Journal of Engineering Research and Reports

Path planning is a critical component of autonomous vehicles, intelligent transportation, and robotic navigation. However, conventional algorithms such as A*, Dijkstra, bidirectional A*, and RRT often suffer from local optima, redundant turning points, excessive cumulative turning angles, and computational overhead in maintaining safe distances, limiting their real-time performance and stability in complex, high-resolution maps. To address these issues, we propose a path planning optimization framework that integrates morphological preprocessing with polar-coordinate-based key-point extraction. Morphological dilation is applied to map representations to adaptively enforce obstacle clearance during planning, improving robustness and efficiency. Subsequently, a polar-coordinate key-point extraction strategy reduces redundant nodes, minimizes cumulative turning angles, and alleviates local optima. The approach was implemented on a ROS–Gazebo simulation platform and systematically compared with multiple baseline algorithms across maps of varying sizes. Experimental results demonstrate that the proposed method reduces path nodes by over 50%, decreases cumulative turning angles by 85%, shortens path length by 23.8%, and improves planning response time by 69%. These results highlight the effectiveness of the method in enhancing path smoothness, computational efficiency, and global optimality, providing a reliable basis for safe autonomous navigation in complex environments.