolution	23	103
Fatigue	27	108
Addition factors	14	112
Chemical impact of water	7	114
Power consumption	20	116
Water resistance	9	117
Lift coefficient	13	120
Vibration resistance	30	120
Buckling resistance	28	123
Water temperature	4	125
Variety	37	127
Strength	26	131
Water density	2	133
Robustness	25	133
Underwater currents	1	134
Communication signals	48	136
Resistance to high pressure	29	145
Fault detection and isolation	38	146
Battery capacity	19	149
State of production technology	41	151
Shock loads	6	155
Resistance coefficient	12	155
Disaster recovery	39	156
Reliability Engineering	40	159
Inertia	11	162
Orientation in space	46	169
Redundancy	36	172
Hydrostatic loads	5	175
System status	47	176
Weight	10	180
Propulsion efficiency	17	183
Motion vector	44	188
Maneuverability	18	190
Traction	16	200
Diving depth	45	227

.

The average degree of consistency in the opinions of all experts is estimated using the coefficient of concordance, particularly in cases where there are tied ranks (i.e., the same rank values given by an expert).

Coefficient of concordance is calculated by the following expression:

$$W={\displaystyle \frac{12\sum _{j=1}^n{R}_j^2-3m^{2}n{(n+1)}^2}{m^{2}n(n^2-1)}}=0.772723,$$

(2)

If W > 0.5, then there is consistency among the experts’ opinions.

Subsequently, we assess the importance of the concordance coefficient. To do this, we compute Pearson’s chi-squared (χ²) consistency criteria

$${\chi }^2=\sum _{i=1}^k{\displaystyle \frac{{(O_i-E_i)}^2}{E_i}},$$

(3)

To evaluate the significance of the concordance coefficient W using Pearson’s chi-squared criterion (χ²), we apply the following formula:

$${\chi }^2=W·m·(n-1),$$

(4)

where W is the Kendall’s coefficient of concordance, m is the number of experts, and n is the number of parameters.

Given values:

W = 0.772723,

m = 5,

n = 48.

Substituting these values into the formula

$${\chi }^2=0.772723·5·(48-1),$$

(5)

$${\chi }^2\approx 181.489905,$$

(6)

we now have a calculated χ² value, which can be compared to the critical value from the chi-squared distribution table to determine statistical significance.

Degrees of freedom:

$$df=n-1=47,$$

(7)

Using the chi-squared distribution table for 47 degrees of freedom, we evaluate significance at the 0.05 or 0.01 significance levels. Typically, for 47 degrees of freedom and a significance level of 5%, the critical value is approximately 63.167, which is much smaller than our calculated value of 181.61, indicating that the concordance coefficient is statistically significant.

According to Table 6, “Ranking of factors by their importance”, the λ weight of each parameter is calculated based on the sum of ranks R across all experts. To do this, we normalize the sum of ranks by taking the total sum of ranks as one, and then calculate each parameter’s weight using the formula

$${\lambda }_i={\displaystyle \frac{R_i}{\sum _{i=48}^n{R}_i}},$$

(8)

The weight for the first parameter is calculated, and similar calculations are performed for the remaining parameters (

Table 7. Calculation of parameter weights.

Parameter’s Name	No.	Sum of Ranks	Weight
Capacity constraints	32	62	0.0098
Multipath distribution	33	63	0.0100
Signal attenuation	31	69	0.0109
Acoustic bandwidth	34	72	0.0114
Probability of error in digital communication	35	73	0.0116
Response time	24	89	0.0141
Biological impact of water	8	90	0.0143
Environmental safety	43	95	0.0151
Lithium reserves	42	96	0.0152
Water salinity	3	101	0.0160
Accuracy and repeatability	21	101	0.0160
Sensitivity	22	102	0.0162
Pitch and yaw coefficients	15	103	0.0163
Range and resolution	23	103	0.0163
Fatigue	27	108	0.0171
Addition factors	14	112	0.0178
Chemical impact of water	7	114	0.0181
Power consumption	20	116	0.0184
Water resistance	9	117	0.0186
Lift coefficient	13	120	0.0190
Vibration resistance	30	120	0.0190
Buckling resistance	28	123	0.0195
Water temperature	4	125	0.0198
Variety	37	127	0.0201
Strength	26	131	0.0208
Water density	2	133	0.0211
Robustness	25	133	0.0211
Underwater currents	1	134	0.0212
Communication signals	48	136	0.0216
Resistance to high pressure	29	145	0.0230
Fault detection and isolation	38	146	0.0232
Battery capacity	19	149	0.0236
State of production technology	41	151	0.0239
Shock loads	6	155	0.0246
Resistance coefficient	12	155	0.0246
Disaster recovery	39	156	0.0247
Reliability Engineering	40	159	0.0252
Inertia	11	162	0.0257
Orientation in space	46	169	0.0268
Redundancy	36	172	0.0273
Hydrostatic loads	5	175	0.0278
System status	47	176	0.0279
Weight	10	180	0.0285
Propulsion efficiency	17	183	0.0290
Motion vector	44	188	0.0298
Maneuverability	18	190	0.0301
Traction	16	200	0.0317
Diving depth	45	227	0.0360

