=Paper=
{{Paper
|id=Vol-3403/paper22
|storemode=property
|title=Solving the Task of Topological Formation Intelligent Mobile «S-bots» for One «Swarm-bot» System
|pdfUrl=https://ceur-ws.org/Vol-3403/paper22.pdf
|volume=Vol-3403
|authors=Gennady Krivoulya,Nikolay Koshevoy,Volodymyr Tokariev,Iryna Ilina,David Dubinsky
|dblpUrl=https://dblp.org/rec/conf/colins/KrivoulyaKTID23
}}
==Solving the Task of Topological Formation Intelligent Mobile «S-bots» for One «Swarm-bot» System==
https://ceur-ws.org/Vol-3403/paper22.pdf
Solving the Task of Topological Formation Intelligent Mobile «S-
bots» for One «Swarm-bot» System
Gennady Krivoulya1, Nikolay Koshevoy2, Volodymyr Tokariev1, Iryna Ilina1, and David
Dubinsky1
1
Kharkiv National University of Radio Electronics, 14 Nauky Ave., Kharkiv, 61166, Ukraine
2
National Aerospace University «Kharkov Aviation Institute», Kharkiv, 61070, Ukraine
Abstract
When controlling the movement of intelligent mobile «s-bots» that are part of one «Swarm-
bot» system, unforeseen situations may arise in a physically disorganized environment. In such
a case, it is necessary that the algorithm built into each «s-bot» be launched, which enables all
intelligent mobile «s-bots» to rebuild their parameters and function stably in this physically
disorganized environment. When such situations arise, prompt solutions are required. Solving
such situations can include the task of overcoming any obstacle in a physically disorganized
environment. Then, the built-in algorithm should include mechanisms for forming various
topologies of intelligent mobile «s-bots». The authors of this work propose to use a
mathematical apparatus, Poya’s enumeration, to solve such a problem. When solving the task
of overcoming any obstacle in a physically unorganized environment, it is possible to program
the «Swarm-bot» system in such a way that each variant of the formed topology for intelligent
mobile «s-bots» corresponds to the shape of the obstacle.
Keywords 1
«Swarm-bot» systems, intelligent mobile «s-bot», physical unorganized environment,
embedded system, embedded algorithm
1. Introduction
In the present day, intelligent mobile «s-bots» that are part of «Swarm-bot» systems have found wide
usage. They play a high relevance in areas that are related to reducing the risk to human life. The
advantage of using «Swarm-bot» systems is due to the fact that such systems have the properties of
tunable structures and programmable logic, reconfiguration, and therefore resistance to failures. The
use of «Swarm-bot» systems makes it possible to increase the radius of action of such systems, due to
the increase in the number of intelligent mobile «s-bots» included in their composition, and the
expanded set of tasks that can be performed significantly increases the probability of achieving the set
goal. Today, «Swarm-bot» systems, which include intelligent mobile «s-bots», are capable of
performing the following tasks:
- to protect the external and internal territory of the specified objects;
- for power structures in a physically disorganized environment;
- search and rescue;
- on providing assistance in the agricultural sector, and others.
Intelligent mobile «s-bots», having the properties of tunable structures and programmable logic, can
more accurately determine the location of the desired target points, but today they have a drawback,
which manifests itself in the low speed of detecting these ground target points. When managing
1COLINS-2023: 7th International Conference on Computational Linguistics and Intelligent Systems, April 20-21, 2023, Kharkiv, Ukraine
EMAIL: krivoulya@yahoo.com (G. Krivoulya); kafedraapi@ukr.net (N. Koshevoy); tokarev@ieee.org (V. Tokariev); ilirvi_71@ukr.net (I.
Ilina); D.Dbbinsky@nure.ua (D. Dubinsky)
ORCID: 0000-0002-6143-5628 (G. Krivoulya); 0000-0001-9465-4467 (N. Koshevoy); 0000-0002-7143-6165 (V. Tokariev); 0000-0003-3132-
7949 (I. Ilina); 0000-0002-5479-4438 (D. Dubinsky)
©️ 2023 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
intelligent mobile «s-bots» that are part of «Swarm-bot» systems, it is necessary to have highly
specialized algorithms that are necessary for:
- motion control by intelligent mobile «s-bots», which does not take into account dynamic change
in the physical unorganized environment;
- management of the formation of the topology of intelligent mobile «s-bots» while moving,
provided that these intelligent mobile «s-bots» that are part of one «Swarm-bot» system have a certain
status.
