This study aims to introduce the fast reactive tree (FRT*) algorithm for enhancing replanning speed and reducing the overall cost of navigation in unknown dynamic environments.
FRT* comprises four key components: inverted tree build, convex hull construction, dead nodes inform activation and lazy-rewiring replanning. First, an initial path is found from the inverted tree where the valid structure is preserved to minimise re-exploration areas during the replanning phase. As the robot encounters environment changes, convex hulls are extracted to sparsely describe impacted areas. Next, the growth direction of the modified tree is biased by the inform activation of dead nodes to avoid unnecessary exploration. In the replanning phase, the tree structure is optimized using the proposed lazy-rewiring replanning to find a high-quality path with low computation burden.
A series of comprehensive simulation experiments demonstrate that the proposed FRT* algorithm can efficiently replan short-cost feasible paths in unknown dynamic environments. The differential wheeled mobile robot with varying reference linear velocities is used to validate the effectiveness and adaptability of the proposed strategy in real word scenarios. Furthermore, ablation studies are conducted to analyze the significance of the key components of FRT*.
The proposed FRT* algorithm introduces a novel approach to addressing the challenges of navigation in unknown dynamic environments. This capability allows mobile robots to safely and efficiently navigate through unknown and dynamic environments, making the method highly applicable to real-world scenarios.
