This paper presents the development of an efficient and smooth path planning algorithm tailored for autonomous systems operating in static environments. A modified bi-directional and goal-oriented rapidly-exploring random tree (RRT) algorithm is proposed, generating an initial rough global path quickly. To further enhance the quality of this path, we introduce a two-step optimization process, involving down-sampling to reduce redundant waypoints and up-sampling to improve path resolution. A cubic curve smoothing technique is then applied to ensure the path maintains continuity and remains collision-free, even in dense, clustered environments. The algorithm is validated by simulations in environments that approximate real-world conditions using MATLAB. This work primarily focuses on improving computational efficiency and path smoothness, addressing key challenges in robotic path planning.