In practice, intelligent mobile «s-bots» move without a priori knowledge of the external physical
unorganized environment and perform topology correction of the «Swarm-bot» system when solving a
task or subtasks in order to achieve the set goal. The authors of this work solve the scientific problem
of developing a highly specialized built-in algorithm for controlling intelligent mobile «s-bots» that are
part of one «Swarm-bot» system. When solving the task, the built-in highly specialized algorithm
controls the formation of the topology of intelligent mobile «s-bots» regardless of the stage of the life
cycle of the entire «Swarm-bot» system. To achieve the set scientific task, it is necessary that intelligent
mobile «s-bots» be able, using built-in algorithms:
- reconfigure their structures;
- rebuild your settings;
- to function in various conditions created by the physical unorganized environment [1,2].
2. Related Works
In the scientific paper «Mathematical Model for Finding Probability of Detecting Victims of Man-
Made Disasters Using Distributed Computer System with Reconfigurable Structure and Programmable
Logic», the authors consider issues related to the development of methods for planning the rescue stages
of victims of man-made disasters, using computer distributed systems with programmable logic and
reconfigurable structure. The paper examines the issues of forming a rescue «s-bot» team based on
them, with the creation of control modules that perform search, task allocation and planning trajectories
moving in an unorganized physical environment with obstacles. The authors solve the problem of
exploring each cell in the time given for exploring the entire workspace, maximizing the probability of
target detection and probabilistic characteristics of various search strategies. The authors conclude, that
the solution of these tasks will allow the effective performance of rescue operations [3]. The authors of
the scientific work «Algorithm of Iterations of Distribution of Subtasks Between «S-Bot» in One
«Swarm-Bot» System» consider issues related to the study of the possibility of using a centralized
particle swarm algorithm for the distribution of subtasks between «s-bot» and one «Swarm -bot»
systems to solve the main task. On the basis of the centralized particle swarm algorithm, the authors
developed a sequence of iterations, which showed that it is an effective algorithm, as it allows to find
the best solution to the task much faster. It was established that the developed algorithm of iterations
based on the centralized particle swarm algorithm is characterized by simplicity of operation, a small
set of input parameters that need to be set at the first iteration, sufficiently acceptable accuracy, and
what especially encouraged the authors is that the developed algorithm has a fast convergence to the
optimal solution [4].
In the scientific work «Implementation of combined method in constructing a trajectory for structure
reconfiguration of a computer system with reconstructible structure and programmable logic», the
authors established that in order to neutralize threats and minimize losses caused by unusual,
emergency, extraordinary and catastrophic situations, leading to the avalanche-like increase in
degradation processes and the destruction of computer systems with a reconfigurable structure and
programmable logic requires the development of new principles, approaches, methods and methods of
operational monitoring, analysis and forecasting of situations, the development of options for
management solutions, procedures for their selection and implementation within the framework of the
theory of structural dynamics management. To solve the optimization task of creating scenarios for the
structural reconfiguration of computer systems, the authors of the article proposed a method and an
algorithm implementing it, the novelty of which consists in the combined use of the random directed
search method and the method of cutting off unpromising variants of the structural reconfiguration of
computer systems of the «branches and boundaries» type. The authors believe that the proposed
approach allows solving the optimization tasks of building scenarios for structural reconfiguration of
computer systems [5]. The scientific publication "Coordinated Route Planning of Multiple Fuel-
constrained Unmanned Aerial Systems with Recharging on an Unmanned Ground Vehicle for Mission
Coverage" solves the difficult task of recharging unmanned aerial vehicles - UAVs. The scientific
contribution of the authors of the article is to develop a heuristic for choosing the UAV route, without
a significant increase in computation time [6].
The scientific publication "Vector Field based Control of Quadrotor UAVs for Wildfire Boundary
Monitoring" solves the problem of monitoring the dynamic boundaries of forest fires using UAVs
equipped with onboard cameras. The forest fire boundary is described using the zero level set function,
and its change is modeled using the Hamilton-Jacobi equation [7].
