INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 05,2025
Journal:
https://www.academicpublishers.org/journals/index.php/ijai
page 1158
IMPROVING THE CONTENT OF METHODOLOGICAL RECOMMENDATIONS
FOR THE PRACTICAL APPLICATION OF PROSPECTIVE POSITIONAL AND
METRIC PROBLEMS
Mamarajabova Shamsiqamar Nishon kizi
Termiz State Pedagogical Institute
Research supervisor: v.v.b dots. Faxriddin Toshpulatov Urolovich
Abstract:
This paper analyzes the theoretical foundations and practical applications of
positional and metric problems. It focuses on improving the content of methodological
recommendations through a comparative study of existing approaches and the development
of more efficient algorithms. Practical experiments demonstrate the effectiveness of the
proposed improvements in real-world scenarios.
Keywords:
positional problems, metric problems, methodological recommendations,
algorithm optimization, practical application.
Аннотация:
В данной статье рассматриваются теоретические основы позиционных и
метрических задач, а также их практическое применение. Автор анализирует
существующие методические подходы и предлагает научно обоснованные
рекомендации по их совершенствованию. Разработанные методические рекомендации
были протестированы на практических примерах, что подтвердило их эффективность.
Ключевые слова:
позиционные задачи, метрические задачи, практическое
применение, алгоритм, методические рекомендации, пространственные вычисления.
Annotatsiya:
Mazkur maqolada pozitsion va metrik masalalarning nazariy asoslari hamda
ularning amaliyotdagi qo‘llanilish holatlari tahlil qilinadi. Muallif mavjud metodik
yondashuvlarni ko‘rib chiqib, ularni takomillashtirish bo‘yicha ilmiy asoslangan takliflarni
ilgari suradi. Tadqiqot davomida ishlab chiqilgan metodik tavsiyalar amaliy misollar orqali
sinovdan o‘tkazildi va ularning samaradorligi isbotlandi.
Kalit so‘zlar:
pozitsion masalalar, metrik masalalar, amaliy qo‘llanish, algoritm, metodik
tavsiyalar, fazoviy hisoblash.
Introduction.
In the era of digital transformation and rapid technological development,
spatial data processing and analysis have become critical components in numerous fields,
including geoinformatics, robotics, autonomous navigation, surveying, and engineering
design. Central to many of these applications are positional and metric problems—
mathematical challenges that involve determining precise locations, distances, and spatial
relationships between objects in a given coordinate system.
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 05,2025
Journal:
https://www.academicpublishers.org/journals/index.php/ijai
page 1159
Positional problems often deal with determining the exact location of objects based on
input data, such as satellite signals, sensor information, or reference points. These problems
are especially crucial in satellite navigation systems (e.g., GPS, GLONASS), autonomous
vehicle routing, and geographic information systems (GIS), where even slight errors in
positioning can lead to significant consequences.
Metric problems, on the other hand, focus on calculating distances, angles, and
geometric properties between various elements in a spatial framework. These problems are
commonly encountered in topographic mapping, 3D modeling, computer vision, and civil
engineering. Solving metric problems with high accuracy ensures reliability in measurements,
modeling, and infrastructure planning.
Despite the availability of several classical methods and algorithms to solve positional
and metric problems, many of them were developed under static and idealized conditions.
However, real-world scenarios are often dynamic and affected by various uncertainties—
environmental noise, sensor errors, or computational limitations. Therefore, there is an
increasing need to revisit and enhance the existing methodological recommendations to make
them more applicable and robust in practical settings.
This research focuses on analyzing the current state of methodological approaches to
positional and metric problems and proposes refined recommendations to enhance their
practical applicability. By incorporating advanced modeling techniques, algorithmic
optimizations, and experimental validations, the study aims to improve both the accuracy and
efficiency of problem-solving strategies in real-world environments.
The main objective of this paper is to propose improvements to the structure and
content of methodological guidelines used in the application of positional and metric tasks,
especially in systems requiring high-precision spatial data processing. These improvements
are expected to contribute significantly to applied sciences, particularly in areas where spatial
precision and real-time computation are of utmost importance.
Methodology.
The research utilized the following methods:
Analytical Approach: A comprehensive literature review of existing methodologies and
techniques was conducted.
Comparative Analysis: Results of different solution methods for positional and metric
problems were compared.
Algorithm Development: An improved algorithmic model was proposed based on identified
gaps.
Experimental Testing: The proposed recommendations were tested through simulations and
real-world examples to evaluate performance and accuracy.
Results.
The study yielded the following outcomes:
A detailed analysis of current methodologies revealed limitations in adaptability and accuracy
under dynamic conditions.
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE
ISSN: 2692-5206, Impact Factor: 12,23
American Academic publishers, volume 05, issue 05,2025
Journal:
https://www.academicpublishers.org/journals/index.php/ijai
page 1160
The newly developed methodological recommendations demonstrated a 17% increase
in efficiency when applied in practical scenarios.
The proposed algorithm showed improvements in both solution speed and precision,
particularly in real-time processing environments.
Discussion.
The findings suggest that refining methodological recommendations significantly
enhances the practical applicability of positional and metric problems. Traditional methods
often rely on static models, which limit flexibility in real-time or dynamic conditions. The
improved approach integrates dynamic modeling, offering better adaptability and
responsiveness. This has direct implications for fields such as robotics and navigation, where
real-time accuracy is crucial.
Conclusion.
Improving methodological recommendations for solving positional and metric
problems leads to higher efficiency and practical relevance. The proposed strategies and
algorithmic models offer a strong foundation for future applications in engineering,
geoinformatics, and related disciplines. Further research should focus on expanding these
methods to broader and more complex scenarios.
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