)

$${\lambda }_i={\displaystyle \frac{134}{6306}}=0.0212,$$

(9)

According to Table 7, “Calculation of Parameter Weights”, it was decided to consider significant only those parameters that, based on the Pareto diagram (Figure 2), have a weight λ ≥ 0.02166. The selected parameters are listed in

Table 8. Parameters selected as the most significant for AUV.

Parameter’s Name	No.	Sum of Ranks	Weight
Diving depth	45	227	0.0360
Traction	16	200	0.0317
Maneuverability	18	190	0.0301
Motion vector	44	188	0.0298
Propulsion efficiency	17	183	0.0290
Weight	10	180	0.0285
System status	47	176	0.0279
Hydrostatic loads	5	175	0.0278
Redundancy	36	172	0.0273
Orientation in space	46	169	0.0268
Inertia	11	162	0.0257
Reliability Engineering	40	159	0.0252
Disaster recovery	39	156	0.0247
Shock loads	6	155	0.0246
Resistance coefficient	12	155	0.0246
State of production technology	41	151	0.0239
Battery capacity	19	149	0.0236
Fault detection and isolation	38	146	0.0232
Resistance to high pressure	29	145	0.0230
Communication signals	48	136	0.0216

.

The red line in Figure 2 represents the cumulative percentage of the total impact of all parameters, providing a visual threshold for determining significant parameters.

In the Pareto diagram, the y-axis represents the weights of the parameters, both in numerical values and cumulative percentages. The x-axis displays the process parameters arranged in descending order of importance. The first columns of the diagram indicate the most critical process parameters, while all other parameters beyond the separating cumulative curve remain unchanged due to their minimal ability to influence the outcome.

By focusing on the significant factors identified through the Pareto analysis, efforts can be concentrated on addressing the most impactful issues.

The development of any system design or software begins with the creation of a conceptual model of the object. This model integrates the key concepts that describe the area of study. A crucial element in this process is the analysis of scientific literature, which provides a clear understanding of the structure of the object and its interrelated components [37].

A graphical representation of a conceptual model is typically preferred over a textual description, as diagrams and charts can more effectively visualize the primary relationships between elements [38]. The concepts included in the model are detailed and interpreted to ensure the model can serve as a foundation for subsequent phases, such as the development of data collection methods and system architecture [39]. Some of the terms within the model may be applied in a mathematical context, allowing for their description through equations and formulas.

The development of the conceptual model is based on data obtained from expert assessments and the identification of key parameters [40]. Table 8 serves as the foundation for the model. An autonomous underwater vehicle is selected as the central object, focusing on the most significant input and output parameters identified in the table. The conceptual model, which illustrates these technological parameters, is presented in Figure 3.

By systematizing the equations that describe the components of the AUV ICS and selecting the most significant elements from these components, a mathematical model of the object is obtained

