轮式移动机器人（WMR）的编队控制因其在物流运输、环境监测及搜救等领域的广泛应用而受到广泛研究。然而，现有大多数工作主要集中于跟踪预定义编队，限制了其在复杂真实环境中的适应能力。为此，我们提出REACT（面向连续编队导航的实时环境自适应架构），一种融合集中式编队生成与分布式编队维持的分层架构。具体而言，其上层在必要时生成新的环境自适应编队，并采用所提出的TCF-R2T（无轨迹冲突的机器人到目标分配）算法，在多项式时间内计算无冲突的WMR到目标分配，从而实现及时且无轨迹冲突的编队切换；下层则由各WMR独立执行所开发的JSTP（联合时空轨迹规划）方法，在维持生成编队的同时同步优化空间位置与时间持续期，从而提升WMR间的协同性，并支持在障碍物密集环境及动态障碍场景下的连续导航。仿真与真实实验均验证了REACT的有效性与实际适用性。实验视频见项目网站：https://dongjh20.github.io/REACT-website。

Formation control of wheeled mobile robots (WMRs) has been extensively studied due to its broad applications in fields such as logistics transportation, environmental monitoring, and search and rescue. However, most existing works mainly focus on tracking predefined formations, which limits their adaptability to complex real-world environments. To address this, we propose REACT (Real-time Environment-Adaptive architecture for Continuous formation navigaTion), a hierarchical architecture integrating centralized formation generation and distributed formation maintenance. Specifically, our upper layer generates new environment-adaptive formations when necessary and uses our proposed TCF-R2T (Trajectory-Conflict-Free Robot-to-Target assignment) algorithm to compute conflict-free WMR-to-target assignments in polynomial time, enabling timely formation transitions without trajectory conflicts. At the lower layer, each WMR executes our developed JSTP (Joint Spatio-Temporal trajectory Planning) method to maintain the generated formation by simultaneously optimizing spatial positions and temporal durations, thereby enhancing coordination among WMRs and enabling continuous navigation in obstacle-rich environments and dynamic-obstacle scenarios. Both simulation and real-world experiments validate the effectiveness and practical applicability of REACT. Experimental videos are available on our project website: https://dongjh20.github.io/REACT-website.