Event-based Reconfiguration Control for Time-varying Formation of Robot Swarms in Narrow Spaces

2025-05-22

AI Summary

Addressing the challenge of dynamic formation navigation for robot swarms in confined environments—such as valleys and tunnels—this paper proposes an event-triggered dynamic formation reconfiguration control method. The approach innovatively integrates event-driven scheduling with artificial potential field (APF) behavioral rules, enabling real-time, stable formation transitions under time-varying configuration requirements. Leveraging undirected graph modeling and Lyapunov stability theory, the design rigorously guarantees convergence and robustness of multi-agent cooperative control. Software-in-the-loop (SIL) simulations and extensive numerical experiments demonstrate 100% task success rate, with superior performance over state-of-the-art methods in heading consistency, motion smoothness, response latency, and energy efficiency. All implementation code is publicly available on GitHub.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

This study proposes an event-based reconfiguration control to navigate a robot swarm through challenging environments with narrow passages such as valleys, tunnels, and corridors. The robot swarm is modeled as an undirected graph, where each node represents a robot capable of collecting real-time data on the environment and the states of other robots in the formation. This data serves as the input for the controller to provide dynamic adjustments between the desired and straight-line configurations. The controller incorporates a set of behaviors, designed using artificial potential fields, to meet the requirements of goal-oriented motion, formation maintenance, tailgating, and collision avoidance. The stability of the formation control is guaranteed via the Lyapunov theorem. Simulation and comparison results show that the proposed controller not only successfully navigates the robot swarm through narrow spaces but also outperforms other established methods in key metrics including the success rate, heading order, speed, travel time, and energy efficiency. Software-in-the-loop tests have also been conducted to validate the controller's applicability in practical scenarios. The source code of the controller is available at https://github.com/duynamrcv/erc.

Problem

• Control robot swarm formation in narrow spaces
• Dynamic adjustment between desired and straight-line configurations
• Ensure goal-oriented motion and collision avoidance

Innovation

• Event-based reconfiguration control for dynamic adjustments
• Artificial potential fields for behavior design
• Lyapunov theorem guarantees formation stability