Sampling-based planners such as RRT* are attractive for robot navigation in complex spaces, but they often suffer from high randomness, low efficiency, slow convergence, and suboptimal path quality in cluttered environments. To address these limitations, this paper proposes PFMS-RRT*, a progress-aware fused-sampling RRT* with a multi-level strategy extension. The method builds on a bidirectional RRT* framework and introduces three main components: (i) a progress-aware fused sampling scheme that adapts an oriented elliptical sampling region based on inter-tree progress and stagnation, mixes locally guided elliptical samples with globally explorative Halton-sequence samples, and dynamically balances exploration and exploitation; (ii) a three-level goal-guided extension mechanism that escalates from direct steering to local probing and then multi-direction detours to maintain forward progress when obstacles block expansion; and (iii) a smooth tangential artificial potential field (APF) extension used as a fallback, with a failure-driven probabilistic switching rule that increases APF usage after repeated extension failures. Simulations in four representative 2D environments (sparse, corridor-like dense, random dense, and narrow passage) show that PFMS-RRT* consistently yields shorter paths, lower and more stable runtime, and fewer nodes than several RRT* variants while maintaining competitive or improved obstacle clearance.
Li et al. (2026) studied this question.