<p>Motion planning is a critical component of autonomous decision-making in intelligent robots, unmanned aerial vehicles, and self-driving systems, where both path quality and planning efficiency are essential. However, conventional path-planning algorithms often suffer from slow convergence, redundant node generation, and limited path quality. To address these limitations, this paper proposes DVFA-RRT*, a progress-driven hybrid sampling and staged extension algorithm for three-dimensional (3D) trajectory planning and obstacle avoidance. The proposed method introduces a goal-distance-based progress metric that adaptively regulates both sampling-strategy selection and the associated probability distribution. A two-stage extension strategy is then developed to balance goal-directed exploitation with global exploration. Specifically, a goal-biased extension strategy integrated with visibility-fan-based obstacle avoidance is used to accelerate convergence, while improved APF-guided exploration with an RRT*-based fallback enhances robustness in cluttered environments. Finally, greedy shortcutting, interpolation-based densification, and B-spline smoothing are applied to refine the generated path and enhance trajectory smoothness. Experiments conducted in five 3D simulation environments demonstrate that DVFA-RRT* generates higher-quality initial paths, requires fewest nodes, and exhibits stronger adaptability across the tested scenarios, thereby improving the overall path-planning performance.</p>