An Improved Informed-RRT* Algorithm Based on Risk-Density-Aware Corridor Sampling and Improvement-Bound Rejection for Path Planning

Abstract

Sampling-based path planning is widely used in autonomous navigation, but Informed-RRT* still relies mainly on geometric ellipsoidal sampling and does not explicitly evaluate local obstacle risk, which can lead to invalid expansion, redundant nodes, and slow convergence. This paper proposes RC-Informed-RRT*, integrating risk-density-aware adaptive corridor sampling, improvement-bound rejection, and search-state-regulated goal bias. The corridor mechanism combines reference-path deviation, obstacle clearance, and local obstacle density; the rejection mechanism filters low-contribution nodes using an optimistic improvement bound; and the goal-bias strategy adapts target-oriented sampling to the search state. Comparative simulations were conducted in sparse, dense, narrow-passage, and W-shaped environments using 50 randomized trials per algorithm with small perturbations of the start/goal positions and obstacle locations. Relative to Informed-RRT*, RC-Informed-RRT* reduced mean planning time by 45.88–74.76% and final node count by 35.92–60.13%, while reducing final path length by 0.75–4.88% and maintaining 98–100% success rates. Sequential ablation and goal-bias sensitivity experiments further support the complementary roles of the three mechanisms and the fairness of the baseline parameter setting.