Improved Informed RRT*: Based on Dynamic Shrinkage Threshold Node Selection Mechanism and Adaptive Goal-Biased Strategy

This study presents an improved Informed RRT* algorithm integrating a dynamic shrinkage threshold node selection mechanism with an adaptive goal-biased strategy, aimed at reducing computational iterations and accelerating convergence performance. To resolve node redundancy during Informed RRT* sampling, a dynamic shrinkage threshold-based node selection mechanism is developed. Through the dynamic evaluation of nodal distances (between newly generated nodes and the existing tree structure) against node selection thresholds, redundant nodes are eliminated to enhance spatial exploration efficiency. To address blind exploration and convergence delays, an adaptive goal-biased strategy guides the directional expansion of the search tree toward target regions, thereby optimizing convergence behavior. Systematic simulations demonstrate the effectiveness of the proposed algorithm across multiple scenarios. Comparative experiments demonstrate that the two key technologies significantly improved the speed of the initial-path generation of Informed RRT*. Moreover, the proposed method shows good adaptability and stability in different environments, which proves its potential and advantages in the path-planning field.