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

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.

Installation

git clone git@github.com:duynamrcv/erc.git
pip install imageio numpy matplotlib

Demo

We implement two controllers, includes the proposed controller ERC and the pure behavior controller BC, which are presented in robot_erc.py and robot_bc.py, respectively. To choose the tested controller, edit parameter CONTROLLER in config.py. To run the demo, run:

cd erc
python3 main.py

The data will be saved in *.txt. For the visualization, run:

python3 visualization.py

Results

Method	Pentagon shape	V-shape
ERC
BC

Citation: Duy-Nam Bui, Manh Duong Phung & Hung Pham Duy. "Event-based Reconfiguration Control for Time-varying Formation of Robot Swarms in Narrow Spaces," in Intelligent Service Robotics. 2025.