SGDT*: Sampling-guided dual-tree for mobile robot rapid replanning in dynamic environments

Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.