In the scientific papers "A Novel Method for Distinguishing Indoor Dynamic and Static Semantic
Objects Based on Deep Learning and Space Constraints in Visual-inertial SLAM" and "Bayesian
Optimization-based Three-dimensional, Time-varying Environment Monitoring using an UAV" the
authors consider questions interactions of the "Swarm-bot" system with a physical unorganized
environment, using on-board cameras [8,9].
In the scientific work "Distributed Fault Estimation and Fixed-Time Fault-Tolerant Formation
Control for Multi-UAVs subject to Sensor Faults", the authors consider the problems of reconfiguration
and fault tolerance that may arise during the operation of unmanned aerial vehicles - UAVs [10]. In the
scientific papers “Deep Learning for Safe Autonomous Driving: Current Challenges and Future
Directions” and “T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction”, the
authors use deep learning methods and graph theory to solve the “Swarm-bot” traffic optimization
problem » system in a physical unorganized environment [11,12].
In the scientific work “Multi-fidelity black-box optimization for time-optimal quadrotor
maneuvers”, the authors explore the problem of multi-point optimization as a black box for solving the
problem of achieving time-optimal maneuvers of unmanned aerial vehicles - UAVs [13]. In the
scientific papers "Kimera: From SLAM to spatial perception with 3D dynamic scene graphs" and "Lane
Detection Method with Impulse Radio Ultra-Wideband Radar and Metal Lane Reflectors", the authors
explore the problem of the interaction of the "Swarm-bot" system with a physical unorganized
environment. [14,15]. In the scientific work "Assistive Robotic Technologies for Next-Generation
Smart Wheelchairs", the authors explore the development trends of next-generation robotic
technologies [16]. The scientific work "Resilient Trajectory Propagation in Multirobot Networks"
explores the interaction between "s-bots" of one "Swarm-bot" system [17]. In the scientific work
"Multi-UAV planning for cooperative wildfire coverage and tracking with quality-of-service
guarantees", the authors investigate the problem of achieving the target functionality when using several
unmanned aerial vehicles - UAVs [18]. In the scientific work "Modified Gray Codes for the Value
(Time) Optimization of a Multifactor Experiment Plans", the authors explore the possibility of using
the modified Gray code to optimize the cost (time) of plans when conducting multifactorial experiments
[19]. In the scientific papers "Artificial intelligence in the Internet of things" and "Modelling and
verification of reconfigurable multi-agent systems", the authors study models for reconfiguring multi-
agent systems using artificial intelligence [20,21]. In the scientific work "Automatic calibration of
dynamic and heterogeneous parameters in agent-based models", the authors study heterogeneous
parameters in agent-based models during the interaction of the "Swarm-bot" system with a physical
unorganized environment [22]. In the scientific papers “Guest Editorial: Special issue on recent
developments in advanced mechatronics systems” and “Passive robust control for uncertain
Hamiltonian systems by using operator theory”, the authors present material on the latest developments
in the field of advanced mechatronics systems and explore models passive robust control for indefinite
Hamiltonian systems using operator theory [23,24]. In scientific papers "Design, Testing, and Evolution
of Mars Rover Testbeds: European Space Agency Planetary Exploration" and "UV-C Mobile Robots
with Optimized Path Planning: Algorithm Design and On-Field Measurements to Improve Surface
Disinfection Against SARS-CoV-2 » the authors investigated optimization models with path planning
for mobile robots and described the evolution of the rover test benches [25,26]. In the materials of the
conferences "IEEE Proceedings International Symposium on Multi-Robot and Multi-Agent Systems",
a team of authors presents research on the interaction between intelligent mobile "s-bots" of one
"Swarm-bot" system. Algorithms for joint multi-agent pathfinding and collision avoidance are
described [27-29].
