To address the challenges of path planning for intelligent agents in complex environments, particularly issues such as slow algorithm convergence, high path redundancy, and insufficient smoothness, this paper proposes a target-biased bidirectional RRT* algorithm based on KD-tree (KDB-RRT*). The algorithm introduces a bidirectional search strategy based on RRT*, incorporates a KD-tree structure to accelerate node lookup, constructs a target-biased dynamic circular sampling strategy to balance search efficiency, designs a bidirectional growth guidance model based on gravitational fields, implements adaptive step-size adjustment using the Sigmoid function combined with obstacle density, and employs the DP algorithm for original path pruning and cubic B-spline curves for path smoothing. The feasibility of KDB-RRT* is verified in “Z-shaped” and “loop-shaped” simulation environments, and comparative experiments are conducted with RRT*, Bi-RRT, and Improved RRT* algorithms in various complex map environments. Finally, path planning experiments are performed on a ROS robot. In the “Z-shaped” and “loop-shaped” simulation environments, compared with the RRT* algorithm, KDB-RRT* reduces the average planning time by 70.2% and 28.0%, decreases the average path length by 4.8% and 10.4%, and increases the node utilization rate by 16.27% and 13.58%, respectively. The results show that the KDB-RRT* algorithm provides a new method for efficient path planning in unstructured environments, and its dynamic sampling model and path optimization framework have important reference value for mobile robot navigation systems.