$$\left\{\begin{array}{c}{\rho }_w·V·g=m·g\\ \left\{\begin{array}{c}I=\sum m_i·r_i^2\\ I=\left[\begin{array}{ccc}{I}_{xx}& I_{xy}& I_{xz}\\ I_{yx}& I_{yy}& I_{yz}\\ I_{zx}& I_{zy}& I_{zz}\end{array}\right]\\ I·\stackrel{.}{\omega }+\omega \times (I·\omega )=\tau \end{array}\right.\\ F_{\mathrm{thrust}}=\dot{m}{v}_{\mathrm{exhaust}}\\ \left\{\begin{array}{c}R=\left(v^2\right)/(g\times \tan (\varphi \left)\right)\\ \dot{\theta }={\displaystyle \frac{M_{angle}}{I_{yy}}}\end{array}\right.\\ {\sigma }_h={\displaystyle \frac{p_{d}D}{2t}}\\ \eta =(T\times v)/P\\ \left\{\begin{array}{c}y=Cx+Du+v\\ \widehat{x}=Ax+Bu+L(y-\widehat{y})\end{array}\right.\\ E=C\times V\\ x(t+1)=Ax\left(t\right)+Bu\left(t\right)+Kf\left(t\right)\\ F_D={\displaystyle \frac{1}{2}}{C}_D\rho v^{2}A\\ R_s=1-{\displaystyle \prod _{i=1}^n}(1-R_i)\\ P_{cr}={\displaystyle \frac{{\pi }^{2}EI}{{\left(KL\right)}^2}}\\ \sigma ={\displaystyle \frac{F}{A}}\\ T(S)={\omega }_{1}R(S)+{\omega }_2\left({\displaystyle \frac{1}{C(S)}}\right)+{\omega }_{3}E(S)\\ MTBF={\displaystyle \frac{1}{\lambda }}\\ m{\displaystyle \frac{d\overrightarrow{v}}{dt}}=\overrightarrow{F}-\mathsf{\nabla }P+{\overrightarrow{f}}_{drag}+\overrightarrow{b}\\ {\displaystyle \frac{dD}{dt}}=v_z-{\displaystyle \frac{({\rho }_{w}V-m)g}{m}}\\ \left\{\begin{array}{c}q={(}^{q_0})\\ \stackrel{.}{q}={\displaystyle \frac{1}{2}}q\otimes \omega \end{array}\right.\\ \left\{\begin{array}{c}s\left(t\right)={[}^{s_1}]\\ \stackrel{.}{s}\left(t\right)=\mathrm{As}\left(t\right)+\mathrm{Bu}\left(t\right)\end{array}\right.\\ c(t)={\displaystyle \sum _{i=1}^N}{h}_i(t)\times x_i(t)+n(t)\end{array}\right.,$$

(10)

The mathematical model for the AUV’s information and control system is a system of equations that estimates and determines the vehicle’s operational parameters, which influence its key characteristics. This model also establishes both internal and external relationships between various systems and components of the vehicle. The results derived from this model can be used to develop algorithms for the optimal control of the AUV.

Thus, the AUV ICS model accounts for the following key indicators (

Table 9. Key indicators.

Key Indicators	Description of the Indicator
Communication signals	Determines the capability and quality of underwater communication
Resistance to high pressure and hydrostatic loads	Critical for underwater conditions
Fault detection and isolation	Ensures stable operation when issues arise
Battery capacity and propulsion efficiency	Essential for autonomy and mission duration
The state of manufacturing technology	Affects the quality and reliability of all components
Shock loads and reliability engineering	Ensures the durability of the apparatus
Drag coefficient and motion vector	Crucial for hydrodynamic performance
Disaster recovery	Essential for maintaining functionality
Inertia and spatial orientation	Determines maneuverability and positioning accuracy
Redundant systems	Improves overall reliability
Mass and thrust	Influences energy efficiency and dive depth

).

These factors collectively determine the operability and efficiency of the AUV ICS in the specified operating conditions.

3. Results

Based on the correlation analysis and expert assessments, a conceptual approach to developing the information and control system for an autonomous underwater vehicle was established. During the research, the key parameters influencing the performance efficiency of the AUV in executing tasks for the MRMC were identified and ranked.

By processing expert opinions, a summary table of ranks was created, and the significance of each parameter was calculated using Kendall’s coefficient of concordance. The coefficient value, W = 0.772723, indicates a high level of agreement among the experts, confirming the validity of the ranking process. The calculation of the Pearson consistency criterion revealed that the obtained value significantly exceeded the critical value, indicating the statistical significance of this study.

From the analysis of 48 AUV parameters, the most significant factors impacting vehicle performance and efficiency were identified. These key parameters include communication signals, high-pressure resistance, fault detection and isolation, battery capacity, state of manufacturing technology, shock loads and reliability engineering, drag coefficient, failure recovery, inertia, spatial orientation, redundancy, hydrostatic loads, system condition, mass, propulsion efficiency, propulsion vector, maneuverability, thrust, and dive depth.

Parameters with a weight exceeding the threshold value of 0.0216 (i.e., λ ≥ 0.0216) were selected for further analysis and the construction of the conceptual model of the AUV. The final Pareto diagram demonstrated that these parameters collectively define the primary functional capabilities and characteristics of the AUV’s information and control system, allowing for a focused optimization of these critical aspects.

Consequently, a conceptual model of the AUV control system was constructed based on the most significant factors. This model features the AUV as the central object and highlights the key input and output parameters that influence its operational efficiency. The developed model provides a foundation for the further design of control systems and the optimization of AUV operations under the complex underwater conditions typical of mineral and raw materials complex tasks.

Future iterations of this model could incorporate control strategies, such as PID controllers or reinforcement learning algorithms, to optimize the AUV’s response to dynamic underwater conditions.

