Robust replanning capabilities are essential for autonomous robots to navigate dynamic and complex environments. This paper aims to propose the sampling-guided dual-tree (SGDT*), designed to simultaneously improve replanning efficiency and promote the generation of satisfactory paths.
SGDT* is composed of configuration space exploration and adaptive replanning. The configuration space is comprehensively and uniformly covered by the configuration space exploration module through the expansion of a main search tree, providing an initial feasible path and all required nodes. Environmental changes are addressed by the adaptive replanning module, where a sampling set is strategically extracted from existing nodes to grow the sub-search tree and rapidly identify new feasible paths. Additionally, feasible paths are used to further contract the sampling set, increasing the probability of selecting valuable nodes and facilitating efficient, high-quality replanning.
The properties of SGDT*, including probabilistic completeness, shortest path and runtime are theoretically analysed. The effectiveness and real-time performance of SGDT* are rigorously validated through comparative simulations and real-world mobile robot experiments in various challenging scenarios.
SGDT* supports stable, real-time updates of collision-free paths in dynamic environments, achieving both efficient replanning and high-quality path generation. SGDT* can be seamlessly integrated into robotic navigation architectures to enhance the reliability and adaptability of autonomous systems that require frequent replanning in complex environments.
