An improved Informed-RRT* algorithm is introduced to tackle issues related to high randomness, a large number of infeasible nodes, and low convergence efficiency in path planning. This algorithm optimizes node usage through global sampling and an adaptive step size. The initial path is generated using a biased bidirectional search and a parent node reselection technique, which offers a more effective starting point for further iterative optimization. During the elliptic iteration, a greedy approach is applied to eliminate unnecessary nodes. Additionally, path backtracking is refined to decrease redundant nodes and improve trajectory smoothness. This study presents two factors: obstacle complexity and map size, to assess the performance of the enhanced algorithm against the original Informed-RRT* algorithm in four different scenarios. Results from 20 experiments show that the improved algorithm decreases the number of trajectory waypoints by 28.6% to 64.3% and reduces trajectory length by 0.3% to 2.7%. These results suggest that our enhanced method enhances node utilization, produces shorter trajectories, and significantly cuts down on computational iterations compared to the Informed-RRT* algorithm.