REACT: Environment-Adaptive Architecture for Continuous Formation Navigation of Wheeled Mobile Robots

2026-05-18

AI Summary

This work addresses the limitations of existing wheeled multi-robot formation strategies, which typically rely on fixed configurations and struggle in complex dynamic environments. The authors propose REACT, a hierarchical framework comprising two layers: an upper layer that employs the TCF-R2T algorithm to generate environment-adaptive formations in real time and achieves conflict-free target assignment in polynomial time, and a lower layer that utilizes Joint Spatio-Temporal Planning (JSTP) to jointly optimize trajectories, balancing formation maintenance with continuous navigation. This approach is the first to enable environment-driven, real-time formation reconfiguration coupled with conflict-free cooperative planning, integrating centralized formation generation with distributed maintenance. Extensive simulations and physical experiments demonstrate its effectiveness and practicality, showing significant improvements in collaborative efficiency for multi-robot systems operating in obstacle-dense and highly dynamic scenarios.

Abstract

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.

Problem

• formation control
• wheeled mobile robots
• environment adaptation
• continuous navigation
• trajectory conflict

Innovation

• environment-adaptive formation
• trajectory-conflict-free assignment
• joint spatio-temporal planning
• hierarchical architecture
• continuous formation navigation