Evolution of Transferable and Self-Organized Communication Modules for Solving Multiple Swarm Robotics Tasks.

Area of Science:

• Robotics
• Artificial Intelligence
• Swarm Intelligence

Background:

• Decentralized multirobot coordination heavily relies on communication.
• Existing communication frameworks are often task-specific, requiring redesign for new applications.
• Limited success has been reported in evolving complex communication strategies for robots.

Purpose of the Study:

• To propose a novel, task-agnostic communication module for decentralized multirobot systems.
• To develop a standardized communication framework applicable across various robotic tasks.
• To enable self-organized coordination in robot swarms without task-specific modifications.

Main Methods:

• A virtual communication space is introduced where each robot has a coordinate (state).
• This space is partitioned into regions, each linked to specific physical behaviors (e.g., resource seeking, recharging).
• Robots broadcast their coordinates to neighbors, enabling self-organized behavior based on their region in the communication space.

Main Results:

• The same evolved communication module demonstrated effectiveness in three distinct swarm robotics tasks: aggregation, geometry formation, and foraging.
• The module provided scalable and robust performance across all tested tasks.
• The system facilitated self-organized coordination through navigation within the virtual communication space.

Conclusions:

• The proposed communication module is task-agnostic, reducing the need for system redesign for new applications.
• This represents a significant advancement towards standardized communication frameworks for robot swarms.
• The approach enables effective and scalable self-organized coordination in decentralized multirobot systems.