View in EDS PDF Full Text

Comprehensive Performance Analysis and Evaluation of Various Maze Solving Algorithms for Optimized Autonomous Navigation and Pathfinding.

Saved in:
Bibliographic Details
Title: Comprehensive Performance Analysis and Evaluation of Various Maze Solving Algorithms for Optimized Autonomous Navigation and Pathfinding.
Alternate Title: Çeşitli Labirent Çözme Algoritmalarının Optimum Otonom Navigasyon ve Yol Bulma için Kapsamlı Performans Analizi ve Değerlendirilmesi.
Authors: ERBİL, Mustafa Emre1 mail@mustafaemreerbil.com, ÖZKAHRAMAN, Merdan1 merdanozkahraman@isparta.edu.tr, BAYRAKÇI, Hilmi Cenk1 cenkbayrakci@isparta.edu.tr
Source: Firat University Journal of Engineering Science / Fırat Üniversitesi Mühendislik Bilimleri Dergisi. 2025, Vol. 37 Issue 1, p151-166. 16p.
Subjects: Technological innovations, Performance evaluation, Algorithms, Autonomous robots, Global Positioning System
Abstract (English): Recent advancements in technology have led to the widespread use of maze-solving algorithms in various applications, such as autonomous robots, GPS-based navigation systems, smart traffic management systems, and healthcare services. This study provides a comprehensive comparative analysis of the performance of several maze-solving algorithms, including A*, Breadth-First Search (BFS), Depth-First Search (DFS), Dijkstra, Flood Fill, Random Mouse, and Recursive Backtracker. The algorithms were evaluated based on key performance metrics such as solution speed, memory usage, and CPU consumption. The results indicate that while the DFS algorithm demonstrates the fastest solution time with minimal memory usage, it has higher CPU consumption. In contrast, the Random Mouse algorithm is the least efficient, showing the highest memory and CPU usage along with the longest solution time. The A* algorithm, although efficient in finding the shortest path, showed moderate performance in both memory and CPU usage. These findings offer valuable insights into the strengths and weaknesses of each algorithm, providing guidance for future improvements and applications in real-world scenarios. This study aims to be a valuable resource for researchers and engineers focused on enhancing the efficiency of mazesolving algorithms in various technological domains. [ABSTRACT FROM AUTHOR]
Abstract (Turkish): Teknolojideki son gelişmeler, otonom robotlar, GPS tabanlı navigasyon sistemleri, akıllı trafik yönetim sistemleri ve sağlık hizmetleri gibi çeşitli uygulamalarda labirent çözme algoritmalarının yaygın olarak kullanılmasına yol açmıştır. Bu çalışma, A*, Genişlik Öncelikli Arama (BFS), Derinlik Öncelikli Arama (DFS), Dijkstra, Flood Fill, Random Mouse ve Recursive Backtracker dahil olmak üzere çeşitli labirent çözme algoritmalarının performansının kapsamlı karşılaştırmalı bir analizini sunmaktadır. Algoritmalar, çözüm hızı, bellek kullanımı ve CPU tüketimi gibi ana performans metrikleri temelinde değerlendirilmiştir. Sonuçlar, DFS algoritmasının minimal bellek kullanımı ile en hızlı çözüm süresini gösterirken, daha yüksek CPU tüketimine sahip olduğunu göstermektedir. Buna karşılık, Random Mouse algoritması en verimsiz olup, en yüksek bellek ve CPU kullanımının yanı sıra en uzun çözüm süresini göstermektedir. A* algoritması, en kısa yolu bulmada verimli olmasına rağmen, bellek ve CPU kullanımında orta düzeyde performans göstermiştir. Bu bulgular, her bir algoritmanın güçlü ve zayıf yönlerine ilişkin değerli bilgiler sunmakta ve gerçek dünya uygulamalarında gelecekteki iyileştirmeler ve uygulamalar için rehberlik sağlamaktadır. Bu çalışma, labirent çözme algoritmalarının verimliliğini artırmayı hedefleyen araştırmacılar ve mühendisler için değerli bir kaynak olmayı amaçlamaktadır. [ABSTRACT FROM AUTHOR]
Copyright of Firat University Journal of Engineering Science / Fırat Üniversitesi Mühendislik Bilimleri Dergisi is the property of Firat University and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Description
Abstract:Recent advancements in technology have led to the widespread use of maze-solving algorithms in various applications, such as autonomous robots, GPS-based navigation systems, smart traffic management systems, and healthcare services. This study provides a comprehensive comparative analysis of the performance of several maze-solving algorithms, including A*, Breadth-First Search (BFS), Depth-First Search (DFS), Dijkstra, Flood Fill, Random Mouse, and Recursive Backtracker. The algorithms were evaluated based on key performance metrics such as solution speed, memory usage, and CPU consumption. The results indicate that while the DFS algorithm demonstrates the fastest solution time with minimal memory usage, it has higher CPU consumption. In contrast, the Random Mouse algorithm is the least efficient, showing the highest memory and CPU usage along with the longest solution time. The A* algorithm, although efficient in finding the shortest path, showed moderate performance in both memory and CPU usage. These findings offer valuable insights into the strengths and weaknesses of each algorithm, providing guidance for future improvements and applications in real-world scenarios. This study aims to be a valuable resource for researchers and engineers focused on enhancing the efficiency of mazesolving algorithms in various technological domains. [ABSTRACT FROM AUTHOR]
ISSN:13089072
DOI:10.35234/fumbd.1518386