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

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