AFI-RRT$$^*$$: Adaptive Free Interval Guided RRT$$^*$$ for Path Planning in Cluttered Environments
Year: 2026
Type: article
Abstract: Path planning for mobile robots and unmanned aerial vehicles in complex environments remains challenging, particularly when efficient exploration, short path generation, and low computational cost are required. RRT \(^{*}\) and its variants can provide high-quality solutions, but they often suffer from excessive exploration and large search trees, especially in geometrically constrained environments. To address these limitations, this study proposes AFI-RRT \(^{*}\) , an Adaptive Free Interval guided RRT \(^{*}\) framework that combines coarse A \(^{*}\) -based global guidance with structured local free-space exploration. Its main advantages include focused sampling through adaptive free-interval layers, reduced unnecessary tree expansion, preservation of global exploration, and direct extraction of the final solution from the RRT \(^{*}\) tree without additional geometric post-processing. The proposed method was evaluated in nine environments over 30 independent runs and compared with Informed RRT \(^{*}\) , A \(^{*}\) , AdaRRT, RRT \(^{*}\) , RRT, RRT-Connect, and QS-RRT \(^{*}\) . AFI-RRT \(^{*}\) achieved the lowest overall mean path length while reducing average execution time by 13.8% and average search-tree size by 52.5% relative to RRT \(^{*}\) . Compared with Informed RRT \(^{*}\) , the corresponding reductions were 35.7% and 53.0%, respectively. The method also maintained a 100% success rate. Wilcoxon signed-rank tests showed statistically significant path-length differences against RRT \(^{*}\) in all nine environments and against Informed RRT \(^{*}\) in seven environments. Overall, AFI-RRT \(^{*}\) provides a favorable balance between path quality and computational efficiency by focusing the search on geometrically relevant free-space regions.
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,490
OpenAlex ID: W7225465815