CC BY
0
yflK 62
Gurbanmammedov D.
4th year student Oguz han Engineering and Technology University of Turkmenistan
Bazarov D.
4th year student Oguz han Engineering and Technology University of Turkmenistan
Davudov A.
4th year student Oguz han Engineering and Technology University of Turkmenistan
Bashimov A.
4th year student Oguz han Engineering and Technology University of Turkmenistan
Jummanov U.
4th year student Oguz han Engineering and Technology University of Turkmenistan
Turkmenistan c. Ashgabat
INTEGRATION OF AN INTELLIGENT MAZE SOLVING ROBOT
Abstract
This research paper presents the design and implementation of an intelligent maze-solving robot that autonomously navigates through complex mazes using advanced algorithms and sensor technologies. The primary objective was to develop a robot capable of identifying the shortest path from a designated start point to an endpoint while effectively avoiding obstacles. The robot utilized the A* algorithm for pathfinding, complemented by various sensors for environmental awareness. Extensive testing was conducted in different maze configurations to evaluate the robot's efficiency and accuracy. The results demonstrated that the robot not only solved mazes effectively but also adapted to various challenges, showcasing its potential applications in fields such as robotics, automation, and artificial intelligence.
Introduction
The integration of robotics and artificial intelligence has led to significant advancements in autonomous systems capable of solving complex problems. One such problem is maze navigation, which has been a popular research topic due to its practical applications in various fields, including robotics, automation, and artificial intelligence.
In this research, we aimed to design a robot that could autonomously navigate a maze using the algorithm for optimal pathfinding. The project involved hardware selection, software development, and extensive testing to evaluate the robot's performance in various maze configurations.
The concept of maze-solving robots has been explored extensively in previous studies. Various methodologies have been proposed, including:
A Algorithm: This algorithm is widely recognized for its efficiency in finding the shortest path in a weighted graph. It combines features of Dijkstra's algorithm and greedy best-first search, making it suitable for real-time applications.
Wall-Following Techniques: Many early maze-solving robots relied on wall-following strategies. While effective in simple mazes, these methods often struggled with more complex environments.
Image Processing: Recent advancements have incorporated image processing techniques to enhance navigation capabilities. Robots equipped with cameras can analyze their surroundings visually and make informed decisions based on visual data.
Hardware Design
The robot was constructed using the following components:
Microcontroller: An MSP432P401R microcontroller was selected for its processing capabilities and ease of integration with various sensors.
Sensors: The robot was equipped with ultrasonic sensors for distance measurement and infrared sensors for obstacle detection. These sensors provided real-time feedback about the robot's environment.
Chassis: A robust chassis was designed to house all components securely while allowing for smooth movement through the maze. Software Development
The software development process is a structured approach to creating software applications that meet user needs and business requirements. It encompasses various stages, methodologies, and best practices aimed at delivering high-quality, maintainable, and scalable software solutions. This section outlines the key aspects of software development, including methodologies, tools, and practices that contribute to successful project outcomes.
Development Methodologies
The choice of development methodology significantly influences the project's direction and success. Several methodologies have emerged, each with unique principles and practices:
Agile Methodology: Agile emphasizes iterative development, where requirements and solutions evolve through collaboration between self-organizing cross-functional teams. This methodology promotes flexibility, allowing teams to respond rapidly to changes in user needs and project scope. Common frameworks within Agile include Scrum and Kanban, which facilitate incremental progress through short cycles called sprints.
DevOps: This approach integrates development (Dev) and operations (Ops) to enhance collaboration and efficiency throughout the software lifecycle. DevOps practices emphasize automation in testing, deployment, and monitoring, enabling continuous integration and continuous deployment (CI/CD). This results in faster delivery of high-quality software while maintaining system stability.
Waterfall Model: A traditional approach that follows a linear sequence of phases—requirements analysis, design, implementation, testing, deployment, and maintenance. While straightforward, this model can be inflexible in accommodating changes once the process has begun.
Lean Development: Lean principles focus on maximizing value by minimizing waste in the development process. This methodology encourages teams to streamline workflows and prioritize essential features that deliver the most significant impact.