3. Methods
When controlling the movement of intelligent mobile "s-bots" that are part of one «Swarm-bot»
system, unforeseen situations may arise in a physically disorganized environment. In such a case, it is
necessary that an algorithm built into each «s-bot» be launched, which enables all intelligent mobile «s-
bots» to rebuild their parameters and function stably in this physically disorganized environment. When
such situations arise, prompt solutions are required. Solving such situations can include the task of
overcoming any obstacle in a physically disorganized environment. Then, the built-in algorithm should
include mechanisms for forming various topologies of intelligent mobile «s-bots». The authors of this
paper propose to use the mathematical apparatus, the theory of Poya's enumeration, to solve such a
problem. When solving the task of overcoming any obstacle in a physically unorganized environment,
it is possible to program the «Swarm-bot» system in such a way that each variant of the formed topology
for intelligent mobile «s-bots» corresponds to the shape of the obstacle. Let's consider the mathematical
apparatus of Poya's enumeration theory. A group is a non-empty set G together with a binary operation
(*), which combines any two elements a and b and forms a second element, which can be denoted as (a
* b) or simply ab. To be identified as a group (G, *), four requirements, known as group axioms, must
be met:
- the first requirement is closure:
∀𝑎, ∀𝑏 ∈ 𝐺, 𝑎 ∗ 𝑏 ∈ 𝐺, (1)
- the second requirement is associativity:
∀𝑎, ∀𝑏, ∀с ∈ 𝐺, (𝑎 ∗ 𝑏) ∗ с = 𝑎 ∗ (𝑏 ∗ с), (2)
- the third requirement is the presence of a neutral element:
∀𝑎 ∈ 𝐺, ∃𝑒 ∈ 𝐺, 𝑒 ∗ 𝑎 = 𝑎 ∗ 𝑒 = 𝑎 , (3)
- the fourth requirement is the presence of an inverse element:
∀𝑎−1 ∈ 𝐺, 𝑎 ∗ 𝑎−1 = 𝑎−1 ∗ 𝑎 = 𝑒, (4)
Then suppose that a finite set T consists of n elements. The set of all one-to-one mappings of a set
T onto itself is called the symmetric group Tn of degree n. Therefore, each such mapping is called a
permutation, and the order of the symmetric group is equal to the number of permutations of elements,
that is, |Tn| = n!
Let A be a permutation group acting on a set T. Those permutations in A leave the given element t
in T fixed and form a subgroup of the group A. Then we obtain the number of orbits D determined by
the permutation group A [30]:
1 (5)
𝐷= ∑ 𝐽1 (𝑎)
|𝐴| 𝑎∈𝐴
where J1(a) - is the number of elements fixed by substitution a. Finally, the cyclic index K(A) of the
permutation group A is a polynomial in the variables t1, t2, t3,…,tk, as defined by:
1 1 𝑛
𝐽 (𝑎) 𝐽 (𝑎) 𝐽 (𝑎) 𝐽 (𝑎) 𝐽 (𝑎) (6)
𝐾(𝐴) = ∑ 𝑡11 𝑡22 𝑡33 … 𝑡𝑘𝑘 = ∑ ∏ 𝑡𝑘𝑘 ,
|𝐴| 𝑎∈𝐴 |𝐴| 𝑎∈𝐴 𝑘=1
Substituting the function c(x,y) into K(A) replaces each tk with c(xk, yk), then for configurations the
enumeration series is obtained by substituting the enumeration series for figures into the cycle index of
the group of configurations:
1 𝑛
(7)
𝐶(𝑥, 𝑦) = 𝐾(𝐴, 𝑐(𝑥, 𝑦)) = ∑ ∏ (𝑐(𝑥 𝑘 , 𝑦 𝑘 )) 𝐽𝑘 (𝑎) ,
|𝐴| 𝑎∈𝐴 𝑘=1
By virtue of expressions (6) and (7), the enumerating series of configurations is obtained from the
cyclic index of some permutation group. Let us now turn to the enumeration series for the figures.
Undoubtedly, the more common enumeration series for figures is:
𝑐(𝑥) = 1 + 𝑥, (8)
Our main task is to determine the value of the function K (A, 1+x) for an arbitrary permutation group
A and apply this function to individual groups. Next, we will perform the formation of a set of formation
structures. Let us introduce the following notation. Consider intelligent mobile «s-bots» - in the amount
of D objects that are part of one «Swarm-bot» system, designated by the vertex M = {1, 2,…,D}. Then,
using graph theory, we get:
𝐺 = (𝑀, 𝐵), (9)
where M - is the top of the graph G;
B - is an edge of G.
Then edge B of graph G:
(𝑖, 𝑗) ∈ 𝐵(𝑖, 𝑗 ∈ 𝑀 и 𝑖 ≠ 𝑗), (10)
describes the relationship between intelligent mobile «s-bots» that are part of one «Swarm-bot» system.
