## 3 Development of a Group Interaction System
Its structure is shown in the Figure 13.  
This planning method was based on the method of potential fields, which provides local planning of the group's path, and the RRT* method, which forms the global trajectory of the group's motion. We successfully carried out full-scale trials confirming the efficiency of the developed algorithms [61]. Development of an Algorithm for a Single Field of View of the MRC Group  
It should be noted that it is of high importance that the group is able to perceive the overall environment for the best decision making in a dynamically changing environment. In view of this, we developed an algorithm that allows implementing a single field of view of the group, and, thus provides the best options for formation reconfiguration and motion in a non-deterministic environment. The developed algorithm combines a system for data aggregation from the hydroacoustic and/or optical sensor system of each AUV and a system responsible for the change in each AUV state and its transition to the appropriate mode. Based on the data received from the sensor system, we analyze what percentage of its coverage area is occupied by an area with a visible obstacle. The sensory system model can be represented with the following Equation (7):  
Figure 13: Developed hybrid path-planning system for the MRC group. \[ \begin{array}{l}\sigma\in\{\psi-\xi/2,\psi+\xi/2\}\\ x_{son}=x_{c}+R_{range}\ cos(\sigma)\\ y_{son}=y_{c}+R_{range}\ sin(\sigma)\end{array}  \tag{7}\]  
where \(\psi\) is orientation angle; \(\xi\)--a sonar and/or lidar viewing width; \(\sigma\)--sector received by sonar. Variables \(x_{son}\) and \(y_{son}\) describe the received coordinates of the sector, in which the data is captured by the sonar or lidar. The array of all calculated points \(\beta\) can be written as (8):  
\[\beta=[x_{son},y_{son}] \tag{8}\]  
An obstacle detection occurs when the condition below is met (9)  
\[P_{p}=\beta\cap obs, \tag{9}\]  
where \(obs\) is the positions of obstacles. Since the ray can cross the obstacle twice, it is necessary to calculate the distances to the points of intersection and select the minimum point from the resulting array in relation to the AUV (10):  
\[ \begin{array}{l}D_{\beta}=|p_{start}-P_{p}|\\ P_{obs}=min(P_{p}(D_{\beta}))\end{array}  \tag{10}\]  
Based on the data received from the sensor system, we can analyze what percentage of its coverage area is occupied by an area with a visible obstacle. To do so, let us compare a number of parameter \(P_{p}\) values and the rays \(\beta\) (as the number of rays that are out of the obstacle area) and describe the obstacle avoidance area for each AUV. Examples of how the total field of view can be estimated are shown in the Figure 14.  
The comparison made allows estimating the percentage of the sensory system being filled with the range of free \(\Omega\) values. It is also possible to estimate the overall completeness of the \(\Omega_{total}\) sensory system by the total sum of all \(\Omega_{i}\) parameters from each AUV, shown in the Figure 15.  
The presented algorithm makes it possible to complement the group management system of the AUV group based on the existing common field of view. The possibility of using this algorithm in devices operating in natural environments requires a laboratory experiment, since the efficiency of the algorithm directly depends on the external conditions of the marine environment, such as turbidity of water and other factors. The presented algorithm makes it possible to complement the group management system of the AUV group based on the existing common field of view. Figure 14: Draft of the general field of view and decision-making on reconfiguration by the AUV group. Determination of the \(\Omega_{total}\) area completeness percentage will allow making up the criteria for the transition of the group to various formation reconfiguration scenarios. The research forms a basis for the authors to conduct the future researches.