To address the low search efficiency, slow convergence speed, and limited path expansion diversity of the RRT family of algorithms, an adaptive multi-strategy dynamic step-size algorithm, AMDS-Bi-RRT*, is proposed. Based on the Bi-RRT* framework, the algorithm enhances convergence efficiency through a dynamic goal-directed extension strategy and an adaptive step-size evaluation function. A multi-directional emergency maneuver strategy is designed to improve adaptability in complex environments. Meanwhile, node sampling is optimized using an improved artificial potential field method, and a three-stage path smoothing approach is introduced to ensure path feasibility. Comparative experiments conducted in four simulation environments of varying complexity against five benchmark algorithms—Bi-APF-RRT*, Bi-RRT*, APF-RRT*, RRT*, and goal-biased RRT*—demonstrate that AMDS-Bi-RRT* reduces average planning time by 12.22%~23.45%, shortens average path length by 0.88%~1.89%, and decreases the average number of nodes by 6.69%~22.85%. The results verify that AMDS-Bi-RRT* outperforms the comparison algorithms in planning efficiency, path quality, and convergence speed, confirming its superior performance across diverse environments.