Utilizing version control systems like Git allows teams to track changes in code, collaborate effectively, and manage different versions of the software. Version control facilitates branching for feature development and enables easy rollback if issues arise. TDD involves writing tests before coding the actual functionality. This practice ensures that all code is tested thoroughly and meets specified requirements from the outset. It significantly improves code quality by identifying defects early in the development cycle. Regular code reviews promote knowledge sharing among team members while enhancing code quality. Peer reviews help identify bugs, ensure adherence to coding standards, and foster collaborative problem-solving. CI/CD practices automate the integration of code changes into a shared repository and deploy them to production environments seamlessly. This reduces manual errors and accelerates the delivery process.
Conclusion
The research successfully demonstrated the feasibility of developing an intelligent maze-solving robot capable of navigating complex environments autonomously. By integrating advanced algorithms with robust sensor technologies, the robot achieved impressive results in terms of efficiency and adaptability. Future work could explore further enhancements through machine learning techniques and additional sensor integrations, paving the way for more sophisticated autonomous systems capable of tackling even more complex challenges beyond maze navigation. By embracing Agile principles, incorporating DevOps practices, prioritizing quality through TDD and code reviews, and leveraging modern tools for collaboration and management, development teams can deliver high-quality software solutions efficiently. References
1. Beck, K., & Andres, C. (2005). Extreme programming explained: Embrace change (2nd ed.).
2. Fowler, M. (2018). Refactoring: Improving the design of existing code (2nd ed.). Addison-Wesley.
3. Hunt, A., & Thomas, D. (1999). The Pragmatic Programmer: From Journeyman to Master.
4. Sutherland, J., & Schwaber, K. (2017). The Scrum Guide: The Definitive Guide to Scrum: The Rules of the Game. Scrum.org.
5. Poppendieck, M., & Poppendieck, T. (2003). Lean software development: An agile toolkit.
© Gurbanmammedov D., Bazarov D., Davudov A., Bashimov A., 2024
UDC 004
Hojanyyazova M.
Student
Oguzhan Engineering and Technology University of Turkmenistan
c. Ashgabat, Turkmenistan
ISOLATION TECHNOLOGY OF MENTHOL FROM MINT (MENTHA ARVENSIS) AND USING ON FOOD INDUSTRY
Abstract
Menthol, a key compound derived from mint (Mentha arvensis), is widely recognized for its refreshing aroma and cooling properties. It is extensively utilized in the food industry to enhance flavor, as well as in pharmaceutical and cosmetic applications. This article discusses the advanced technologies employed in isolating menthol from mint, particularly focusing on efficient extraction methods. Furthermore, it highlights the diverse applications of menthol in the food industry, its safety profile, and emerging trends in its utilization.
Introduction
Menthol is a naturally occurring monoterpene alcohol found in the essential oils of several mint species, with Mentha arvensis being one of the most common sources. It is valued for its distinctive cooling effect and aromatic properties, making it a versatile ingredient in various industries, especially the food and beverage sector. The isolation of menthol from mint plants involves several extraction techniques, each aiming to maximize yield while maintaining the purity and quality of the compound.
1. Isolation Techniques for Menthol Extraction
The extraction of menthol from Mentha arvensis can be achieved through several methods, with each having distinct advantages and limitations.
1.1 Steam Distillation
Steam distillation is the most traditional and widely used method for isolating menthol. It involves passing steam through crushed mint leaves, causing the essential oils to evaporate. The vapors are then condensed and separated, yielding menthol along with other essential oils. While effective, this method requires careful temperature control to avoid the degradation of menthol.
1.2 Solvent Extraction
Solvent extraction involves the use of organic solvents, such as ethanol or hexane, to dissolve the essential oils from mint leaves. After solvent removal, menthol can be further purified. This method is more efficient than steam distillation but requires the careful handling of solvents to ensure food-grade purity.
1.3 Supercritical Fluid Extraction (SFE)
Supercritical fluid extraction, particularly using carbon dioxide (CO2), has gained popularity due to its efficiency and environmentally friendly nature. SFE allows for the extraction of menthol at lower temperatures,
