This paper presents a comparative study of A* and Rapidly-exploring Random Tree (RRT) algorithms for autonomous path planning. A*, a heuristic-based method, ensures optimal paths but is computationally intensive, whereas RRT offers faster, scalable solutions in dynamic environments at the cost of path smoothness. Both algorithms are evaluated under identical simulated conditions using metrics like trajectory quality, computation time, and search space coverage. The study also introduces enhancements—ripple reduction for A* and a refined RRT variant—to improve trajectory quality. Results and visualizations highlight trade-offs, aiding in the informed selection of path planning strategies.