The permutation group at the vertices of a graph G of order n is precisely the symmetric group T n. This
group induces a permutation group acting on edges in a natural way, which the authors propose to
denote as Tn(2). Thus, different graphs are represented by different equivalence classes under the action
of Tn. Therefore, from (7) we obtain an enumerating polynomial for graphs with n vertices:
(2) (11)
𝑔𝑛 (𝑥) = 𝐾(𝑇𝑛 , 1 + 𝑥),
4. Experiment
For example,the «Swarm-bot» system includes three intelligent mobile «s-bots»,which have some
kind of status in relation to each other. According to (9):
𝐺 = (𝑀, 𝐵).
we get:
𝑀 = {1,2,3}.
𝐵 = {(1,2), (1,3), (2,3)}.
Let the permutation group T3 for vertices be M and the permutation group T3(2) be induced by the
permutations, then, for each:
𝛼 ∈ 𝑇3 , (12)
exists:
(2) (13)
𝛼 ′ ∈ 𝑇3 ,
such that:
𝛼 ′ {𝑖, 𝑗} = {𝛼𝑖 , 𝛼𝑗 }, (14)
then we find the structure of the cycle S3:
- firstly:
1 2 3
𝛼0 = ( ) = (1)(2)(3).
1 2 3
1 2 3
𝛼1 = ( ) = (123).
2 3 1
1 2 3
𝛼2 = ( ) = (132).
3 1 2
1 2 3
𝛼3 = ( ) = (1)(23).
1 3 2
1 2 3
𝛼4 = ( ) = (13)(2).
3 2 1
1 2 3
𝛼5 = ( ) = (12)(3).
2 1 3
Next, we find the structure of the Т3(2) cycle:
- secondly:
𝛼0′ {1,2} = {1,2}.
𝛼0′ {1,3} = {1,3}.
𝛼0′ {2,3} = {2,3}.
𝛼0′ {1,3} = {1,3}.
then:
12 13 23
𝛼0′ = ( ) = (12)(13)(23).
12 13 23
12 13 23
𝛼1′ = ( ) = (12 23 13).
23 12 13
12 13 23
𝛼2′ = ( ) = (12 13 23).
13 23 12
12 13 23
𝛼3′ = ( ) = (12 13)(23).
13 12 23
12 13 23
𝛼4′ = ( ) = (12 23)(13).
23 13 12
12 13 23
𝛼5′ = ( ) = (12)(13 23).
12 23 13
The cycle index K(T3) of the permutation group T is the average value:
𝑛 (𝛼)
(15)
𝐽
∏ 𝛼𝑘𝑘 ,
𝑘=1
throughout the permutation in the group. Therefore, the cycle indices of the symmetric group T3 and
the pair group T3(2) are obtained from Table 1:
(2) 1 (16)
K(𝑇3 ) = K (𝑇3 ) = (𝛼13 + 2𝛼3 + 3𝛼1 𝛼2 ),
6
applying formula (7) we get the graphs:
1 (17)
𝑔𝑛 (𝑥) = [(1 + 𝑥)3 + 2(1 + 𝑥)3 + 3(1 + 𝑥)(1 + 𝑥 2 )] = 1 + 𝑥 + 𝑥 2 + 𝑥 3 ,
6
Table 1
Comparison of cyclic structures Т3 and Т3(2)
Т3 cyclic structures Т3(2) cyclic structures
(1)(2)(3) 𝛼13 (12)(13)(23) 𝛼13
(123) 𝛼3 (12 23 13) 𝛼3
(132) 𝛼3 (12 13 23) 𝛼3
(1)(23) 𝛼1 𝛼2 (12 13) (23) 𝛼1 𝛼2
(13)(2) 𝛼1 𝛼2 (12 23) (13) 𝛼1 𝛼2
(12)(3) 𝛼1 𝛼2 (12) ( 13 23) 𝛼1 𝛼2
5. Results
As it was said above, if the «Swarm-bot» system includes three intelligent mobile «s-bots», then
four topologies of intelligent mobile «s-bots» are possible. Each intelligent mobile «s-bot» in the
«Swarm-bot» system has a certain status in relation to the other «s-bot». Then we can confirm Figure
1 in the graph G = (A,B) there is no parameter (B), since every intelligent mobile «s-bot» in the «Swarm-
bot» system has an equal status.
