Path planning is crucial in robotics and autonomous systems, but traditional algorithms like Rapidly Exploring Random Tree Star (RRT*) face limitations in exploration efficiency and adaptability. We propose Quadrant Partitioned-RRT* (QP-RRT*), which addresses these challenges by dividing the map into quadrants and focusing exploration efforts dynamically based on a density threshold. If the threshold is exceeded, the start point shifts to a new quadrant, enabling efficient and adaptive exploration. Experimental results demonstrate that QP-RRT* outperforms RRT* in path quality, exploration efficiency, and adaptability. Future research will focus on integrating obstacle avoidance and dynamic environments. QP-RRT* provides a promising solution for complex path-planning challenges.