Akademik Veri Yönetim Sistemi
2025 Innovations in Intelligent Systems and Applications Conference, ASYU 2025, Bursa, Türkiye, 10 - 12 Eylül 2025, (Tam Metin Bildiri)
Path planning algorithms are fundamental software components that enable autonomous mobile robots to reach their targets safely and efficiently within complex environments. In simulation environments that closely mimic real-world conditions, the presence of static and dynamic obstacles directly affects the performance of these algorithms and becomes a critical factor in determining the success of decision-making systems. Therefore, systematically analyzing the effectiveness of various planning algorithms under different environmental conditions is of great importance for both academic research and practical applications. This study presents a comparative analysis of the performance of path planning algorithms included in the OMPL (Open Motion Planning Library) by testing them on a mobile robot in the CoppeliaSim simulation environment. As part of the experimental setup, a total of five scenarios with varying dimensions and obstacle configurations were created. The environments consist of both small and large areas; some include only static obstacles, while others feature a combination of static and dynamic obstacles. This diversity allows for a comprehensive evaluation of the algorithms under various environmental conditions. The performance of each algorithm was assessed using two main metrics: total path length (in meters) and time to reach the target (in seconds). The robot was ensured to reach the target without collisions, and each algorithm was executed under these consistent conditions. The obtained data were presented through tables and graphical representations, enabling a detailed comparison of the strengths and weaknesses of each algorithm. This study aims to shed light on the performance of different path planning algorithms across various scenarios and contribute to the selection process of optimal planning strategies in autonomous systems.