a)
b)
c)
Figure 1: The topology of intelligent mobile "s-bot" a), b) and c) is presented as part of one "Swarm-
bot" system. With such a topology, each "s-bot" has an equal status and is the leader, so the parameter
(B) from the formula for describing the graph G = (A, B) is absent
a) b)
c)
Figure 2: The "Swarm-bot" system includes two intelligent mobile "s-bot" a) and b) whose status is
higher (they are leading) in relation to the third "s-bot" c), but equal in relation to each other, so the
parameter (B) from the graph description formula G = (A, B) is present as a single edge of the graph
a) b) c)
Figure 3: The "Swarm-bot" system has one intelligent mobile "s-bot" c), which has a higher status (it
is the leader) in relation to the other two "s-bots" a) and b), but the statuses "s-bots" a) and b) are
equal to each other, therefore the parameter (B) from the graph description formula G = (A, B) is
present as two graph edges
a)
c)
b)
Figure 4: The "Swarm-bot" system has one intelligent mobile "s-bot" c), which has a higher status (it
is the leader) in relation to the other two "s-bots" a) and b), but the statuses "s-bots" a) and b) are not
equal to each other, therefore the parameter (B) from the graph description formula G = (A, B) is
present in the form of three graph edges
6. Discussions
After analyzing the data in Table 1, we can say that four topologies of intelligent mobile «s-bots»
are possible. Each intelligent mobile «s-bot» in the «Swarm-bot» system has some status in relation to
another «s-bot». Then we can assert:
- first, the presented topology in Figure 1, there are no edges in the graph, since each intelligent
mobile «s-bot» in the «Swarm-bot» system has an equal status;
- second, the topology shown in Figure 2, the Swarm-bot system has two intelligent mobile «s-bots»,
which have a higher status than the third «s-bot», i.e. there is one graph edge;
- third, the topology shown in Figure 3, the «Swarm-bot» system contains one intelligent mobile «s-
bot», which has a higher status in relation to the other two «s-bots», i.e. there are two edges of the graph;
- fourth, the topology shown in Figure 4 in the «Swarm-bot» system has one intelligent mobile «s-
bot», which has a higher status in relation to the other two «s-bots», i.e. there are three edges of the
graph.
The authors of this work, when solving the problem of overcoming some kind of obstacle in a
physical unorganized environment, used a mathematical apparatus, the Poya enumeration thorium. The
solution of such a problem showed that when overcoming some kind of obstacle in a physical
unorganized environment, it is possible to program the «Swarm-bot» system in such a way that each
variant of the formed topology for intelligent mobile «s-bots» correlates with the shape of the obstacle.
The results obtained convinced the authors that the application of the Poya enumeration theory was
fully justified. It is supposed to continue research in this direction and conduct a comparative analysis
of algorithms to eliminate the disadvantage associated with the low speed of detection and location of
ground target points. Based on the results of the analysis, develop recommendations on the
appropriateness of using one or another algorithm in Swarm-bot systems.
7. Conclusions
In this work, a scientific problem was posed and successfully solved, which may arise when
controlling movement in a physical unorganized environment by intelligent mobile «s-bots» that are
part of one «Swarm-bot» system. In this case, it is necessary that the algorithm built into each «s-bot»
be launched, which enables all intelligent mobile «s-bots» to rebuild their parameters and function
stably in this physical unorganized environment. When such situations arise, they require a prompt
solution. The solution of such situations can be attributed to the problem of overcoming any obstacles
in a physical unorganized environment. Then, the built-in algorithm should include mechanisms for
generating various options for the topology of intelligent mobile «s-bots».
The authors of this paper used a mathematical apparatus, the Pois enumeration, to solve this problem.
The solution of such a task showed that when overcoming any obstacle in a physically unorganized
environment, it is possible to program the «Swarm-bot» system in such a way that each variant of the
formed topology for intelligent mobile «s-bots» corresponds to the shape of the obstacle. The obtained
results convinced the authors that the application of Poya's enumeration theory was fully justified.
It is proposed to continue research in this direction and conduct a comparative analysis of algorithms
to eliminate the shortcoming associated with the low speed of detecting and determining the location
of ground target points. Based on the results of the analysis, develop recommendations on the
expediency of using one or another algorithm in «Swarm-bot» systems.
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