提出可实时适应环境的轮式机器人编队导航架构，实现无冲突连续移动。
REACT: Environment-Adaptive Architecture for Continuous Formation Navigation of Wheeled Mobile Robots
- 分层架构：上层生成自适应编队，下层协同规划时空轨迹
- 多项式时间求解无冲突机器人到目标分配，支持快速编队切换
- 实测验证在复杂动态环境中稳定编队与连续导航能力
轮式移动机器人（WMRs）的编队控制因在物流运输、环境监测和搜救等领域的广泛应用而受到广泛关注。然而，现有方法多聚焦于追踪预设编队，难以适应复杂真实环境。为此，本文提出REACT（实时环境自适应连续编队导航架构），采用分层设计，集成集中式编队生成与分布式编队维持。上层在必要时生成环境自适应编队，并通过提出的TCF-R2T（轨迹无冲突机器人到目标分配）算法，在多项式时间内计算无冲突的WMR-to-target分配，实现无轨迹冲突的及时编队转换。下层每个WMR执行自主研发的JSTP（联合时空轨迹规划）方法，同时优化空间位置与时间时长，增强机器人间协调性，支持在障碍物密集及动态障碍场景下的持续导航。仿真与真实实验均验证了REACT的有效性与实用性。实验视频见项目网站：https://dongjh20.github.io/REACT-website。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。