Path planning is a critical component of autonomous mobile robots, enabling efficient and safe navigation across diverse environments. Achieving optimal path planning is essential for ensuring both smooth trajectories and computational efficiency. The A-star (A*) algorithm is extensively utilized for path optimization due to its effectiveness in identifying the shortest path. However, it frequently generates non-smooth paths, particularly in low-resolution environments, which can adversely affect robot performance and safety. To address this limitation, this paper integrates the A* algorithm with cubic B-spline interpolation. Cubic B-splines enhance path smoothness by increasing the number of reference points and generating more continuous trajectories. While the improved A* method excels in static environments, dynamic obstacle avoidance is managed through the Artificial Potential Field method (APF). Our hybrid approach preserves the strengths of A* while significantly improving path smoothness and dynamic obstacle handling. Simulation results indicate that this combined method produces smoother trajectories without extending path length or increasing computational complexity, thereby augmenting the navigation capabilities of autonomous robots.
