Performance Analysis of Deterministic and Probabilistic Path Planning Algorithms in Complex Environments

Path planning, in an other saying navigation, algorithms are vital in assorted applications, including robotics, autonomous vehicles, and drones. These algorithms can be broadly categorized into deterministic and probabilistic methods along with other branches. This study focuses and examines two classical deterministic path planning algorithms, A-star (A*) and Dijkstra's algorithm, alongside two prominent probabilistic path planning algorithms, Rapidly-exploring Random Trees Star (RRT*) and Probabilistic Roadmap (PRM). In the paper, with creating multi-level building interior floor maps and testing the performance of these four algorithms are performed on each level. Performance metrics included execution time, CPU usage, memory usage, and path distance. The results, presented in comparative tables, provide a comprehensive analysis of the efficiency and resource demands of each algorithm. Furthermore, this research offers valuable insights for selecting appropriate path planning algorithms in various autonomous navigation applications, guiding future implementations in robotics, autonomous electric vehicles, and drone technology.