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

Mustafa Emre Erbil; Merdan Özkahraman; Hilmi Cenk Bayrakçı

Article Details

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

Authors:

Mustafa Emre Erbil | Merdan Özkahraman | Hilmi Cenk Bayrakçı

Publisher:

Fırat University

Main Topics:

Planlama ve Karar Verme | Yapay Yaşam ve Karmaşık Uyarlanabilir Sistemler | Yapay Zeka (Diğer)

Journal:

Fırat Üniversitesi Mühendislik Bilimleri Dergisi Vol.37 Issue 1, pp.151-166

Document Type:

Article

Publication Date:

3/2025

Document Language:

English

Document ID (Org No.):

2001334863

pISSN:

1308-9072

Keywords:

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 maze-solving algorithms in various technological domains.

 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 maze-solving algorithms in various technological domains.

 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 maze-solving algorithms in various technological domains.

 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.

Article Details

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

Authors:

Mustafa Emre Erbil | Merdan Özkahraman | Hilmi Cenk Bayrakçı

Publisher:

Fırat University

Main Topics:

Planlama ve Karar Verme | Yapay Yaşam ve Karmaşık Uyarlanabilir Sistemler | Yapay Zeka (Diğer)

Journal:

Fırat Üniversitesi Mühendislik Bilimleri Dergisi Vol.37 Issue 1, pp.151-166

Document Type:

Article

Publication Date:

3/2025

Document Language:

English

Document ID (Org No.):

2001334863

pISSN:

1308-9072

Autonomous systems

maze solving algorithms

navigation technologies

pathfinding optimization

performance analysisOtonom sistemler

labirent çözme algoritmaları

navigasyon teknolojileri

yol bulma optimizasyonu

performans analizi

Issues

Article Details

Authors: Mustafa Emre Erbil | Merdan Özkahraman | Hilmi Cenk Bayrakçı

Publisher: Fırat University

Main Topics: Planlama ve Karar Verme | Yapay Yaşam ve Karmaşık Uyarlanabilir Sistemler | Yapay Zeka (Diğer)

Journal: Fırat Üniversitesi Mühendislik Bilimleri Dergisi Vol.37 Issue 1, pp.151-166

Document Type: Article

Publication Date: 3/2025

Document Language: English

Document ID (Org No.): 2001334863

pISSN: 1308-9072

Issues

Authors:

Mustafa Emre Erbil | Merdan Özkahraman | Hilmi Cenk Bayrakçı

Publisher:

Fırat University

Main Topics:

Planlama ve Karar Verme | Yapay Yaşam ve Karmaşık Uyarlanabilir Sistemler | Yapay Zeka (Diğer)

Journal:

Fırat Üniversitesi Mühendislik Bilimleri Dergisi Vol.37 Issue 1, pp.151-166

Document Type:

Article

Publication Date:

3/2025

Document Language:

English

Document ID (Org No.):

2001334863

pISSN:

1308-9072