4. Discussion

The results of this study indicate that the chosen approach to parameter ranking and significance analysis effectively identifies the most critical aspects of AUV operation in underwater conditions.

The model constructed in this study does not explicitly implement control strategies. Instead, it serves as informational support for a subsequent phase, where control algorithms, such as feedback or adaptive control, can be integrated into the AUV’s design. For instance, parameters like propulsion efficiency and maneuverability directly inform the development of trajectory and motion control algorithms.

The primary focus was placed on factors that determine the stability, reliability, and efficiency of AUVs in executing tasks related to the exploration and extraction of mineral resources at great depths. For instance, parameters such as resistance to high pressure and hydrostatic loads were identified as among the most important, reflecting the extreme operating conditions encountered by submersibles. These parameters play a key role in ensuring the stability and functionality of the vehicle in deepwater environments, where environmental pressures significantly impact both design and control systems.

Another crucial aspect identified in this study is the reliability of the communication systems. Stable and reliable communication signals are essential for data transmission and real-time operational control of the AUV. Given the complexity of the underwater environment, the development of efficient and resilient communication systems is a prerequisite for improving operational efficiency and enabling prompt decision-making. In this context, it is also important to account for the probability of errors in digital communications and implement methods to mitigate them.

Additionally, parameters related to power consumption and vehicle autonomy were recognized as significant. Battery capacity, propulsion efficiency, and the vehicle’s ability to recover from failures are vital for ensuring prolonged and uninterrupted operation, especially critical during long-duration exploration and mining missions. High autonomy reduces the need for frequent maintenance, which is particularly important in hard-to-reach areas such as the seabed.

It is also worth emphasizing the importance of parameters like spatial orientation and maneuverability. An AUV must exhibit high maneuverability and positioning accuracy to successfully perform complex tasks in confined environments, necessitating the optimization of its hydrodynamic characteristics and motion control systems.

The results of the consistency analysis of expert opinions demonstrated that the proposed approach to assessing parameter significance and ranking provides a reliable foundation for the design and development of the AUV’s information and control system. This study involved five experts, all of whom are highly experienced in underwater robotics and AUV systems. Their expertise was instrumental in identifying and ranking parameters, ensuring the robustness of the findings. However, it is important to acknowledge the limitations of this study. The small size of the expert panel and its limited geographic representation could influence the generalizability of the results. Technologies and strategies in AUV design can vary significantly across regions, highlighting the potential benefits of including a larger and more geographically diverse group of experts in future studies.

Additionally, while the methods employed in this study—such as correlation analysis and ranking techniques—were effective, they could be refined or supplemented with alternative evaluation approaches to better account for the interrelationships between parameters and their collective impact on system performance. Future research will focus on addressing these limitations by expanding the expert panel and incorporating international perspectives, thereby enhancing the comprehensiveness and applicability of the findings.

A critical future step involves validating the proposed model in a simulated environment replicating real-world deep-sea conditions. Such simulations will evaluate the ICS’s predictive power and confirm the relevance of identified parameters under dynamic environmental influences.

In conclusion, the proposed conceptual model of the AUV’s ICS provides a structured basis for further optimization of the vehicle control systems. By incorporating critical operational parameters, this model can enhance the efficiency of AUV operations under challenging underwater conditions, improve the reliability of operations, and ensure that tasks related to the MRMC are performed with a high degree of autonomy and precision.

5. Conclusions

This study presents the development of a conceptual model for the information and control system of an autonomous underwater vehicle designed to address challenges in the mineral and raw materials complex. By employing correlation analysis and expert evaluations, this study successfully identified the most significant parameters that influence the efficiency and reliability of AUV operations in deepwater environments.

The key findings indicate that resistance to high pressure, the reliability of communication systems, autonomy, and maneuverability are the critical factors determining the functionality and performance of the vehicle. The development of the AUV’s ICS model, incorporating these parameters, enhances the design and operational capabilities of the vehicle, enabling more efficient and safer execution of tasks related to the exploration and extraction of seabed mineral resources.

The proposed approach to parameter ranking and evaluation can be utilized in future efforts to optimize AUV control and improve its performance characteristics. However, for a more comprehensive assessment of the ICS’s effectiveness and its practical implementation, further research is needed. This includes expanding the expert panel, employing more advanced methods to analyze the interrelationships between parameters, and conducting practical tests in real underwater conditions.

Overall, the conceptual model developed in this study serves as a foundation for further research and development in underwater robotics, aimed at improving the efficiency and safety of operations within the mineral and raw materials complex. The integration of autonomous underwater vehicles with an optimized ICS will significantly boost productivity and reduce risks in the exploration and production of underwater mineral resources, opening new opportunities for industry advancement.